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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

Question 1

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

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

Question 2

In unsupervised learning, which algorithm is best suited for grouping customers based on their purchase history to target marketing efforts more effectively?

A. K-Means Clustering
B. Decision Trees
C. Linear Regression
D. Support Vector Machines

Question 3

Which analytic technique is NOT typically used to address business requirements?

A. Proofreading
B. Clustering
C. Decision trees
D. Regression analysis

Question 4

In the context of IBM Garage Methodology, which of the following best describes the "Enterprise Design Thinking" stage?

A. It is primarily concerned with the technical deployment of solutions.
B. It focuses on maintaining and operating solutions at scale.
C. It involves the rapid building of prototypes to validate ideas.
D. It emphasizes understanding user outcomes and business needs.

Question 5

What is the primary purpose of hyperparameter tuning in machine learning models?

A. To increase the number of features in the dataset automatically
B. To reduce the training time of the model to an absolute minimum
C. To ensure the model uses all available computational resources
D. To adjust the model's complexity to improve its performance on unseen data

Solutions: