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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. When helping businesses articulate and define problems, what is an essential first step?

A) Identifying potential data sources
B) Selecting the analytical techniques
C) Defining key performance indicators (KPIs)
D) Establishing a clear problem statement

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

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

3. Which of the following best exemplifies the use of the CRISP-DM methodology in a business context?

A) A business starting with data collection before understanding the problem
B) A company following a strict top-down approach for all decisions
C) A project manager focusing exclusively on deployment
D) A team iterating between different stages as needed based on project feedback

4. Which method is NOT traditionally used for collecting data?

A) Using Python APIs for external data
B) Using SQL to fetch data from a data warehouse
C) Predicting future trends with machine learning models
D) Scraping data from a webpage

5. 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) Random split without considering the target variable
C) Splitting based on the order of data collection
D) Stratified split

Solutions: