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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

Question 1

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

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

Question 2

Which statement best differentiates machine learning from deep learning?

A. Deep learning algorithms require less data to learn.
B. Machine learning algorithms perform better on structured data, while deep learning excels with unstructured data like images and text.
C. Machine learning models are always transparent, whereas deep learning models cannot be interpreted.
D. Deep learning algorithms are a subset of machine learning algorithms that do not require feature engineering.

Question 3

When comparing models to choose the best one, which factor is least likely to be considered?

A. The complexity of the model
B. The explainability of the model's predictions
C. The color scheme of the model's output visualizations
D. The performance of the model on validation data

Question 4

What is a critical consideration when selecting the right model class for a given problem?

A. The nature of the problem (e.g., classification, regression) and the characteristics of the data.
B. The theoretical complexity of the model, with more complex models always being preferred.
C. The availability of high-performance computing resources.
D. The model's ability to produce results quickly, regardless of accuracy.

Question 5

In unsupervised learning, which algorithm is best suited for grouping customers based on their purchase history to target marketing efforts more effectively?

A. Support Vector Machines
B. K-Means Clustering
C. Decision Trees
D. Linear Regression

Solutions: