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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

Question 1

Which metric would be most appropriate for evaluating a model in a highly imbalanced classification problem?

A. Accuracy
B. F1-score
C. Precision
D. Recall

Question 2

In the case of imbalanced data, what technique is recommended to ensure that the train and test sets have similar distributions of the target variable?

A. Using only the majority class for splitting
B. Stratified split
C. Random split without considering the target variable
D. Splitting based on the order of data collection

Question 3

In the context of building models, why is it important to select a tool based on algorithm requirements and expertise?

A. It is legally required to use only certain tools for specific types of data.
B. Tools with the most features should always be selected to ensure model complexity.
C. Selecting a tool that matches the team's expertise ensures more efficient model development and troubleshooting.
D. All machine learning tools are essentially the same, making the selection process trivial.

Question 4

In defining a business problem, what is essential to align with the stakeholders?

A. Data sources
B. Project milestones
C. Business objectives
D. Technical requirements

Question 5

When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?

A. The popularity of the algorithm in recent academic papers.
B. Compatibility of the algorithm with the data characteristics and the predictive task.
C. Choosing algorithms that are only based on supervised learning.
D. The algorithm that requires the least amount of data preprocessing.

Solutions: