Topic 1, Describe Artificial Intelligence workloads and considerations
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
Reliability and safety: To build trust, it’s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
DRAG DROP
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.
You run a charity event that involves posting photos of people wearing sunglasses on Twitter.
You need to ensure that you only retweet photos that meet the following requirements:
Include one or more faces.
Contain at least one person wearing sunglasses.
What should you use to analyze the images?
- A . the Verify operation in the Face service
- B . the Detect operation in the Face service
- C . the Describe Image operation in the Computer Vision service
- D . the Analyze Image operation in the Computer Vision service
B
Explanation:
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/face/overview
For a machine learning progress, how should you split data for training and evaluation?
- A . Use features for training and labels for evaluation.
- B . Randomly split the data into rows for training and rows for evaluation.
- C . Use labels for training and features for evaluation.
- D . Randomly split the data into columns for training and columns for evaluation.
B
Explanation:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/split-data
HOTSPOT
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic. NOTE: Each correct selection is worth one point.
Explanation:
For the first statement, "There are [answer choice] correctly predicted positives.", the correct choice is 11. This number represents the true positives, where the model correctly predicted the positive class.
For the second statement, "There are [answer choice] false negatives.", the correct choice is 5. This number represents the cases where the actual class was positive, but the model incorrectly predicted the negative class.
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Yes
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Box 2: Yes
Statement: A triage bot that prioritizes insurance claims based on injuries is an example of the Microsoft reliability and safety principle for responsible AI.
Answer. Yes
Explanation: The reliability and safety principle focuses on ensuring that AI systems operate reliably and safely, prioritizing user well-being and avoiding harm. A triage bot prioritizing insurance claims based on the severity of injuries aligns with ensuring well-being and safety.
Box 3: No
Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Your company is exploring the use of voice recognition technologies in its smart home devices. The company wants to identify any barriers that might unintentionally leave out specific user groups.
This an example of which Microsoft guiding principle for responsible AI?
- A . accountability
- B . fairness
- C . inclusiveness
- D . privacy and security
C
Explanation:
Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
AI systems should empower everyone and engage people. AI should bring benefits to all parts of society, regardless of physical ability, gender, sexual orientation, ethnicity, or other factors.
https://docs.microsoft.com/en-us/learn/modules/get-started-ai-fundamentals/7-understand-responsible-ai
You are building an AI system.
Which task should you include to ensure that the service meets the Microsoft transparency principle for responsible AI?
- A . Ensure that all visuals have an associated text that can be read by a screen reader.
- B . Enable autoscaling to ensure that a service scales based on demand.
- C . Provide documentation to help developers debug code.
- D . Ensure that a training dataset is representative of the population.
C
Explanation:
Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
You are building an AI-based app.
You need to ensure that the app uses the principles for responsible AI.
Which two principles should you follow? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
- A . Implement an Agile software development methodology
- B . Implement a process of Al model validation as part of the software review process
- C . Establish a risk governance committee that includes members of the legal team, members of the risk management team, and a privacy officer
- D . Prevent the disclosure of the use of Al-based algorithms for automated decision making
B,C
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/3-implications-responsible-ai-practical
DRAG DROP
Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.
Explanation:
Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments.
This is an example of which Microsoft guiding principle for responsible AI?
- A . fairness
- B . inclusiveness
- C . reliability and safety
- D . accountability
B
Explanation:
Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer.
Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
Reliability & Safety
https://en.wikipedia.org/wiki/Tay_(bot)
“To build trust, it’s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. It’s also important to be able to verify that these systems are behaving as intended under actual operating conditions.
How they behave and the variety of conditions they can handle reliably and safely largely reflects the range of situations and circumstances that developers anticipate during design and testing. We believe that rigorous testing is essential during system development and deployment to ensure AI systems can respond safely in unanticipated situations and edge cases, don’t have unexpected performance failures, and don’t evolve in ways that are inconsistent with original expectations”
A company employs a team of customer service agents to provide telephone and email support to customers.
The company develops a webchat bot to provide automated answers to common customer queries.
Which business benefit should the company expect as a result of creating the webchat bot solution?
- A . increased sales
- B . a reduced workload for the customer service agents
- C . improved product reliability
DRAG DROP
Match the principles of responsible AI to appropriate requirements.
To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Explanation:
Graphical user interface, text, application, email
Description automatically generated
When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable.
This is an example of which Microsoft guiding principle for responsible AI?
- A . transparency
- B . inclusiveness
- C . fairness
- D . privacy and security
A
Explanation:
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Reference:
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/best-practices/trusted-ai
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/strategy/responsible-ai
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
DRAG DROP
You plan to deploy an Azure Machine Learning model as a service that will be used by client applications.
Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order.
Explanation:
Graphical user interface, text, application, chat or text message
Description automatically generated
You build a machine learning model by using the automated machine learning user interface (UI).
You need to ensure that the model meets the Microsoft transparency principle for responsible AI.
What should you do?
- A . Set Validation type to Auto.
- B . Enable Explain best model.
- C . Set Primary metric to accuracy.
- D . Set Max concurrent iterations to 0.
B
Explanation:
Model Explain Ability.
Most businesses run on trust and being able to open the ML “black box” helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.
Reference: https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning-service/
What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
- A . knowledgeability
- B . decisiveness
- C . inclusiveness
- D . fairness
- E . opinionatedness
- F . reliability and safety
C,D,F
Explanation:
Reference: https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
DRAG DROP
Match the Microsoft guiding principles for responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right. Each principle may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Reliability and safety
To build trust, it’s critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Box 2: accountability
Box 3: Privacy and security
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply withprivacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: No
Box 2: Yes
Box 3: No
Anomaly detection encompasses many important tasks in machine learning:
Identifying transactions that are potentially fraudulent.
Learning patterns that indicate that a network intrusion has occurred.
Finding abnormal clusters of patients.
3 、 Checking values entered into a system.
Predicting whether a patient will develop diabetes based on the patient ’ s medical history is an example of anomaly detection.
Answer. No
This is more related to predictive modeling or classification rather than anomaly detection.
Topic 2, Describe fundamental principles of machine learning on Azure
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
You need to predict the income range of a given customer by using the following dataset.
Which two fields should you use as features? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
- A . Education Level
- B . Last Name
- C . Age
- D . Income Range
- E . First Name
A,C
Explanation:
First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
Table
Description automatically generated with medium confidence
Regression is a machine learning task that is used to predict the value of the label from a set of related features.
You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
- A . the model name
- B . the training endpoint
- C . the authentication key
- D . the REST endpoint
C,D
Explanation:
https://docs.microsoft.com/en-in/learn/modules/create-regression-model-azure-machine-learning-designer/deploy-service
DRAG DROP
You need to use Azure Machine Learning designer to build a model that will predict automobile prices.
Which type of modules should you use to complete the model? To answer, drag the appropriate modules to the correct locations. Each module may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
Explanation:
Diagram
Description automatically generated
Box 1: Select Columns in Dataset
For Columns to be cleaned, choose the columns that contain the missing values you want to change. You can choose multiple columns, but you must use the same replacement method in all selected columns.
Example:
Box 2: Split data
Splitting data is a common task in machine learning. You will split your data into two separate datasets. One dataset will train the model and the other will test how well the model performed.
Box 3: Linear regression
Because you want to predict price, which is a number, you can use a regression algorithm.
For this example, you use a linear regression model.
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: No
The validation dataset is different from the test dataset that is held back from the training of the model.
Box 2: Yes
A validation dataset is a sample of data that is used to give an estimate of model skill while tuning model’s hyperparameters.
Box 3: No
The Test Dataset, not the validation set, used for this. The Test Dataset is a sample of data used to provide an unbiased evaluation of a final model fit on the training dataset.
Which type of machine learning should you use to identify groups of people who have similar purchasing habits?
- A . classification
- B . regression
- C . clustering
C
Explanation:
Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset
Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
In the most basic sense, regression refers to prediction of a numeric target.
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable.
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions.
Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
- A . dataset
- B . compute
- C . pipeline
- D . module
A,D
Explanation:
You can drag-and-drop datasets and modules onto the canvas.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
Confidence
Confidence is the calculated probability of a correct image classification. In classification tasks, the model often outputs a confidence score representing the probability that the given input belongs to each of the possible classes.
DRAG DROP
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.
Explanation:
1- Regression
2- Clustering
3- Classification
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
Text
Description automatically generated
Regression is a machine learning task that is used to predict the value of the label from a set of related features.
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
Graphical user interface, table
Description automatically generated
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
- A . classification
- B . regression
- C . clustering
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
To perform real-time inferencing, you must deploy a pipeline as a real-time endpoint.
Real-time endpoints must be deployed to an Azure Kubernetes Service cluster.
You are building a tool that will process images from retail stores and identify the products of competitors.
The solution will use a custom model.
Which Azure Cognitive Services service should you use?
- A . Custom Vision
- B . Form Recognizer
- C . Face
- D . Computer Vision
A
Explanation:
The Azure Custom Vision service allows you to build and refine custom image classifiers for specific needs. In this case, if you’re building a tool to process images from retail stores and identify competitor’s products, you’d likely need to train a model on specific images of these products. Generic computer vision models (such as the one provided by the Computer Vision service – Option D) may not be trained to recognize these specific products. Form Recognizer (B) is designed for extracting text and structured data from documents, and Face (C) is for detecting, recognizing, and analyzing human faces, neither of which is relevant to the task at hand.
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Explanation:
Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset
Regression is a machine learning task that is used to predict the value of the label from a set of related features.
Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/tasks
When training a model, why should you randomly split the rows into separate subsets?
- A . to train the model twice to attain better accuracy
- B . to train multiple models simultaneously to attain better performance
- C . to test the model by using data that was not used to train the model
C
Explanation:
The goal is to produce a trained (fitted) model that generalizes well to new, unknowndata. The fitted model is evaluated using “new” examples from the held-out datasets (validation andtestdatasets) to estimate the model’s accuracy in classifying new data.
https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets#:~:text=Training%20dataset,-A%20training%20dataset&text=The%20goal%20is%20to%20produce,accuracy%20in%20classifying%20new%20data.
What are two metrics that you can use to evaluate a regression model? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
- A . coefficient of determination (R2)
- B . F1 score
- C . root mean squared error (RMSE)
- D . area under curve (AUC)
- E . balanced accuracy
A,C
Explanation:
A: R-squared (R2), or Coefficient of determination represents the predictive power of the model as a value between -inf and 1.00. 1.00 means there is a perfect fit, and the fit can be arbitrarily poor so the scores can be negative.
C: RMS-loss or Root Mean Squared Error (RMSE) (also called Root Mean Square Deviation, RMSD), measures the difference between values predicted by a model and the values observed from the environment that is being modeled.
Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/resources/metrics
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Yes
Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.
Box 2: No
Box 3: Yes
During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The higher the score, the better the model is considered to "fit" your data. It will stop once it hits the exit criteria defined in the experiment.
Box 4: No
Apply automated ML when you want Azure Machine Learning to train and tune a model for you using the target metric you specify.
The label is the column you want to predict.
Which metric can you use to evaluate a classification model?
- A . true positive rate
- B . mean absolute error (MAE)
- C . coefficient of determination (R2)
- D . root mean squared error (RMSE)
A
Explanation:
What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Yes
In machine learning, if you have labeled data, that means your data is marked up, or annotated, to show the target, which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription, or processing.
Box 2: No
Box 3: No
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the classes), or you are more interested in the performance on either one of the classes, accuracy doesn’t really capture the effectiveness of a classifier.
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
Classification
A medical research project uses a large anonymized dataset of brain scan images that are categorized into predefined brain haemorrhage types.
You need to use machine learning to support early detection of the different brain haemorrhage types in the images before the images are reviewed by a person.
This is an example of which type of machine learning?
- A . clustering
- B . regression
- C . classification
C
Explanation:
Reference: https://docs.microsoft.com/en-us/learn/modules/create-classification-model-azure-machine-learning-designer/introduction
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
feature engineering
Ensuring that numeric variables in training data are on a similar scale is part of feature engineering. This process, often called normalization or scaling, is crucial for many machine learning algorithms to work correctly and converge faster during the training process.
Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?
- A . Form Recognizer
- B . Text Analytics
- C . Ink Recognizer
- D . Custom Vision
A
Explanation:
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
Reference: https://azure.microsoft.com/en-us/services/cognitive-services/form-recognizer/
DRAG DROP
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Model evaluation
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection
In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
You are evaluating whether to use a basic workspace or an enterprise workspace in Azure Machine Learning.
What are two tasks that require an enterprise workspace? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
- A . Use a graphical user interface (GUI) to run automated machine learning experiments.
- B . Create a compute instance to use as a workstation.
- C . Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.
- D . Create a dataset from a comma-separated value (CSV) file.
A,C
Explanation:
Note: Enterprise workspaces are no longer available as of September 2020. The basic workspace now has all the functionality of the enterprise workspace.
Reference:
https://www.azure.cn/en-us/pricing/details/machine-learning/
https://docs.microsoft.com/en-us/azure/machine-learning/concept-workspace
You need to create a training dataset and validation dataset from an existing dataset.
Which module in the Azure Machine Learning designer should you use?
- A . Select Columns in Dataset
- B . Add Rows
- C . Split Data
- D . Join Data
C
Explanation:
A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and then validate the model on the training data. Use the Split Data module to divide a dataset into two distinct sets.
The studio currently supports training/validation data splits
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits2
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.
You have the Predicted vs. True chart shown in the following exhibit.
Which type of model is the chart used to evaluate?
- A . classification
- B . regression
- C . clustering
B
Explanation:
What is a Predicted vs. True chart?
Predicted vs. True shows the relationship between a predicted value and its correlating true value for a regression problem. This graph can be used to measure performance of a model as the closer to the y=x line the predicted values are, the better the accuracy of a predictive model.
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-m
You need to predict the sea level in meters for the next 10 years.
Which type of machine learning should you use?
- A . classification
- B . regression
- C . clustering
B
Explanation:
Regression is a type of supervised learning approach that predicts a continuous outcome variable (in this case, sea level). Since the goal is to predict a numerical or continuous value, which is the sea level in meters for the next 10 years, regression is the most appropriate machine learning approach to use.
You have a dataset that contains information about taxi journeys that occurred during a given period.
You need to train a model to predict the fare of a taxi journey.
What should you use as a feature?
- A . the number of taxi journeys in the dataset
- B . the trip distance of individual taxi journeys
- C . the fare of individual taxi journeys
- D . the trip ID of individual taxi journeys
B
Explanation:
The label is the column you want to predict. The identified Features are the inputs you give the model to predict the Label.
Example:
The provided data set contains the following columns:
vendor_id: The ID of the taxi vendor is a feature.
rate_code: The rate type of the taxi trip is a feature.
passenger_count: The number of passengers on the trip is a feature.
trip_time_in_secs: The amount of time the trip took. You want to predict the fare of the trip before the trip is completed. At that moment, you don’t know how long the trip would take. Thus, the trip time is not a feature and you’ll exclude this column from the model. trip_distance: The distance of the trip is a feature.
payment_type: The payment method (cash or credit card) is a feature.
fare_amount: The total taxi fare paid is the label.
Reference: https://docs.microsoft.com/en-us/dotnet/machine-learning/tutorials/predict-prices
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Yes
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box 2: Yes
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft.
Box 3: No
HOTSPOT
You have the following dataset.
You plan to use the dataset to train a model that will predict the house price categories of houses.
What are Household Income and House Price Category? To answer, select the appropriate option in the answer area. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: A feature
Box 2: A label
Topic 3, Describe features of computer vision workloads on Azure
You need to develop a mobile app for employees to scan and store their expenses while travelling.
Which type of computer vision should you use?
- A . semantic segmentation
- B . image classification
- C . object detection
- D . optical character recognition (OCR)
D
Explanation:
Azure’s Computer Vision API includes Optical Character Recognition (OCR) capabilities that extract printed or handwritten text from images. You can extract text from images, such as photos of license plates or containers with serial numbers, as well as from documents – invoices, bills, financial reports, articles, and more.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-recognizing-text
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Explanation:
Azure Custom Vision is a cognitive service that lets you build, deploy, and improve your own image classifiers. An image classifier is an AI service that applies labels (which represent classes) to images, according to their visual characteristics. Unlike the Computer Vision service, Custom Vision allows you to specify the labels to apply.
Note: The Custom Vision service uses a machine learning algorithm to apply labels to images. You, the developer, must submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then the algorithm trains to this data and calculates its own accuracy by testing itself on those same images. Once the algorithm is trained, you can test, retrain, and eventually use it to classify new images according to the needs of your app. You can also export the model itself for offline use.
DRAG DROP
Match the types of computer vision to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Facial recognition
Face detection that perceives faces and attributes in an image; person identification that matches an individual in your private repository of up to 1 million people; perceived emotion recognition that detects a range of facial expressions like happiness, contempt, neutrality, and fear; and recognition and grouping of similar faces in images.
Box 2: OCR
Box 3: Objection detection
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can’t be localized with bounding boxes.
DRAG DROP
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each machine learning type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.
Explanation:
Graphical user interface, text, application
Description automatically generated
Box 1: Image classification
Image classification is a supervised learning problem: define a set of target classes (objects to identify in images), and train a model to recognize them using labeled example photos.
Box 2: Object detection
Object detection is a computer vision problem. While closely related to image classification, object detection performs image classification at a more granular scale. Object detection both locates and categorizes entities within images.
Box 3: Semantic Segmentation
Semantic segmentation achieves fine-grained inference by making dense predictions inferring labels for every pixel, so that each pixel is labeled with the class of its enclosing object ore region.
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Explanation:
A screenshot of a computer
Description automatically generated with medium confidence
Box 1: Yes
Custom Vision functionality can be divided into two features. Image classification applies one or more labels to an image. Object detection is similar, but it also returns the coordinates in the image where the applied label(s) can be found.
Box 2: Yes
The Custom Vision service uses a machine learning algorithm to analyze images. You, the developer, submit groups of images that feature and lack the characteristics in question. You label the images yourself at the time of submission. Then, the algorithm trains to this data and calculates its own accuracy by testing itself on those same images.
Box 3: No
Custom Vision service can be used only on graphic files.
HOTSPOT
You have a database that contains a list of employees and their photos.
You are tagging new photos of the employees.
For each of the following statements select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
DRAG DROP
Match the facial recognition tasks to the appropriate questions.
To answer, drag the appropriate task from the column on the left to its question on the right. Each task may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: verification
Face verification: Check the likelihood that two faces belong to the same person and receive a confidence score.
Box 2: similarity
Box 3: Grouping
Box 4: identification
Face detection: Detect one or more human faces along with attributes such as: age, emotion, pose, smile, and facial hair, including 27 landmarks for each face in the image.
You need to determine the location of cars in an image so that you can estimate the distance between the cars.
Which type of computer vision should you use?
- A . optical character recognition (OCR)
- B . object detection
- C . image classification
- D . face detection
B
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can’t be localized with bounding boxes.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
You send an image to a Computer Vision API and receive back the annotated image shown in the exhibit.
Which type of computer vision was used?
- A . object detection
- B . semantic segmentation
- C . optical character recognition (OCR)
- D . image classification
A
Explanation:
Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found. For example, if an image contains a dog, cat and person, the Detect operation will list those objects together with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same tag in an image.
The Detect API applies tags based on the objects or living things identified in the image. There is currently no formal relationship between the tagging taxonomy and the object detection taxonomy. At a conceptual level, the Detect API only finds objects and living things, while the Tag API can also include contextual terms like "indoor", which can’t be localized with bounding boxes.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection
What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
- A . Predict stock prices.
- B . Detect brands in an image.
- C . Detect the color scheme in an image
- D . Translate text between languages.
- E . Extract key phrases.
In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
- A . Extract the invoice number from an invoice.
- B . Translate a form from French to English.
- C . Find image of product in a catalog.
- D . Identity the retailer from a receipt.
A,D
Explanation:
Reference: https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/#features
What is a use case for classification?
- A . predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night.
- B . analyzing the contents of images and grouping images that have similar colors
- C . predicting whether someone uses a bicycle to travel to work based on the distance from home to work
- D . predicting how many minutes it will take someone to run a race based on past race times
Your company wants to build a recycling machine for bottles. The recycling machine must automatically identify bottles of the correct shape and reject all other items.
Which type of AI workload should the company use?
- A . anomaly detection
- B . conversational AI
- C . computer vision
- D . natural language processing
C
Explanation:
Azure’s Computer Vision service gives you access to advanced algorithms that process images and return information based on the visual features you’re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview
You need to build an image tagging solution for social media that tags images of your friends automatically.
Which Azure Cognitive Services service should you use?
- A . Computer Vision
- B . Face
- C . Text Analytics
- D . Form Recognizer
What are two tasks that can be performed by using the Computer Vision service? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
- A . Train a custom image classification model.
- B . Detect faces in an image.
- C . Recognize handwritten text.
- D . Translate the text in an image between languages.
B,C
Explanation:
B: Azure’s Computer Vision service provides developers with access to advanced algorithms that process images and return information based on the visual features you’re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.
C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to extract printed and handwritten text from images and documents.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home
Detect faces in an image -Face API Microsoft Azure provides multiple cognitive services that you can use to detect and analyze faces, including:
Computer Vision, which offers face detection and some basic face analysis, such as determining age.
Video Indexer, which you can use to detect and identify faces in a video.
Face, which offers pre-built algorithms that can detect, recognize, and analyze faces.
Recognize hand written text -Read API
The Read API is a better option for scanned documents that have a lot of text. The Read API also has the ability to automatically determine the proper recognition model
You are processing photos of runners in a race.
You need to read the numbers on the runners’ shirts to identity the runners in the photos.
Which type of computer vision should you use?
- A . facial recognition
- B . optical character recognition (OCR)
- C . semantic segmentation
- D . object detection
B
Explanation:
Optical character recognition (OCR) allows you to extract printed or handwritten text from images and documents.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview-ocr
Topic 4, Describe features of Natural Language Processing (NLP) workloads on Azure
You are developing a chatbot solution in Azure.
Which service should you use to determine a user’s intent?
- A . Translator Text
- B . QnA Maker
- C . Speech
- D . Language Understanding (LUIS)
D
Explanation:
Language Understanding (LUIS) is a cloud-based API service that applies custom machine-learning intelligence to a user’s conversational, natural language text to predict overall meaning, and pull out relevant, detailed information.
Design your LUIS model with categories of user intentions called intents. Each intent needs examples of user utterances. Each utterance can provide data that needs to be extracted with machine-learning entities.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis
You have insurance claim reports that are stored as text.
You need to extract key terms from the reports to generate summaries.
Which type of Al workload should you use?
- A . conversational Al
- B . anomaly detection
- C . natural language processing
- D . computer vision
C
Explanation:
Key phrase extractionis the concept of evaluating the text of a document, or documents, and then identifying the main talking points of the document(s).
Key phase extraction is a part of Text Analytics. The Text Analytics service is a part of the Azure Cognitive Services offerings that can performadvanced natural language processingover raw text.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-
get-started-azure
Which AI service can you use to interpret the meaning of a user input such as “Call me back later?”
- A . Translator Text
- B . Text Analytics
- C . Speech
- D . Language Understanding (LUIS)
D
Explanation:
https://docs.microsoft.com/en-us/azure/cognitive-services/luis/what-is-luis
You use natural language processing to process text from a Microsoft news story.
You receive the output shown in the following exhibit.
Which type of natural languages processing was performed?
- A . entity recognition
- B . key phrase extraction
- C . sentiment analysis
- D . translation
A
Explanation:
https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. You can provide the Text Analytics service with unstructured text and it will return a list of entities in the text that it recognizes. The service can also provide links to more information about that entity on the web. An entity is essentially an item of a particular type or a category; and in some cases, subtype, such as those as shown in the following table.
https://docs.microsoft.com/en-us/learn/modules/analyze-text-with-text-analytics-service/2-get-started-azure
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
You are developing a natural language processing solution in Azure. The solution will analyze customer reviews and determine how positive or negative each review is.
This is an example of which type of natural language processing workload?
- A . language detection
- B . sentiment analysis
- C . key phrase extraction
- D . entity recognition
B
Explanation:
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
You build a QnA Maker bot by using a frequently asked questions (FAQ) page.
You need to add professional greetings and other responses to make the bot more user friendly.
What should you do?
- A . Increase the confidence threshold of responses
- B . Enable active learning
- C . Create multi-turn questions
- D . Add chit-chat
D
Explanation:
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/qnamaker/how-to/chit-chat-knowledge-base?tabs=v1
You are building a Language Understanding model for an e-commerce business.
You need to ensure that the model detects when utterances are outside the intended scope of the model.
What should you do?
- A . Test the model by using new utterances
- B . Add utterances to the None intent
- C . Create a prebuilt task entity
- D . Create a new model
B
Explanation:
The None intent is filled with utterances that are outside of your domain.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/LUIS/luis-concept-intent
DRAG DROP
Match the types of natural languages processing workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.
Explanation:
Box 1: Entity recognition
Classify a broad range of entities in text, such as people, places, organisations, date/time and percentages, using named entity recognition. Whereas: – Get a list of relevant phrases that best describe the subject of each record using key phrase extraction.
Box 2: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 3: Translation
Using Microsoft’s Translator text API
This versatile API from Microsoft can be used for the following:
Translate text from one language to another.
Transliterate text from one script to another.
Detecting language of the input text.
Find alternate translations to specific text.
Determine the sentence length.
HOTSPOT
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Explanation:
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language detection.
Box 1: Yes
You can detect which language the input text is written in and report a single language code for every document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural languages. The language code is paired with a score indicating the strength of the score.
Box 2: No
Box 3: Yes
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
DRAG DROP
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be used once, more than once, or not at all. NOTE: Each correct selection is worth one point.
Explanation:
Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in text, including key
Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information on the web.
You are developing a solution that uses the Text Analytics service.
You need to identify the main talking points in a collection of documents.
Which type of natural language processing should you use?
- A . entity recognition
- B . key phrase extraction
- C . sentiment analysis
- D . language detection
B
Explanation:
Broad entity extraction: Identify important concepts in text, including key
Key phrase extraction/ Broad entity extraction: Identify important concepts in text, including key phrases and named entities such as people, places, and organizations.
Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing