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

2. Profiling and visualizing data using Watson tools primarily helps in:

A) Creating aesthetically pleasing presentations without regard to data relevance
B) Identifying patterns, outliers, and insights in the data
C) Increasing the quantity of data for analysis
D) Simplifying the data collection process without analyzing quality

3. Which of the following best describes when to use deep learning over traditional machine learning algorithms?

A) When working with high-dimensional data, such as images or natural language, where feature extraction is complex.
B) When the dataset is small and easily interpretable.
C) For simple tasks that require straightforward predictive modeling.
D) When computational resources are limited and model interpretability is not a concern.

4. Key metrics for a solution should be defined based on:

A) The personal preferences of the project stakeholders
B) The specific objectives and desired outcomes of the project
C) The most recent technological trends
D) The number of available data scientists

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

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

Solutions: