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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

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

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

2. What is a key disadvantage of using Grid Search for hyperparameter tuning?

A) It is unable to handle discrete parameters
B) It is too quick and may miss out on evaluating some hyperparameters
C) It requires no prior knowledge of the hyperparameters
D) It can be computationally expensive and time-consuming due to its exhaustive nature

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

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

4. Cloud Pak for Data's integration with Spark allows users to:

A) Leverage distributed computing for processing large datasets efficiently
B) Use Spark exclusively for data visualization purposes
C) Avoid using any form of data processing or analysis
D) Perform complex computations on small datasets only

5. What is the primary purpose of partitioning data into training and test sets?

A) To evaluate the model's performance on unseen data
B) To increase the computational efficiency of model training
C) To ensure that the model gets exposed to all possible data scenarios during training
D) To maximize the accuracy of the model by using all data for training

Solutions: