IBM C1000-154

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IBM Watson Data Scientist v1 Sample Questions:

1. Which search algorithm is known for its exhaustive search over a specified parameter space for hyperparameter tuning?

A) Sequential Search
B) Binary Search
C) Random Search
D) Grid Search

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

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

3. Selecting the right model for a data science project depends on:

A) The size of the dataset only
B) The preference of the data scientist
C) The project's budget only
D) The type of data and the problem to be solved

4. In IBM Garage Methodology, the 'Minimum Viable Product' (MVP) concept is crucial for:

A) Waiting for all possible features to be developed before release
B) Testing hypotheses with the smallest investment of time and resources
C) Extending the timeline of the project indefinitely
D) Maximizing the budget before the product launch

5. 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) Order
B) Filter
C) Condition
D) Key

Solutions: