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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?

A) Reduce the number of layers in the model to decrease computation time.
B) Enable mixed precision training to improve computational efficiency.
C) Increase the batch size to fully utilize available GPU memory and reduce per-sample processing overhead.
D) Use DLProf's Tensor Core Analysis to check if the model is leveraging Tensor Cores effectively.

2. A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU- accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.
What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

A) Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.
B) Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.
C) Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.
D) Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.

3. You are working on a structured dataset of around 10GB and need to perform exploratory data analysis (EDA), feature engineering, and filtering operations efficiently using NVIDIA technologies. The dataset fits into a single GPU's memory.
Which data processing library should you use to achieve the best performance?

A) Spark with RAPIDS Accelerator
B) cuDF
C) pandas
D) Dask DataFrame with Dask-CUDA

4. You are analyzing a dataset containing millions of transaction records to detect fraudulent transactions. The dataset is highly imbalanced, with fraudulent cases making up only 0.5% of the data.
Which data analysis strategy will most effectively handle the imbalance while minimizing the risk of overfitting?

A) Stratified random sampling
B) Random oversampling of the minority class
C) SMOTE (Synthetic Minority Over-sampling Technique)
D) Random undersampling of the majority class

5. A machine learning engineer is working with a financial dataset that contains multiple numerical features, including income, loan amount, and transaction frequency. Some features are normally distributed, while others have a highly skewed distribution with extreme outliers.
Which of the following approaches best ensures uniformity across features before training a model?

A) Use one-hot encoding to transform numerical features into categorical representations
B) Apply log transformation to skewed features before standardizing them with z-score normalization
C) Remove outliers before applying standardization
D) Scale all numerical features using min-max normalization

Solutions: