IBM C1000-154 : IBM Watson Data Scientist v1

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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. How can data splits be made reproducible in a machine learning experiment?

A) By partitioning the data manually
B) By using a consistent random seed when splitting the data
C) By using a different random seed each time the data is split
D) By splitting the data in a sequential manner without randomization

2. In defining a business problem, what is essential to align with the stakeholders?

A) Project milestones
B) Business objectives
C) Data sources
D) Technical requirements

3. Understanding how to use libraries in Python within a deployment environment is essential for:

A) Deploying models that are incompatible with the deployment environment
B) Ensuring that all models are developed without any external libraries
C) Increasing the complexity and maintenance cost of the deployed solution
D) Leveraging specific functionalities for data analysis, manipulation, and model building

4. In the context of deployment environments, understanding resources is crucial.
What does this typically involve?

A) Choosing the most aesthetically pleasing user interface
B) Focusing exclusively on the cost of storage
C) Selecting the programming language with the least number of keywords
D) Determining the computational power and memory requirements for the deployed solution

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

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

Solutions: