IBM C1000-154 IBM Watson Data Scientist v1 Online Training
IBM C1000-154 Online Training
The questions for C1000-154 were last updated at Feb 20,2025.
- Exam Code: C1000-154
- Exam Name: IBM Watson Data Scientist v1
- Certification Provider: IBM
- Latest update: Feb 20,2025
When helping businesses articulate and define problems, what is an essential first step?
- A . Identifying potential data sources
- B . Defining key performance indicators (KPIs)
- C . Establishing a clear problem statement
- D . Selecting the analytical techniques
An E-retailer uses several important data sources, including web logs which contain all of the information on how customers navigate the web site. There are non-informative entries in the web logs that need to be removed.
During which phase should these non-informative entries be removed in the CRISP-DM model?
- A . Modeling
- B . Data Preparation
- C . Data Understanding
- D . Business Understanding
What is a key disadvantage of using Grid Search for hyperparameter tuning?
- A . It is too quick and may miss out on evaluating some hyperparameters
- B . It requires no prior knowledge of the hyperparameters
- C . It can be computationally expensive and time-consuming due to its exhaustive nature
- D . It is unable to handle discrete parameters
Which method is used for merging records in SPSS Modeler Merge node that allows specifying a requirement to be satisfied in order for the merge to take place?
- A . Key
- B . Order
- C . Filter
- D . Condition
Why is it important to create data splits that are reproducible?
- A . To ensure that each model run can be exactly replicated for verification and comparison
- B . To guarantee that the model will perform with 100% accuracy on unseen data
- C . To use more data for testing than for training
- D . To allow for larger test sets for more comprehensive testing
In unsupervised learning, which algorithm is best suited for grouping customers based on their purchase history to target marketing efforts more effectively?
- A . Support Vector Machines
- B . K-Means Clustering
- C . Linear Regression
- D . Decision Trees
In the context of avoiding underfitting and overfitting, what role does splitting the data into training, testing, and validation sets play?
- A . It ensures that the model is trained on the maximum amount of data possible
- B . It allows for the model to be validated and tested on different subsets of data to check its generalization ability
- C . It guarantees that the model will perform with 100% accuracy on unseen data
- D . It increases the computational complexity without improving model performance
Which of the following is true about the AUC measure in the context of classification models?
- A . It represents the degree of separability between classes.
- B . It is less useful when the classes are highly imbalanced.
- C . It indicates the number of false positives.
- D . It measures the model’s accuracy using a single threshold.
What is the primary purpose of partitioning data into training and test sets?
- A . To ensure that the model gets exposed to all possible data scenarios during training
- B . To maximize the accuracy of the model by using all data for training
- C . To evaluate the model’s performance on unseen data
- D . To increase the computational efficiency of model training
Which analytic technique is NOT typically used to address business requirements?
- A . Regression analysis
- B . Clustering
- C . Decision trees
- D . Proofreading
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