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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. In model lifecycle management, what is a key consideration when deploying models with Watson Machine Learning?

A) Deploying all models simultaneously regardless of use case
B) The ability to update or retire models based on performance metrics
C) Avoiding the use of APIs for integration with applications
D) Ensuring there is no logging or monitoring of model performance

2. Why is it important to create data splits that are reproducible?

A) To use more data for testing than for training
B) To ensure that each model run can be exactly replicated for verification and comparison
C) To guarantee that the model will perform with 100% accuracy on unseen data
D) To allow for larger test sets for more comprehensive testing

3. Given the Confusion matrix below, which is the formula for specificity?

A) TP/(FN + TP)
B) TN/(TN + FP)
C) TP/(FP + TP)
D) (TP + TN)/(FN + FP + TN + TP)

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

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

5. The process of aligning on user intents for a solution involves:

A) Understanding the business model in depth
B) Determining the technical feasibility exclusively
C) Identifying and understanding the needs and goals of end-users
D) Focusing on the data management strategies

Solutions: