A00-406 Exam Training Practice Tests (SASInstitute SAS® Viya® Supervised Machine Learning Pipelines)]

Question 1

What does "data lineage" refer to in the context of data source management?

A. The history of data transformation processes B. The structure of a relational database C. The physical location of data storage D. The security protocols for data access

Correct Answer: A

Question 2

Which evaluation metric is commonly used for assessing the performance of a regression model?

A. Mean Absolute Error (MAE) B. Precision C. Confusion Matrix D. F1 Score

Correct Answer: A

Question 3

What is overfitting in machine learning, and how can it be addressed in a pipeline?

A. Overfitting occurs when the model fits the training data too closely and may not generalize well. It can be addressed by regularization techniques. B. Overfitting occurs when the model is too complex and overperforms. C. Overfitting occurs when the model is too simple and underperforms. D. Overfitting is not a concern in machine learning pipelines.

Correct Answer: A

Question 4

What is the main purpose of feature engineering in model building?

A. Model evaluation B. Creating new features or transforming existing ones to improve model performance C. Data preprocessing D. Data visualization

Correct Answer: B

Question 5

In machine learning, what does "overfitting" refer to?

A. A model that is undertrained and has high bias B. A model that has too much complexity and fits the training data too closely C. A model that performs well on new, unseen data D. A model that is unable to make predictions

Correct Answer: B

Question 6

What does the term "bagging" refer to in ensemble learning?

A. The process of combining multiple identical models to reduce variance B. A type of feature extraction C. A form of dimensionality reduction D. A technique that reduces model complexity

Correct Answer: A

Question 7

What is the purpose of a "canary release" in the context of model deployment?

A. To assess data quality B. To deploy a new model version to a small subset of users or systems for testing C. To create synthetic data D. To evaluate model accuracy

Correct Answer: B

Question 8

What does "feature selection" refer to in the context of model building?

A. The visualization of data distribution B. The process of choosing the most relevant variables (features) for the model C. The creation of synthetic features from existing data D. The evaluation of model accuracy

Correct Answer: B

Question 9

When deploying a machine learning model, what is "model drift"?

A. The process of feature extraction B. A sudden increase in the model's accuracy C. A measure of feature importance D. A change in the distribution of the input data or target variable over time

Correct Answer: D

Question 10

Which of the following metrics is commonly used to evaluate the performance of a binary classification model in a machine learning pipeline?

A. Accuracy B. Mean Absolute Error (MAE) C. Root Mean Squared Error (RMSE) D. R-squared

Correct Answer: A

SASInstitute SAS® Viya® Supervised Machine Learning Pipelines - A00-406 Exam Practice Test