Handling class imbalance and generating synthetic data: Practice Questions — Data Manipulation and Preparation (NVIDIA-Certified Associate: Accelerated Data Science)

Practice Questions: Handling Class Imbalance and Generating Synthetic Data This set of multiple-choice questions is designed to help candidates...

Practice Questions: Handling Class Imbalance and Generating Synthetic Data

This set of multiple-choice questions is designed to help candidates prepare for the NVIDIA-Certified Associate: Accelerated Data Science exam, focusing on the topic of handling class imbalance and generating synthetic data within data manipulation and preparation.

What is the primary purpose of generating synthetic data in the context of class imbalance?
- A. To reduce the size of the dataset for faster processing
- B. To increase the number of samples in the minority class to balance the dataset
- C. To remove outliers from the dataset
- D. To convert categorical variables into numerical ones
Correct answer: B
Explanation: Synthetic data generation is used to augment the minority class by creating new, artificial samples, which helps balance the dataset and improve model performance.
Which of the following techniques is commonly used for generating synthetic data to address class imbalance?
- A. Principal Component Analysis (PCA)
- B. SMOTE (Synthetic Minority Over-sampling Technique)
- C. Random Forest
- D. K-Means Clustering
Correct answer: B
Explanation: SMOTE is a popular technique that creates synthetic samples by interpolating between existing minority class samples.
When handling class imbalance, why is it important to avoid simply duplicating minority class samples?
- A. It increases the risk of overfitting the model
- B. It reduces the dataset size
- C. It changes the distribution of the majority class
- D. It decreases training time
Correct answer: A
Explanation: Duplicating samples can lead to overfitting because the model may learn to memorize repeated instances rather than generalizing well.
Which GPU-accelerated library can be used to efficiently implement synthetic data generation techniques like SMOTE?
- A. cuDF
- B. RAPIDS cuML
- C. Dask
- D. Apache Spark
Correct answer: B
Explanation: RAPIDS cuML provides GPU-accelerated machine learning algorithms, including implementations of synthetic data generation methods such as SMOTE.
What is a potential drawback of using synthetic data generation methods indiscriminately?
- A. Increased computational cost without improving model accuracy
- B. Loss of original data features
- C. Creation of noisy or unrealistic samples that may degrade model performance
- D. Reduction in dataset size
Correct answer: C
Explanation: Poorly generated synthetic samples can introduce noise or unrealistic data points, which may confuse the model and reduce its predictive accuracy.
Which approach can be combined with synthetic data generation to further improve model performance on imbalanced datasets?
- A. Dimensionality reduction
- B. Feature scaling
- C. Ensemble methods such as boosting or bagging
- D. Data shuffling
Correct answer: C
Explanation: Ensemble methods help improve robustness and accuracy, especially when combined with balanced datasets achieved through synthetic data generation.
In the context of class imbalance, what is the role of data sampling techniques?
- A. To generate new features from existing data
- B. To select a representative subset of data to balance classes
- C. To convert categorical data into numerical data
- D. To normalize numerical features
Correct answer: B
Explanation: Data sampling techniques such as undersampling or oversampling aim to balance the class distribution by selecting or augmenting samples.

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