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IBM C1000-154 Exam Syllabus Topics:

IBM Watson Data Scientist v1 Sample Questions:

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

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

2. Which statement describes bagging?

A) Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.
B) Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.
C) Building models and using their output as features into a final model.
D) Building models in parallel and aggregating their predictions to select the final prediction.

3. A model's performance is not solely dependent on its accuracy but also on:

A) The number of features selected
B) The choice of programming language
C) The color of the visualization charts
D) Metrics like precision, recall, and F1 score

4. In the context of building models, why is it important to select a tool based on algorithm requirements and expertise?

A) It is legally required to use only certain tools for specific types of data.
B) Tools with the most features should always be selected to ensure model complexity.
C) All machine learning tools are essentially the same, making the selection process trivial.
D) Selecting a tool that matches the team's expertise ensures more efficient model development and troubleshooting.

5. In classification models, which of the following metrics is NOT directly derived from the confusion matrix?

A) Recall
B) F1-score
C) Precision
D) Mean Absolute Error (MAE)

Solutions: