IBM Watson Data Scientist v1 : C1000-154

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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

1. The first step in performing exploratory data analysis (EDA) typically involves:

A) Connecting to as many data sources as possible
B) Selecting a random sample of data to analyze
C) Determining the hypothesis for the analysis
D) Choosing a color palette for data visualization

2. Which of the following is a common issue identified during the preprocessing of data?

A) Overly detailed documentation
B) Presence of missing values
C) Excessively large file names
D) Aesthetically unpleasing charts

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

4. What is a key advantage of using supervised learning techniques over unsupervised learning techniques?

A) Supervised learning is more effective for discovering hidden patterns in data without prior labeling.
B) Supervised learning is typically used for prediction with known outcomes, providing clear metrics for model performance.
C) Supervised learning can work without any labeled data.
D) Supervised learning algorithms can automatically label data.

5. Which feature is NOT available when managing models with Watson Machine Learning?

A) Rollback capabilities for model versions
B) Version control of deployed models
C) Real-time performance monitoring
D) Automatic conversion of all models to deep learning models

Solutions: