The analytics team has been asked to determine if the organization should launch their highest revenue generating product into the North American market. To date, this has only been available in Eastern Europe. To answer this, the team formulates several research questions, including:
- A . What product launch related costs can we expect?
- B . How much revenue does the product generate in Eastern Europe?
- C . Why does management need to know this?
- D . Do existing customers really like the product?
D
Explanation:
One of the steps in identifying the research questions for business data analytics is to assess the feasibility and desirability of the proposed solution or change1. This involves understanding the needs, preferences, and satisfaction of the existing and potential customers. Therefore, asking whether the existing customers really like the product is a relevant research question for the analytics team.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 22.
An analyst has just completed building a data model that shows the table structures including table names, table relationships with primary and foreign keys and column names with respective data types.
What type of data model has the analyst just built?
- A . Physical
- B . Hierarchical
- C . Conceptual
- D . Logical
A
Explanation:
A physical data model is the most detailed and specific type of data model, which shows how the data is stored, accessed, and manipulated in the database. It includes the table structures, column names, data types, primary and foreign keys, constraints, indexes, and other physical attributes of the data12.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 542: Data Modeling Essentials, Graeme Simsion and Graham Witt, 2005, p. 15.
The analytics team is identifying research questions to address a business problem. The business analysis professional reminds the team that the most important dimension to consider is the:
- A . Sources of data
- B . Quality of the data
- C . Timeframe of analysis
- D . Measurement scale
B
Explanation:
The quality of the data is the most important dimension to consider when identifying research questions, as it affects the validity, reliability, and accuracy of the analysis and the results. Data quality refers to the degree to which the data meets the requirements and expectations of the stakeholders and the purpose of the analysis12. Poor data quality can lead to erroneous conclusions, ineffective decisions, and wasted resources3.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 282: Data Quality Assessment, Arkady Maydanchik, 2007, p. 33: Data Quality: The Field Guide, Thomas C. Redman, 2001, p. 1.
An analyst at a supermarket chain has been asked to extract data from multiple data sources to complete a study on customer spending habits. The analyst is going to query data from various databases.
Which statement is true about database querying?
- A . Querying can be used to create predictive data models
- B . Irrespective of the querying language used, data results retrieved are always in a tabular format
- C . A querying language is independent of the type of database being used
- D . Querying is a structured way of searching, manipulating and managing data
D
Explanation:
Querying is a technique that allows analysts to access, filter, join, aggregate, and transform data from various databases using a specific syntax and logic1. Querying can be used for different purposes, such as data exploration, data preparation, data analysis, and data visualization2. Querying is not limited to creating predictive data models, nor does it always produce tabular results. Moreover, querying languages may vary depending on the type and structure of the database, such as relational, hierarchical, or document-based3.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 552: Data Analysis Using SQL and Excel, Gordon S. Linoff, 2016, p. 33: Database Systems: Design, Implementation, and Management, Carlos Coronel and Steven Morris, 2019, p. 17.
A lab is conducting a study on protein interactions. They have used the data to create a graph visualization.
In graph visualization, what would a layout be?
- A . A single data point
- B . A link between two data points
- C . A dedicated algorithm that calculates the node positions
- D . A collection of data points and links
C
Explanation:
A layout is a way of arranging the nodes and links of a graph visualization to convey meaningful information about the data. A layout is determined by a dedicated algorithm that calculates the node positions based on certain criteria, such as minimizing edge crossings, maximizing node spacing, or emphasizing clusters12. A layout can also be influenced by user interaction, such as zooming, panning, or dragging3.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 642: Graph Drawing: Algorithms for the Visualization of Graphs, Giuseppe Di Battista et al., 1999, p. 33:
Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward et al., 2015, p. 227.
An analyst at a bank is trying to identify research questions for an analytical study on top customer issues across branches.
During an interview with a branch manager, the analyst asks the manager what their top customer concerns are relating to this branch?
After the manager’s reply, the analyst asks a follow up question on how their top customer concerns compare against the top customer concerns across all branches? Was the analyst’s follow-up question valid?
- A . No, there is no value comparing the results of a single branch with results across all branches
- B . Yes, it builds on the previous question and allows the analyst to identify branch-specific concerns
- C . No, the question is not valid in this particular scenario
- D . Yes, only for the purpose of ensuring that the manager is aware of the company-wide reports
B
Explanation:
The analyst’s follow-up question is valid because it helps to refine the scope and context of the research questions for the analytical study. By comparing the top customer concerns across branches, the analyst can identify the common and unique issues that affect customer satisfaction and loyalty. This can also help to prioritize the most critical or urgent problems that need to be addressed by the bank12.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 212: Business Analysis for Practitioners: A Practice Guide, PMI, 2015, p. 43.
Interested in experimenting with analytics, a manufacturing company hires an analyst to see how the capability can be developed within its organization. The analyst is getting started and recognizes the need to show value from the onset of their work to gain upper management’s trust and future funding.
What action will accomplish these objectives?
- A . Solve the biggest problem the organization has first to quickly grab the support and attention of senior management
- B . Develop a question that can be answered quickly regardless of alignment to strategy, just
to get started - C . Develop a meaningful question that can be answered with data the company already has in its possession
- D . Perform a market analysis to understand how competitors are using analytics and then launch a similar initiative
C
Explanation:
The best action for the analyst to show value from the onset of their work is to develop a meaningful question that can be answered with data the company already has in its possession. This way, the analyst can demonstrate the potential of analytics to solve relevant business problems, without spending too much time or resources on data collection or market research. The question should also be aligned with the organization’s strategy and goals, and provide actionable insights for decision making12.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 202: Data Science for Business, Foster Provost and Tom Fawcett, 2013, p. 14.
A large car manufacturer is interested in comparing the number of sales for a specific model of electric car across all 50 US states.
The data analytics team sourced and acquired the data, and the business analyst created the model to compare sales across states.
In a meeting to review the results, the feedback received included several complaints concerning an inability to distinguish the number of sales per state.
What model would result in such confusion?
- A . Bullet chart
- B . Dual axis chart
- C . Bar chart
- D . Pie chart
D
Explanation:
A pie chart is a circular chart that shows the proportion of each category in a whole by dividing the circle into slices. A pie chart would result in confusion when comparing the number of sales for a specific model of electric car across all 50 US states, because it is difficult to compare the angles and areas of the slices, especially when there are many categories with similar values. A pie chart also does not show the absolute values of each category, unless they are labeled or annotated12. A better alternative would be a bar chart, which can show the number of sales for each state along a common axis, making it easier to compare and rank the values3.
Reference: 1: Guide to Business Data
Analytics, IIBA, 2020, p. 652: Storytelling with Data, Cole Nussbaumer Knaflic, 2015, p. 673: The Visual Display of Quantitative Information, Edward R. Tufte, 2001, p. 178.
The definition of data elements is different across various data sources. The organization is looking to improve the usability of data across the organization.
Which practice would help address this problem?
- A . Data governance
- B . Data quality
- C . Data architecture
- D . Data ethics
A
Explanation:
Data governance is the practice of establishing and enforcing policies, standards, roles, and responsibilities for the management and use of data across the organization. Data governance helps to address the problem of inconsistent data definitions across various data sources by ensuring that data is properly defined, documented, classified, and aligned with the business objectives and requirements12.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 292: Data Governance: How to Design, Deploy and Sustain an Effective Data Governance Program, John Ladley, 2012, p. 3.
Insights based on the data collected indicate that a multi-national company could increase its sales of a mature product by reducing its price by 20% which would result in increased revenues of 2% over a 6-month period. The team recommends this as an appropriate goal for its organization. This is considered a good goal because:
- A . It meets all the criteria for a well-defined objective
- B . The organization can derive additional revenue from the product
- C . It indicates that the company does not have to incur costs associated with retiring this product
- D . Management will be pleased that the mature product can still contribute to revenue
A
Explanation:
A well-defined objective is one that is specific, measurable, achievable, relevant, and time-bound (SMART)1. The goal of increasing sales of a mature product by reducing its price by 20% which would result in increased revenues of 2% over a 6-month period meets all these criteria, as it clearly states what the desired outcome is, how it will be measured, whether it is realistic and attainable, how it aligns with the organization’s strategy, and when it will be achieved2.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 192: SMART Goals: How to Make Your Goals Achievable, MindTools, 2021, 1.
The marketing department for a major restaurant chain is interested in testing a Kids Eat Free campaign to determine if it will help to increase sales. They are interested in piloting the campaign to determine which day of the week will improve sales the most.
The campaign is launched across 7 cities with each city promoting a different day of the week. The sales data is collected and provided to a team for analysis.
What concern might the analytics team have regarding data quality across cities?
- A . Normality
- B . Heteroskedacity
- C . Linearity
- D . Variation
D
Explanation:
Variation is the degree to which the data values differ from each other or from a central tendency measure, such as the mean or median. Variation can affect the data quality across cities, as it can indicate the presence of outliers, errors, noise, or inconsistency in the data collection or processing methods. Variation can also influence the statistical analysis and interpretation of the results, as it can affect the significance, confidence, and validity of the findings12.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 302: Statistics for Business and Economics, David R. Anderson et al., 2014, p. 83.
A call center has requested to review their sales conversion data for the month. The analyst working on this request is trying to identify the chart that will effectively present the data, which includes: the number of leads, the number of calls made, the number of calls completed, the number of customers interested and the number of sales.
What chart should the analyst use to show the values across each stage of the pipeline?
- A . Pie chart
- B . Funnel chart
- C . Bar chart
- D . Bullet chart
B
Explanation:
A funnel chart is a type of chart that shows the values of different stages of a process, such as a sales pipeline, where each stage represents a subset of the previous one. A funnel chart is useful for showing the conversion rate, the drop-off rate, and the potential revenue or profit at each stage12. A funnel chart would be an effective way to present the data requested by the call center, as it would show the number of leads, calls, customers, and sales, as well as the percentage of change between each stage.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 662: Data Visualization: A Practical Introduction, Kieran Healy, 2018, p. 233.
A government agency is conducting a study on the performance of 12th grade students’ in mathematics across the country. In particular, they want to understand if there is a relationship between intelligence and scores, as well as the difference in performance between various locations.
Which combination of inferential statistics procedures should be used?
- A . Range, standard deviation
- B . Mean, median
- C . Correlation co-efficient, analysis of variance
- D . Frequency distribution, time-series
C
Explanation:
A correlation co-efficient is a measure of the strength and direction of the linear relationship between two variables, such as intelligence and scores. A correlation co-efficient can range from -1 to 1, where -1 indicates a perfect negative relationship, 0 indicates no relationship, and 1 indicates a perfect positive relationship12. An analysis of variance (ANOVA) is a procedure that tests whether the means of two or more groups are significantly different from each other, such as the performance of students across various locations. ANOVA can compare the variation within each group and the variation between groups to determine if there is a statistically significant difference among the group means34.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 582:
Statistics for Business and Economics, David R. Anderson et al., 2014, p. 7133: Guide to Business Data
Analytics, IIBA, 2020, p. 594: Statistics for Business and Economics, David R. Anderson et al., 2014, p.
A government agency is conducting a study on the performance of 12th grade students’ in mathematics across the country. In particular, they want to understand if there is a relationship between intelligence and scores, as well as the difference in performance between various locations.
Which combination of inferential statistics procedures should be used?
- A . Range, standard deviation
- B . Mean, median
- C . Correlation co-efficient, analysis of variance
- D . Frequency distribution, time-series
C
Explanation:
A correlation co-efficient is a measure of the strength and direction of the linear relationship between two variables, such as intelligence and scores. A correlation co-efficient can range from -1 to 1, where -1 indicates a perfect negative relationship, 0 indicates no relationship, and 1 indicates a perfect positive relationship12. An analysis of variance (ANOVA) is a procedure that tests whether the means of two or more groups are significantly different from each other, such as the performance of students across various locations. ANOVA can compare the variation within each group and the variation between groups to determine if there is a statistically significant difference among the group means34.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 582:
Statistics for Business and Economics, David R. Anderson et al., 2014, p. 7133: Guide to Business Data
Analytics, IIBA, 2020, p. 594: Statistics for Business and Economics, David R. Anderson et al., 2014, p.
An organization’s customers are categorized based on the amount of purchases completed over the last 12 months. The analytics team would like to ensure the accuracy of their survey results and decide to randomly select 500 customers to participate in a survey from this large pool of customers.
This is an example of:
- A . Stratified sampling
- B . Quota sampling
- C . Purposive sampling
- D . Snowball sampling
A
Explanation:
Stratified sampling is a technique that divides the population into homogeneous subgroups (strata) based on a relevant characteristic, such as the amount of purchases, and then randomly selects a proportional number of elements from each subgroup to form the sample. Stratified sampling ensures that the sample is representative of the population and reduces the sampling error and bias12.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 312: Statistics for Business and Economics, David R. Anderson et al., 2014, p. 262.
The results of the data analytics work led to some clear and strongly supported outcomes and the analytics team is very confident in their recommendations; particularly given that the payback on the required changes are a short 3 months. However, there is concern because the organization operates in a highly regulated environment and some new regulatory changes are being considered with announcements and implementation in the next 6 months.
Under these conditions the team decides to:
- A . Recommend no action be taken at this time and revisit in 6 months
- B . Reassess their results to ensure their validity and then decide what to do
- C . Identify and carefully document assumptions for their recommendation
- D . Postpone recommendations for 6 months until the announcements are made
C
Explanation:
The best option for the team under these conditions is to identify and carefully document the assumptions for their recommendation, such as the expected impact of the regulatory changes, the risks and benefits of implementing the changes before or after the announcements, and the sensitivity of the results to different scenarios. This way, the team can communicate their findings and recommendations clearly and transparently, while also acknowledging the uncertainty and limitations of their analysis. This can help the decision makers to evaluate the trade-offs and make informed choices12.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 242: Data-Driven Decision Making: A Primer for Beginners, Anand Rao, 2018, 1.
A colleague proposes measuring job satisfaction by asking the question "What is your salary?".
What is the concerning factor about this question?
- A . Validity
- B . Clarity
- C . Reproducibility
- D . Subjectivity
A
Explanation:
Validity is the extent to which a measure or a question accurately captures the intended concept or construct1. The question “What is your salary?” is not a valid measure of job satisfaction, as it does not reflect the various aspects of job satisfaction, such as work environment, recognition, autonomy, growth, etc. Salary is only one possible factor that may influence job satisfaction, but it is not a direct or comprehensive indicator of it23. Therefore, the question is not valid for measuring job satisfaction.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 302: Job Satisfaction: Application, Assessment, Causes, and Consequences, Paul E. Spector, 1997, p. 23: Job Satisfaction Survey, 1.
A marketing director has asked the question ‘How many product purchases are expected this coming year given the current marketing campaign?
What type of analytics would be performed to answer this question?
- A . Descriptive
- B . Predictive
- C . Diagnostic
- D . Prescriptive
B
Explanation:
Predictive analytics is a type of analytics that uses historical and current data, as well as statistical and machine learning techniques, to forecast future events or outcomes, such as product purchases, customer behavior, or market trends12. To answer the question ‘How many product purchases are expected this coming year given the current marketing campaign?’, predictive analytics would be performed to estimate the demand and sales based on the existing data and the marketing campaign variables.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 182: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, Eric Siegel, 2016, p. 3.
An insurance company has seen an upward trend in winter-related accidents over the past three years. The company has just completed an analytics study to better understand the primary reasons for these accidents and assess how many of the drivers were using winter tires. This analysis will help the company decide how to move forward with drivers not taking precautionary measures during winter.
What type of analysis will help in determining the primary reasons and percentage of those drivers with winter tires?
- A . Prescriptive
- B . Descriptive and Predictive
- C . Descriptive
- D . Descriptive and Diagnostic
D
Explanation:
Descriptive analytics is a type of analytics that summarizes and visualizes the data to provide an overview of what has happened or is happening, such as the trend of winter-related accidents over the past three years, or the percentage of drivers using winter tires12. Diagnostic analytics is a type of analytics that explores and analyzes the data to understand why something has happened or is happening, such as the primary reasons for these accidents, or the factors that influence the drivers’ decisions13. To answer the question, both descriptive and diagnostic analytics would be needed to provide the relevant information and insights for the company.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 182: Business Analytics: Data Analysis & Decision Making, S. Christian
Albright and Wayne L. Winston, 2015, p. 53: Data Science for Business, Foster Provost and Tom Fawcett, 2013, p. 13.
A Human Resource manager recently learned that their competitor reduced employee attrition rates by 20% after implementing personality tests as part of their screening process. Intrigued by the idea, the manager suggests collecting data on personality tests and attrition rates over the next year. The data from this year is then analyzed to explore possible relationships.
What type of analytics has the team been asked to perform?
- A . Predictive
- B . Descriptive
- C . Prescriptive
- D . Diagnostic
B
Explanation:
Descriptive analytics is a type of analytics that summarizes and visualizes the data to provide an overview of what has happened or is happening, such as the attrition rates and the personality test scores of the employees12. The team has been asked to perform descriptive analytics to explore possible relationships between the data variables, without making any predictions or prescriptions for the future.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 182: Business Analytics: Data Analysis & Decision Making, S. Christian Albright and Wayne L. Winston, 2015, p. 5.
A large telecommunications company wants to increase their Average Revenue Per User per month by 5%, by end of year, to increase revenue in a highly competitive market.
From a SMART target perspective, what is missing?
- A . T – The increase should be seen sooner
- B . A – It is too easy of a target to attain
- C . R – Since competition is high, focus should be on increasing customer base and not on ARPU
- D . S – There is no mention of which product group/line the target pertains to
D
Explanation:
A SMART target is one that is specific, measurable, achievable, relevant, and time-bound1. The target of increasing the Average Revenue Per User (ARPU) per month by 5%, by end of year, to increase revenue in a highly competitive market is missing the specificity criterion, as it does not mention which product group or line the target applies to. The target should be more specific and clear about the scope and context of the desired outcome, such as which segment, region, or service the target relates to23.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 192: SMART Goals: How to Make Your Goals Achievable, MindTools, 2021, 13: How to Set SMART Marketing Goals, CoSchedule, 2021, 2.
An analytics team has completed some initial data analysis but is considering revising their research question based on their analysis findings. The team was concerned the original question was too broad.
What outcome would lead the team to have this concern?
- A . Data once analyzed had significant data quality issues
- B . Data the team had planned to use was not available
- C . Difficult to identify the KPIs to measure
- D . The source data sets could not be merged
C
Explanation:
A research question is a clear and focused question that guides the data analytics process and defines the expected outcome or value of the analysis1. A research question that is too broad may lead to the concern of being difficult to identify the key performance indicators (KPIs) to measure, as KPIs are specific, quantifiable, and relevant metrics that indicate the progress and success of the analysis in relation to the research question23. A broad research question may also result in too much or too little data, unclear or conflicting objectives, or irrelevant or ambiguous results4.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 202: Guide to Business Data Analytics, IIBA, 2020, p. 233: Key Performance Indicators: Developing, Implementing, and Using Winning KPIs, David Parmenter, 2015, p. 34: How to Write a Good Research Question, ThoughtCo, 2021, 1.
A manufacturing company, specializing in turf maintenance equipment, has recently seen a decline in their lawn mower sales. As a result, the analytics team is asked to review the latest customer satisfaction survey results. An analyst on this team creates a report for senior management with attractive visuals, supported by the KPI results. Upon reviewing the report, the analyst’s manager mentions that the report is missing the narrative.
What does this mean?
- A . The data tables that support the visuals and help answer questions
- B . A narrative that supports insights with additional context and draws correlations
- C . Notes on assumptions and unavailable data for analysis
- D . Commentary around why each graphic was selected to provide additional context
B
Explanation:
A narrative is a written or spoken explanation of the data analysis results that tells a story with the data, provides additional context and background information, highlights the key insights and findings, and draws correlations and implications for the decision makers12. The report is missing the narrative, meaning that it does not communicate the meaning and value of the data analysis effectively, and it leaves the interpretation and action to the senior management without any guidance or recommendation34.
Reference: 1: Guide to Business Data Analytics, IIBA, 2020, p. 672: Storytelling with Data, Cole Nussbaumer Knaflic, 2015, p. 93: Data Storytelling: The Essential Data Science Skill Everyone Needs, Brent Dykes, 2016, 14: The Power of Data Storytelling, Harvard Business Review, 2018, 2.
The analytics team scheduled a meeting with key stakeholders to present their recommendations. The team envisioned this as the final step of their work and fully expected complete acceptance of those recommendations, particularly given that very few questions were asked. They were surprised when they received word that the organization wasn’t ready to move forward.
What did they overlook?
- A . Stakeholders need to hear the same information multiple times
- B . Stakeholders never make quick decisions
- C . Communicating information requires a written report
- D . Communicating information is bi-directional and iterative
D
Explanation:
The analytics team overlooked the fact that communicating information is not a one-way or one-time process, but rather a bi-directional and iterative one. This means that the team should not only present their recommendations, but also solicit feedback, address concerns, clarify doubts, and confirm understanding from the stakeholders. By doing so, the team can ensure that the stakeholders are fully engaged, informed, and aligned with the recommendations, and that any potential barriers or risks are identified and mitigated before moving forward.
Reference:
• Understanding the Guide to Business Data Analytics, page 9
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 4: Interpret and Report Results
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 5, Step 3 C Schedule and Take The Exam
While creating a dataset for analysis, the analyst reviews the data collected and finds a large percentage of records are missing values.
Which activity would the analyst perform in order to use this dataset?
- A . Clustering
- B . Scale validation
- C . Weighting
- D . Factor analysis
C
Explanation:
Weighting is a technique that assigns different values or weights to different records or variables in a dataset, based on their importance or relevance. Weighting can be used to handle missing values by giving them a lower weight or imputing them with a weighted average of other values. Weighting can also help to adjust for sampling bias or non-response bias in the data collection process.
Reference:
• Understanding the Guide to Business Data Analytics, page 16
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 4
The analytics team has completed analyzing a dataset and unfortunately the data didn’t deliver the kinds of insights that the team was hoping for.
After much contemplation, they decide to:
- A . Summarize the results and indicate the outcome was inconclusive
- B . Inform management that analytics could not derive insightful results
- C . Wait a few weeks and rerun the analysis using refreshed data
- D . Restart the work with formation of a new research question
D
Explanation:
The analytics team should restart the work with formation of a new research question, because the existing one may not be well-defined, relevant, or feasible. A well-formed research question is the first step of the business data analytics cycle, and it guides the subsequent steps of sourcing, analyzing, interpreting, and reporting data. If the data analysis does not yield meaningful insights, the team should revisit the research question and refine it based on the business problem, stakeholder needs, data availability, and analytical methods.
Reference:
• Understanding the Guide to Business Data Analytics, page 10-11
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 1: Identify the Research Questions
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 5
An analyst is using a Data Flow Diagram (DFD) to depict the flow of data across a data security company.
Which of the following is true about DFDs?
- A . Can be categorized as Logical or Physical
- B . Can illustrate a sequence of activities
- C . Provide similar information as process flows
- D . Are used to model data attributes
A
Explanation:
A Data Flow Diagram (DFD) is a technique that shows the flow of data among processes, data stores, and external entities in a system. DFDs can be categorized as logical or physical, depending on the level of detail and abstraction. A logical DFD focuses on the business functions and data flows, without specifying the implementation details. A physical DFD shows the actual components and mechanisms that are involved in the data flow, such as hardware, software, files, and network connections.
Reference:
• 10.13 Data Flow Diagrams | IIBA® – International Institute of Business …, menu, 10.13 Data Flow Diagrams
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 2: Source Data
• Introduction to Business Data Analytics: Organizational View, page 16, Figure 6: Data Flow Diagram
A consumer goods manufacturer has recently completed an analytics study to understand how to improve its operational excellence. From the top highlights, online sales outperformed other channels in sales growth and there was a direct relationship between positive customer reviews and increased internet sales.
Which strategic business decision may be logically derived from these results?
- A . Improve quality of the products
- B . Create an empowered and collaborative work culture
- C . Encourage customers to complete online reviews
- D . Improve operational efficiencies
C
Explanation:
The strategic business decision that may be logically derived from the results is to encourage customers to complete online reviews, because the results show that there is a direct relationship between positive customer reviews and increased internet sales. By increasing the number and quality of online reviews, the consumer goods manufacturer can boost its online sales performance, which outperformed other channels in sales growth. Online reviews can also help the manufacturer gain customer feedback, improve customer loyalty, and enhance its brand reputation.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 5: Use Results to Influence Business Decision Making
• Understanding the Guide to Business Data Analytics, page 9
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 6
A real estate broker is tracking monthly sales between two of its teams. The results have been visualized.
What insights can be drawn from the chart?
- A . Q2 was the strongest performing quarter with Team B having the top monthly sales in May
- B . Q3 was the strongest performing quarter with Team A having the top monthly sales in the quarter
- C . Q4 was the lowest performing quarter with November having the lowest monthly sales in the year
- D . Q4 was the lowest performing quarter with Team A having the lowest monthly sales in the Quarter
C
Explanation:
The chart visualizes monthly sales data for two teams over a year, divided into quarters. By analyzing the data, it is evident that November (part of Q4) had the lowest monthly sales in the year, making option C correct. There isn’t enough information to verify the performance of individual teams in each quarter as per Business Data Analytics (IIBA®- CBDA) objectives and resources.
Reference:
• [Business Analysis Certification in Data Analytics, CBDA | IIBA®], CBDA Competencies, Domain 4: Interpret and Report Results
• [Understanding the Guide to Business Data Analytics], page 9
• [CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®], page 8, CBDA Exam Sample Questions and Self-Assessment, Question 7
The analytics team has been asked to assess sales data from their company’s website with the hopes of providing insights to help increase online sales. It’s the first time the team is looking at this specific data and they are concerned about the quality of data that has been captured.
They decide to use the following approach as the next step:
- A . Trend Analysis
- B . Classification analysis
- C . Data Analysis
- D . Exploratory analysis
D
Explanation:
Exploratory analysis is the approach that the analytics team should use as the next step, because it is a technique that allows them to examine the quality, structure, and characteristics of the data, without making any assumptions or hypotheses. Exploratory analysis can help the team identify any issues or anomalies in the data, such as missing values, outliers, or errors, and decide how to handle them. Exploratory analysis can also help the team discover any patterns, trends, or relationships in the data, and generate new research questions or hypotheses for further analysis.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data
• Understanding the Guide to Business Data Analytics, page 16
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 8
Operation managers are concerned about the increasing attrition rates in the call center. A series of interviews is being conducted with call center agents to collect information to better understand the problem. Interviewees will ask open and closed ended questions that are both quantitative and qualitative.
Which of the following is considered a qualitative open-ended question?
- A . How does call volume contribute to job burnout?
- B . Would morale improve if you could work 2 days per week from home?
- C . How many calls on average do you service in an hour?
- D . Do you receive more calls on Mondays or Fridays?
A
Explanation:
A qualitative open-ended question is a question that allows the respondent to express their thoughts, feelings, or opinions in their own words, without being constrained by predefined options or categories. A qualitative open-ended question can help the interviewer explore the underlying reasons, motivations, or perceptions of the respondent. Option A is a qualitative open-ended question, because it asks the respondent to explain how call volume affects their job satisfaction and well-being, which may vary from person to person and require elaboration. Options B, C, and D are not qualitative open-ended questions, because they ask the respondent to choose between two alternatives (B and D) or provide a numerical value ©, which are quantitative and closed-ended responses.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 2: Source Data
• Understanding the Guide to Business Data Analytics, page 14
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 9
The research question prompting the use of analytics is well-defined. The team obtains the results and determines that the source data did not provide reliable results. As a result of this finding, the team modifies the original question to one that can be answered by the data.
What is a risk that could impact the value of this analysis?
- A . The objective of the original research may not be met
- B . Timelines will be pushed out making stakeholders unhappy
- C . Increased costs associated with the source data
- D . The quality of the analysis may be negatively impacted
A
Explanation:
The risk that could impact the value of this analysis is that the objective of the original research may not be met, because the team modified the research question to fit the data, rather than finding the data that fits the research question. This could lead to a loss of alignment between the research question and the business problem, stakeholder needs, or analytical methods. The team may end up answering a different or less relevant question than the one they intended to answer, and thus provide less valuable insights or recommendations.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 1: Identify the Research Questions
• Understanding the Guide to Business Data Analytics, page 10-11
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 10
An online retailer of men’s athletic apparel is seeking to become the market leader in the industry. To deliver on this strategy, the analytics team continuously collects data on the prices of competitor products and uses this information to adjust the retailer’s prices.
What type of analytics is the retailer using to maintain their pricing structure?
- A . Descriptive
- B . Diagnostic
- C . Predictive
- D . Prescriptive
D
Explanation:
Prescriptive analytics is the type of analytics that the retailer is using to maintain their pricing structure, because it is a technique that uses data and models to recommend the best course of action for a given situation. Prescriptive analytics can help the retailer optimize their prices based on the data collected from the competitors, the market conditions, and the customer preferences, and thus achieve their strategic goal of becoming the market leader.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data
• Understanding the Guide to Business Data Analytics, page 17
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 11
A financial software company has growth and expansion as one of their top strategic priorities for the year. The senior executive team would like to assess their sales performance over the last 3 years to help set sales objectives. In discussion with the business analytics manager, for a comprehensive sales report, the sales lead recommends looking into the number of contracts signed over the past 3 years and the dollar value for the signed contracts.
Which other question is important to consider when evaluating sales performance?
- A . What is the time to market the software?
- B . What is the total cost incurred per year?
- C . What is the number of customers retained over the past 3 years?
- D . What is the average time for conversion?
D
Explanation:
The average time for conversion is the average number of days it takes to convert a lead into a customer. This is an important question to consider when evaluating sales performance, because it indicates the efficiency and effectiveness of the sales process. A shorter time for conversion means that the sales team can close more deals in less time, and thus increase the revenue and profitability of the company. A longer time for conversion may indicate that there are bottlenecks, challenges, or inefficiencies in the sales process that need to be addressed.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 5: Use Results to Influence Business Decision Making
• Understanding the Guide to Business Data Analytics, page 9
• Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 4: Interpret and Report Results, Lecture 19: Sales Performance Metrics
With the recent departure of two of its employees, an IT helpdesk team is now understaffed and finding it difficult to keep up with the current workload. The number of tickets being received has increased as well as the number of days to resolve the tickets. The IT manager has set up a meeting with the IT director to request funding for two new helpdesk agents. To prepare for the meeting, the manager is interested in showing the tickets processed against ticket volume over the past year.
What type of chart should the manager use to effectively show the change in processing rate over time?
- A . A pie chart to compare the number of tickets coming in versus tickets being processed each month, over the past year
- B . A column chart to compare the number of tickets coming in versus tickets being processed each month, since June
- C . A line chart to show the widening gap between the number of tickets being processed against the number coming over the past year
- D . A waterfall chart to show the number of tickets coming in are a lot higher than those being processed as of year to date
C
Explanation:
A line chart is the type of chart that the manager should use to effectively show the change in processing rate over time, because it is a technique that displays data as a series of points connected by straight lines. A line chart can help the manager visualize the trends and patterns in the ticket volume and processing rate over the past year, and highlight the widening gap between them. A line chart can also show the seasonal variations and fluctuations in the data, and compare the performance of different categories or groups. Options A, B, and D are not suitable for showing the change in processing rate over time, because they are techniques that display data as proportions (A), comparisons (B), or accumulations (D) of different categories or groups at a single point in time or over a fixed period.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 4: Interpret and Report Results
• Understanding the Guide to Business Data Analytics, page 18
• 16 Best Types of Charts and Graphs for Data Visualization [+ Guide]
The analytics team has established two equally strong potential recommendations which will deliver the desired outcomes with similar benefits to be derived from each one. On the surface there is no discernable difference in costs or schedule for either option.
To help the analytics team reach a recommendation the business analysis professional recommends the team:
- A . Complete market research
- B . Assess risks for each option
- C . Vote to choose the recommendation
- D . Seek management guidance
B
Explanation:
Assessing risks for each option is the recommendation that the business analysis professional should make to the analytics team, because it is a technique that involves identifying, analyzing, and evaluating the potential positive or negative impacts of each option on the project, the organization, or the stakeholders. Assessing risks can help the team compare the pros and cons of each option, and determine which one has the highest expected value or the lowest expected loss. Assessing risks can also help the team prepare contingency plans or mitigation strategies for the chosen option, and communicate the rationale and assumptions behind their recommendation.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 5: Use Results to Influence Business Decision Making
• Understanding the Guide to Business Data Analytics, page 9
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam
Sample Questions and Self-Assessment, Question 12
Based on the results of a recently completed analytics initiative, the Human Resource department for a major department store implemented a change to its hiring practice to address the attrition rates of its sales associates. The new policy stated that candidates applying for sales positions must possess at least 3 years of relevant sales experience to be considered. After implementing the change, attrition rates are 10% higher and management is frustrated.
Which of the following could result in this outcome?
- A . The results of analysis have been incorrectly interpreted
- B . Sales experience is not a relevant skill
- C . Analytics is not helpful given this situation
- D . The change proposed is not aligned to company strategy
D
Explanation:
The change proposed is not aligned to company strategy, because it may not address the root cause of the attrition problem, or it may conflict with other organizational goals or values. For example, the change may reduce the pool of qualified candidates, increase the hiring costs, or lower the diversity or customer satisfaction of the sales team. The change may also ignore other factors that influence the attrition rates, such as compensation, training, feedback, or recognition. Therefore, the change may not achieve the desired outcome of reducing attrition, and may even worsen it.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 5: Use Results to Influence Business Decision Making
• Understanding the Guide to Business Data Analytics, page 9
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 13
A company wants to gauge the thoughts of their employees towards a new company product. On the 25th of March the interviewer makes a list of all employees who were at work on that day and then chooses a subset of those employees to interview.
Which term describes the list of all employees present on March 25th?
- A . Population of interest
- B . Survey sample
- C . Sampling frame
- D . Sample weights
C
Explanation:
The sampling frame is the term that describes the list of all employees present on March 25th, because it is a technique that defines the set of elements from which a sample is drawn. The sampling frame should ideally match the population of interest, which is the group of elements that the researcher wants to study or make inferences about. In this case, the population of interest is the employees of the company, and the sampling frame is the subset of employees who were at work on a specific day. The survey sample is the technique that selects a portion of the sampling frame to participate in the survey. The sample weights are the technique that assigns different values or importance to each element in the sample, based on their representation in the population.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 2: Source Data
• Understanding the Guide to Business Data Analytics, page 14
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 14
A Data Dictionary is being developed for an employee database. When reviewing the data dictionary, the analyst recommends adding another primitive data element.
Which element would be suggested?
- A . Street address
- B . First name
- C . Customer name
- D . Work phone number
A
Explanation:
A street address is a primitive data element, because it is a basic unit of data that cannot be further decomposed into smaller components. A primitive data element has a distinct name, definition, format, and value domain. A street address can be used to identify the location of an employee or a customer, and it can be stored as a string or a combination of numbers and characters. Options B, C, and D are not primitive data elements, because they can be further broken down into smaller components. For example, a first name can be divided into a prefix, a given name, and a suffix. A customer name can be composed of a first name and a last name. A work phone number can be split into a country code, an area code, and a local number.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 2: Source Data
• Business analysis data dictionary C The Functional BA
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 15
The results for a certification exam were revealed in percentage and percentile. The results for one of the attendees was: 75%, 90th percentile.
What is the value in sharing the percentile score?
- A . The percentile score provides value by assessing the attendee’s score against the average score for that exam
- B . While the exam score is an objective score, the percentile is a relative score that assesses the attendee’s score against the highest possible score
- C . By ranking, it provided additional insight on how the attendee performed in comparison to other attendees
- D . The percentile score does not add any additional value in assessing the attendee’s performance
C
Explanation:
The percentile score provides value by ranking the attendee’s score among all the scores of the exam takers. A percentile score of 90 means that the attendee scored higher than 90% of the exam takers, and only 10% scored higher than the attendee. This gives a relative measure of how the attendee performed in comparison to other attendees, and how competitive or exceptional the score is. The percentile score does not depend on the average or the highest possible score of the exam, but only on the distribution of the scores of the exam takers.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 4: Interpret and Report Results
• Understanding the Guide to Business Data Analytics, page 9
• What is a Percentile? – Statistics By Jim
Senior executives in a large organization receive numerous sales reports of every sale through a corporate dashboard on a weekly basis. The executives are considering budget increases for various functions but would like to know if they are obtaining good returns for current budget allocations. They ask the analytics team to research and answer: "How effective is our marketing spend?" This question is:
- A . Already answered in the sales data
- B . Difficult to analyze because its narrowly focused
- C . Sufficient to begin initial analysis
- D . Too broadly scoped to be effectively answered
D
Explanation:
The question “How effective is our marketing spend?” is too broadly scoped to be effectively answered, because it is a vague and ambiguous question that does not specify the criteria, scope, or timeframe for measuring the effectiveness of the marketing spend. The question also does not define what constitutes marketing spend, or how it relates to the sales data or the budget allocations. The question needs to be refined and clarified to make it more focused, relevant, and feasible for the analytics team to answer. For example, the question could be rephrased as “How does the marketing spend per channel affect the sales revenue and customer retention rate in the last quarter?”
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 1: Identify the Research Questions
• Understanding the Guide to Business Data Analytics, page 10-11
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 16
The analytics team is struggling with which recommendation to make. Their challenge is that they have five good options and this indecision is stopping them from moving forward.
To help the team finalize their recommendation, the BA professional on the team recommends they complete:
- A . Root cause analysis
- B . Business rules analysis
- C . Data flow diagrams
- D . Acceptance and evaluation criteria
D
Explanation:
Acceptance and evaluation criteria are the techniques that the BA professional on the team should recommend they complete, because they are the standards or measures that are used to evaluate the suitability and value of each option. Acceptance and evaluation criteria can help the team compare the benefits, costs, risks, and impacts of each option, and determine which one best meets the needs and expectations of the stakeholders. Acceptance and evaluation criteria can also help the team communicate the rationale and evidence behind their recommendation, and ensure that the recommendation is aligned with the business goals and objectives.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 5: Use Results to Influence Business Decision Making
• Understanding the Guide to Business Data Analytics, page 9
• Acceptance and Evaluation Criteria | Business Analysis
The analytics team is assessing the results of their analysis. They are surprised to find that their data indicates two events seem to be strongly related even though the general belief in the organization is that they are independent of each other. Knowing that this information will be used for decision making, they are concerned about presenting this data.
At an impasse, the business analysis professional reminds them that the data can be presented as long as the team has:
- A . Review the results with management ahead of time and highlight any potential risk of using this data
- B . Confidence that the correlation will reliably occur in the future and the risk of acting on this is low
- C . Followed all rules for data analysis endorsed as organizational standards so the risk of acting on this is low
- D . The ability to rerun the data analysis and the results are the same thereby minimizing the risk of acting on this
D
Explanation:
The ability to rerun the data analysis and the results are the same is the condition that the team should have before presenting the data, because it is a technique that ensures the validity, reliability, and reproducibility of the data analysis. By rerunning the data analysis, the team can verify that the results are consistent and not affected by random errors, biases, or anomalies. The team can also confirm that the data analysis process is well-documented, transparent, and traceable, and that the results can be replicated by other analysts or stakeholders. This can minimize the risk of acting on the data, and increase the confidence and trust in the data analysis.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 4: Interpret and Report Results
• Understanding the Guide to Business Data Analytics, page 9
• Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 4: Interpret and Report Results, Lecture 20: Data Validation and Verification
An analytics team has been asked to answer the following question: "Given that you’re a customer, would you work at our company?"
The team is concerned about answering this question because it is:
- A . Insignificant
- B . Short
- C . Unethical
- D . Unclear
D
Explanation:
The question “Given that you’re a customer, would you work at our company?” is unclear, because it is a hypothetical and subjective question that does not specify the purpose, scope, or context of the analysis. The question also does not define what constitutes a customer, or how the customer’s experience or satisfaction relates to the employee’s motivation or performance. The question needs to be refined and clarified to make it more focused, relevant, and feasible for the analytics team to answer. For example, the question could be rephrased as “How does the customer satisfaction score affect the employee retention rate in our company?”
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 1: Identify the Research Questions
• Understanding the Guide to Business Data Analytics, page 10-11
• CERTIFICATION IN BUSINESS DATA ANALYTICS HANDBOOK – IIBA®, page 8, CBDA Exam Sample Questions and Self-Assessment, Question 16
A data scientist is performing statistical analysis and is interested in graphically depicting the data set according to the associated quartiles Minimum, First Quartile, Median, Second Quartile, Third Quartile.
Which technique would allow for the display of this statistical five number summary?
- A . Gaussian distribution
- B . Scatter plot
- C . Multivariate histogram
- D . Box plot
D
Explanation:
A box plot is the technique that would allow for the display of the statistical five number summary, because it is a technique that shows the distribution of a data set using a rectangular box and whiskers. A box plot can help the data scientist visualize the minimum, maximum, median, first quartile, and third quartile of the data set, as well as any outliers or skewness. A box plot can also help the data scientist compare the variation and symmetry of different groups or categories of data. Options A, B, and C are not suitable for displaying the statistical five number summary, because they are techniques that show the frequency, relationship, or density of the data, but not the quartiles or outliers.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data
• Understanding the Guide to Business Data Analytics, page 18
• 16 Best Types of Charts and Graphs for Data Visualization [+ Guide]
An online retailer has been successful utilizing analytics to guide decisions on product placement and marketing spend.
Management has requested a task force be assembled to make recommendations on how to further develop their analytics capabilities. To begin this work, the task force builds a model to develop a shared understanding about customer segments, customer relationships, key partnerships, and the company’s value proposition.
The team has leveraged the following model to facilitate this discussion?
- A . Value chain analysis
- B . Balanced scorecard
- C . Business model canvas
- D . CATWOE
C
Explanation:
The business model canvas is the model that the task force has leveraged to facilitate the discussion, because it is a technique that describes the logic of how an organization creates, delivers, and captures value. The business model canvas consists of nine building blocks that cover the key aspects of a business: customer segments, value proposition, channels, customer relationships, revenue streams, key resources, key activities, key partnerships, and cost structure. The business model canvas can help the task force develop a shared understanding of the current state of the online retailer, and identify the opportunities and challenges for developing their analytics capabilities.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 6: Guide Organization-level Strategy for Business Analytics
• Understanding the Guide to Business Data Analytics, page 9
• 10.8 Business Model Canvas | IIBA®
While sourcing data, an analyst runs into a situation where different business units are using different names to refer to the same data element. This lack of standardization is resulting in confusion and additional time required to properly prepare data for analysis.
Which practice, if implemented would address this situation and mature the organization’s business analytics practice?
- A . Data quality management
- B . Database operations management
- C . Data warehousing
- D . Meta data management
D
Explanation:
Meta data management is the practice that, if implemented, would address the situation and mature the organization’s business analytics practice, because it is a technique that involves defining, documenting, and maintaining the information about the data elements, such as their names, definitions, formats, sources, and relationships. Meta data management can help the analyst resolve the inconsistencies and ambiguities in the data element names, and ensure that the data is standardized, consistent, and understandable across different business units. Meta data management can also help the analyst improve the data quality, accessibility, and usability for the analysis.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 2: Source Data
• Guide to Business Data Analytics – Iiba – Google Books, page 14
• Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 2: Source Data, Lecture 8: Meta Data Management
A dataset contains 10 measures of workplace sustainability. The analytics team is in need of producing a single score of sustainability.
Which of the following techniques if used would achieve this objective?
- A . Logistic regression
- B . Linkage algorithms
- C . Factor analysis
- D . K means clustering
C
Explanation:
Factor analysis is the technique that, if used, would achieve the objective of producing a single score of sustainability, because it is a technique that reduces the dimensionality of a data set by identifying the underlying factors or latent variables that explain the variation and correlation among the observed variables. Factor analysis can help the analytics team combine the 10 measures of workplace sustainability into a smaller number of factors, and then derive a composite score of sustainability based on the factor loadings and weights. Factor analysis can also help the analytics team simplify and interpret the data, and identify the key drivers of sustainability.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data
• Understanding the Guide to Business Data Analytics, page 17
• Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 3: Analyze Data, Lecture 15: Factor Analysis
An analyst is looking at a particular dataset that includes the scores across all 8th grade students, across three schools. The analyst is trying to determine which type of statistics average to use to best represent the results. On looking through the dataset, the analyst has identified a few extreme outliers. As a result, the analyst was led to use the following type of average:
- A . Median
- B . Range
- C . Mean
- D . Mode
A
Explanation:
The median is the type of statistics average that the analyst should use to best represent the results, because it is a measure of central tendency that divides the data set into two equal halves. The median is the middle value of the data set when it is arranged in ascending or descending order. The median is not affected by extreme outliers, unlike the mean, which is the arithmetic average of the data set. The median can give a more accurate representation of the typical score of the 8th grade students across the three schools. Options B, C, and D are not types of statistics average, but types of statistics measures that describe other aspects of the data set. The range is a measure of dispersion that shows the difference between the highest and the lowest values of the data set. The mean is a measure of central tendency that shows the sum of the values of the data set divided by the number of values. The mode is a measure of central tendency that shows the most frequent value of the data set.
Reference:
• Business Analysis Certification in Data Analytics, CBDA | IIBA®, CBDA Competencies, Domain 3: Analyze Data
• Understanding the Guide to Business Data Analytics, page 17
• Business Data Analytics (IIBA®-CBDA Exam preparation) | Udemy, Section 3: Analyze Data, Lecture 13: Descriptive Statistics
A software company launched a new product in late 2016. The product manager is reviewing a Box and Whisker plot used to compare year-over-year sales, from 2017 to 2018.
What is the conclusion he can make from this chart?
- A . 2017 minimum and maximum sales are higher than 2018, and the 2017 median result is higher than the 2018 median result
- B . 2017 minimum and maximum sales are higher than 2018, but the 2017 median result is lower than 2018 1st quartile result
- C . 2018 minimum and maximum sales are higher than 2017, and the 2018 quartile results are higher than 2017 quartile results
- D . 2018 minimum and maximum sales are higher than 2017, and the 2018 1st quartile is higher than 2017 median result
The interplay between enterprise systems and data analytics can be envisioned at various layers.
The layer that connects the business processes to data analytics is the:
- A . information layer
- B . physical layer
- C . technical layer
- D . infrastructure layer
A
Explanation:
The information layer is the layer that connects the business processes to data analytics. It consists of the data models, data quality, data governance, and data security that enable the data to be accessed, analyzed, and transformed into insights. The information layer also supports the communication and collaboration among the stakeholders involved in the data analytics process. The other layers are the physical layer, which deals with the hardware and software components of the data infrastructure; the technical layer, which handles the data integration, data storage, data processing, and data analysis techniques; and the infrastructure layer, which provides the network, cloud, and security services for the data environment12
Reference: 1: Data and Analytics (D&A) – Gartner 2: Enterprise Data Analytics – SelectHub
The team has completed their analysis on a vast amount of collected data and agree on their recommendations for action.
However, they are having difficulty in developing the appropriate messages to support their recommendations. The business analysis professional suggests which technique to assist the team?
- A . T-Testing
- B . Simulation
- C . Visioning
- D . Storyboarding
D
Explanation:
Storyboarding is a technique that helps the team to develop the appropriate messages to support their recommendations by creating a visual sequence of the main points, evidence, and actions. Storyboarding helps the team to organize their thoughts, identify gaps, and communicate their findings in a clear and compelling way12
Reference: 1: Developing Key Messages for Effective Communication – MSKTC 2: 11 Ways Highly Successful Leaders Support Their Team – Redbooth
A database analyst is modelling a database for a large toy manufacturer.
Which statement describes a logical database model?
- A . The layer of views created to summarize data or provide another perspective of certain data
- B . A model that depicts the actual design of the relational database
- C . An abstraction of the conceptual data model that includes rules of normalization
- D . Modelling that involves objects being defined at the schema level
C
Explanation:
A logical database model is a data model of a specific problem domain expressed independently of a particular database management product or storage technology. It describes data using notation that corresponds to a data organization used by a database management system, such as relational tables and columns. It also includes rules of normalization, which are the process of converting complex data structures into simple, stable data structures12
Reference: 1: Logical schema – Wikipedia 2: What Is a Data Model? | Coursera
A professional association is funded by membership fees. The membership renewal occurs every 5 years. Although, they have a strong subscription rate each year, their renewal rate is low. They are working with an external firm specializing in Business Analytics to identify the groups of customers that have a high likelihood of cancelling their subscription after their first 5-year term ends.
This type of study is called:
- A . Untrained learning
- B . Supervised learning
- C . Trained learning
- D . Unsupervised learning
D
Explanation:
Unsupervised learning is a type of study that involves finding patterns or clusters in data without any predefined labels or outcomes. It is useful for exploring data and discovering hidden structures or groups of customers. For example, the professional association can use unsupervised learning to identify the characteristics of customers who are likely to cancel their subscription after their first 5-year term ends, and then design strategies to retain them12
Reference: 1: What is Unsupervised Learning? – IBM 2: Unsupervised Learning – IIBA BABOK Guide v3
Which attributes from the Order entity will need to be normalized to avoid redundancies?
. Orderld
. OrderDate
. Itemld
. ItemName
. Quantity
. ItemPrice
- A . OrderDate ItemPrice
- B . ItemName ItemPrice
- C . OrderDate ItemName
- D . Item Name Quantity
B
Explanation:
The attributes ItemName and ItemPrice need to be normalized to avoid redundancies because they depend on the attribute ItemId, which is not part of the primary key of the Order entity. This is a case of partial dependency, which violates the second normal form (2NF) of database normalization. To achieve 2NF, the Order entity should be split into two entities: Order and Item, where Item contains the attributes ItemId, ItemName, and ItemPrice, and Order contains the attributes OrderId, OrderDate, ItemId, and Quantity. This way, the ItemName and ItemPrice are stored only once for each ItemId, and the Order entity references them through a foreign key12
Reference: 1: Balancing Data Integrity and Performance: Normalization vs … 2: Normalization Process in DBMS – GeeksforGeeks
The architecture team puts forth a solution architecture that integrates multiple data sources from within and outside the organization. The architecture provides the foundation to source a new analytics program.
If one of the objectives of the analytics team was to provide ‘one source of the truth’, this objective would be referring to which of the following?
- A . Identifying one key stakeholder, who can make final decisions about which sources to relate/merge
- B . Evaluating the completeness, validity, and reliability of the data from source systems
- C . Ensuring stakeholders always have clear insight into the final requirements at all times
- D . Enforcing master data management principles and practices
D
Explanation:
Providing ‘one source of the truth’ means ensuring that there is a single, consistent, and authoritative source of data that can be used for analytics and decision making across the organization. This objective can be achieved by enforcing master data management principles and practices, which involve defining, governing, and maintaining the quality and integrity of the core data entities that are shared by multiple systems and processes. Master data management helps to eliminate data silos, reduce data duplication and inconsistency, and improve data accuracy and reliability12
Reference: 1: What is Master Data Management (MDM)? – Informatica 2: Master Data Management – IIBA BABOK Guide v3
As the team discusses how to utilize the results of their data analysis to put forth a business recommendation, an analyst on the team voices concern over the current organizational culture presenting a roadblock to their ability to influence business decision making.
Which of the following would be a justifiable concern at this stage of the team’s efforts?
- A . Difficulty bringing business stakeholders to a shared understanding about value when sharing data assets across business domains
- B . Changing the mindsets of business stakeholders to trust insights gleaned from data over experience and intuition
- C . Applying a myopic view of data and establishing data silos which create roadblocks to exploring
available data sources - D . Finding data that creates value creating difficulties, as not all data helps a business make better decisions
B
Explanation:
A justifiable concern at this stage of the team’s efforts is changing the mindsets of business stakeholders to trust insights gleaned from data over experience and intuition. This is because some stakeholders may have a strong attachment to their own opinions or beliefs, and may resist or ignore data that contradicts them. This can create a barrier to data-driven decision making, which requires a culture of curiosity, openness, and evidence-based reasoning. The team needs to communicate the value and validity of their data analysis, and persuade the stakeholders to adopt a data-driven mindset12
Reference: 1: Use Data to Accelerate Your Business Strategy 2: Data-Driven Decision Making: A Step-by-Step Guide
An analytics team is interested in reviewing the results of a public opinion poll that is going to be conducted at the end of the month. One of the factors the team is interested in, is ensuring the result set is statistically significant.
Why would this factor be important to the team?
- A . To make sure the criteria for the target audience is met
- B . Guarantee that the objectives of the poll are met
- C . Improve the likelihood of receiving a response rate of 100%
- D . Ensure that results are not biased or random
D
Explanation:
Ensuring the result set is statistically significant is important to the team because it means that the difference or relationship observed in the data is unlikely to be due to chance or sampling error. Statistical significance helps the team to assess the validity and reliability of their findings, and to draw meaningful conclusions and recommendations from the data. Statistical significance also helps the team to communicate their results with confidence and credibility to the stakeholders and decision makers12
Reference: 1: An Easy Introduction to Statistical Significance (With Examples) – Scribbr 2: Statistical Significance in Experimentation and Data Analysis – All About Circuits
The outcome from an analytics initiative has resulted in key stakeholders wanting to move forward
with a project to redesign the company’s website. The business analyst has called a meeting to work on drafting a plan to assess the level of effort required to complete this work. Many of the invited participants redesigned the website before and were invited so they could provide estimates using their knowledge and experience from the past.
The business analyst is using which method to estimate this work?
- A . Rolling wave
- B . PERT
- C . Parametric
- D . Rough order of magnitude
D
Explanation:
The business analyst is using the rough order of magnitude method to estimate this work. This method is based on expert opinion or experience from past projects, and it provides a quick and approximate estimate of the cost, time, or effort required for a project or a task. This method is useful when there is limited information or data available, or when a high-level estimate is needed for planning or budgeting purposes. However, this method also has a high degree of uncertainty and variability, and it should be refined as more details become available12
Reference: 1: Project Estimation Techniques Business Analysts Should Know About 2: Estimation techniques for business analysts C The Functional BA
The analytics team has been asked to provide an estimate of the number of customers they expect to have in 12 months. They debated how accurate that figure needs to be and determined that based on the availability of good data, they could predict within + or – 10%.
This is an example of a:
- A . ROM estimate
- B . Delphi estimate
- C . Parametric estimate
- D . Definitive estimate
A
Explanation:
A ROM estimate is a rough order of magnitude estimate that provides a quick and approximate estimate of the cost, time, or effort required for a project or a task. A ROM estimate is based on expert opinion or experience from past projects, and it usually has a large range of variation, such as + or – 10%. A ROM estimate is useful when there is limited information or data available, or when a high-level estimate is needed for planning or budgeting purposes. However, a ROM estimate also has a high degree of uncertainty and variability, and it should be refined as more details become available12
Reference: 1: Project Estimation Techniques Business Analysts Should Know About 2: Estimation techniques for business analysts C The Functional BA
To gain traction on online sales, a retailer initiated a marketing campaign using banner ads. The company has requested their analytics team to evaluate the performance of the campaign. During the presentation, the analyst confirmed that the campaign did bring in a large number of net new customers to the website and met the target sales conversion rate. They also noted that there was a high number of repeat visitors not completing a sale.
What decision would help the retailer improve sales conversion rates for repeat visitors?
- A . Increase investment in banner ads
- B . Incentivize customers to subscribe to promotional notifications
- C . Add additional new products to attract customers
- D . Ensure the sales checkout process is streamlined
D
Explanation:
According to the Business Data Analytics: A Decision-Making Paradigm1, one of the key steps in the analytics process is to communicate insights and recommendations to stakeholders. The analyst should present the findings in a clear and concise manner, and provide actionable suggestions to improve the business outcomes. In this case, the analyst has identified that repeat visitors are not completing a sale, which indicates a possible issue with the sales checkout process. Therefore, the analyst should recommend the retailer to streamline the sales checkout process, which could reduce friction, increase customer satisfaction, and boost sales conversion rates for repeat visitors.
Reference: Business Data Analytics: A Decision-Making Paradigm