Regression, classification, and clustering techniques: Practice Questions — Machine Learning With RAPIDS (NVIDIA-Certified Associate: Accelerated Data Science)

Practice Questions: Regression, Classification, and Clustering Techniques with RAPIDS

These multiple-choice questions are designed to help you prepare for the Machine Learning with RAPIDS section of the NVIDIA-Certified Associate: Accelerated Data Science exam. Focus is on regression, classification, and clustering techniques accelerated by GPU libraries such as cuML and XGBoost.

1. Which RAPIDS library is primarily used for GPU-accelerated regression and classification tasks?

   • A. cuDF
   • B. cuML
   • C. cuGraph
   • D. cuSignal

   Correct answer: B

   Explanation: cuML provides GPU-accelerated implementations of machine learning algorithms including regression and classification, making it the primary RAPIDS library for these tasks.

2. In a binary classification problem, which metric derived from the confusion matrix best measures the proportion of correctly predicted positive cases?

   • A. Accuracy
   • B. Precision
   • C. Recall
   • D. F1 Score

   Correct answer: B

   Explanation: Precision measures the proportion of true positives among all positive predictions, indicating the accuracy of positive predictions.

3. Which clustering technique available in RAPIDS cuML is best suited for discovering clusters of arbitrary shape?

   • A. K-Means
   • B. DBSCAN
   • C. Gaussian Mixture Model
   • D. Agglomerative Clustering

   Correct answer: B

   Explanation: DBSCAN identifies clusters based on density and can find arbitrarily shaped clusters, unlike K-Means which assumes spherical clusters.

4. When tuning hyperparameters for an XGBoost regression model in RAPIDS, which technique helps to estimate model generalization performance?

   • A. Cross-validation
   • B. Grid search without validation
   • C. Training on full dataset only
   • D. Early stopping without validation set

   Correct answer: A

   Explanation: Cross-validation partitions data to evaluate model performance on unseen data, providing a reliable estimate of generalization.

5. In regression tasks using RAPIDS cuML, which metric quantifies the average squared difference between predicted and actual values?

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

   Correct answer: C

   Explanation: MSE calculates the average of squared differences between predictions and true values, penalizing larger errors more heavily.

6. Which of the following is a key advantage of using GPU-accelerated XGBoost with RAPIDS for classification problems?

   • A. Supports only linear models
   • B. Enables faster training on large datasets
   • C. Does not support hyperparameter tuning
   • D. Requires CPU-only execution

   Correct answer: B

   Explanation: GPU acceleration in RAPIDS XGBoost significantly reduces training time on large datasets, improving efficiency for classification tasks.

7. Which method in RAPIDS cuML would you use to evaluate the quality of clusters formed by K-Means?

   • A. Silhouette Score
   • B. Confusion Matrix
   • C. Mean Squared Error
   • D. Precision

   Correct answer: A

   Explanation: The Silhouette Score measures how similar an object is to its own cluster compared to other clusters, assessing clustering quality.

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