IBM Certification C1000-154

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IBM Watson Data Scientist v1 Sample Questions:

1. What does the term "complexity" in model comparison refer to?

A) The amount of computational resources required for training and inference
B) The number of hyperparameters that need to be tuned
C) The size of the dataset the model can handle
D) The aesthetic appeal of the model's graphical representations

2. What is the primary purpose of hyperparameter tuning in machine learning models?

A) To adjust the model's complexity to improve its performance on unseen data
B) To increase the number of features in the dataset automatically
C) To reduce the training time of the model to an absolute minimum
D) To ensure the model uses all available computational resources

3. Which of the following is true about the AUC measure in the context of classification models?

A) It represents the degree of separability between classes.
B) It measures the model's accuracy using a single threshold.
C) It is less useful when the classes are highly imbalanced.
D) It indicates the number of false positives.

4. Which method is NOT traditionally used for collecting data?

A) Using Python APIs for external data
B) Scraping data from a webpage
C) Predicting future trends with machine learning models
D) Using SQL to fetch data from a data warehouse

5. 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 performance of the model on validation data
D) The color scheme of the model's output visualizations

Solutions: