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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. Which metric is commonly used to evaluate the performance of a regression model?

A) Precision
B) Accuracy
C) Mean Squared Error (MSE)
D) Recall

2. Which type of machine learning algorithm would be most appropriate for predicting house prices based on various features like location, size, and number of bedrooms?

A) Clustering
B) Regression
C) Classification
D) Dimensionality Reduction

3. How do you determine which tool to use based on algorithm requirements and expertise?

A) Always use the most complex tool to ensure the model's accuracy.
B) Choose the newest tools on the market for the most up-to-date features.
C) Consider the tool's compatibility with the algorithm requirements and the team's expertise.
D) Select tools that the team is already familiar with, even if they are not the best fit for the algorithm.

4. In Cognos Analytics, which two features distinguish stories from dashboards?

A) Stories convey a conclusion.
B) Stories automatically load different filters for different users.
C) Stories are text-based and do not contain visualizations.
D) Stories can be embedded in websites or documents.
E) Stories provide a narrative over time.

5. In the context of model selection, explainability refers to:

A) The complexity of the algorithm used to build the model.
B) The model's ability to operate without any data.
C) How colorful and visually appealing the model's output is.
D) The ease with which humans can understand how the model makes decisions.

Solutions: