Dataset augmentation and integration: Practice Questions — Data Science Pipelines and Workflow Automation (NVIDIA-Certified Associate: Accelerated Data Science)
Practice Questions: Dataset Augmentation and Integration This set of multiple-choice questions is designed to help you prepare for the Dataset...
Practice Questions: Dataset Augmentation and Integration
This set of multiple-choice questions is designed to help you prepare for the Dataset Augmentation and Integration portion of the NVIDIA-Certified Associate: Accelerated Data Science exam. Each question focuses on key concepts related to augmenting and integrating datasets effectively within GPU-accelerated data science pipelines.
Which of the following is not a common technique used for dataset augmentation in tabular data?
- A. Synthetic data generation using SMOTE
- B. Random cropping of images
- C. Adding Gaussian noise to features
- D. Feature swapping between samples
Correct answer: B
Explanation: Random cropping is an image augmentation technique and is not applicable to tabular data. The other options are common methods to augment tabular datasets.
When integrating multiple datasets with differing feature scales, what is a crucial preprocessing step?
- A. Feature scaling or normalization
- B. Removing all missing values
- C. Increasing dataset size by duplication
- D. Applying one-hot encoding only to numerical features
Correct answer: A
Explanation: Feature scaling or normalization ensures that features from different datasets are on comparable scales, which is essential for many machine learning algorithms.
Which RAPIDS library is primarily used to build scalable, GPU-accelerated data pipelines for integration and transformation?
- A. cuDF
- B. Dask-cuDF
- C. cuML
- D. cuGraph
Correct answer: B
Explanation: Dask-cuDF extends cuDF with Dask’s parallel computing capabilities, enabling scalable distributed data processing and integration on GPUs.
What is the main benefit of dataset augmentation in the context of mitigating overfitting?
- A. It reduces the size of the training dataset
- B. It increases model complexity
- C. It artificially increases dataset diversity
- D. It eliminates the need for validation data
Correct answer: C
Explanation: Dataset augmentation increases the diversity of training samples, helping the model generalize better and reducing overfitting.
Which approach is best suited for integrating datasets with different schemas but overlapping information?
- A. Concatenating datasets without modification
- B. Feature engineering to create common keys for merging
- C. Ignoring schema differences and training separate models
- D. Removing all non-overlapping features
Correct answer: B
Explanation: Creating common keys or engineered features enables meaningful joins and integration of datasets with different schemas.
In the context of workflow automation with Dask, what is a key advantage when performing dataset augmentation?
- A. It allows augmentation to be performed sequentially on a single CPU core
- B. It enables distributed and parallel augmentation on large datasets
- C. It automatically selects the best augmentation technique
- D. It replaces the need for feature selection
Correct answer: B
Explanation: Dask facilitates distributed and parallel computation, which accelerates augmentation tasks on large-scale datasets using multiple GPUs or CPU cores.
Which of the following best describes dataset integration?
- A. Generating synthetic samples to increase dataset size
- B. Combining multiple datasets into a unified dataset for analysis
- C. Removing outliers from a dataset
- D. Applying feature scaling to a single dataset
Correct answer: B
Explanation: Dataset integration involves merging or joining multiple datasets to create a comprehensive dataset for model training or analysis.
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