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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A machine learning engineer is working on an image classification problem where the dataset is small and lacks variability. To improve generalization, the engineer decides to augment the dataset using NVIDIA RAPIDS.
What is the best method to generate synthetic data efficiently while leveraging GPU acceleration?

A) Apply cuML.GaussianMixture() to generate new synthetic data points based on an estimated probability distribution.
B) Use cuDF with cudf.DataFrame.sample() to create new samples by randomly selecting existing rows.
C) Use cuML.PCA() to reduce dimensionality and create synthetic samples by reconstructing the data with added noise.
D) Use traditional CPU-based augmentation techniques like OpenCV to transform images and generate new data.

2. A team is processing large-scale tabular data using cuDF and cuML on NVIDIA GPUs but is facing performance degradation.
Which of the following techniques would be the most effective in identifying and resolving bottlenecks in the pipeline?

A) Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.
B) Increase GPU clock speed manually to force higher processing power.
C) Convert all datasets into pandas DataFrames for initial processing before moving to cuDF.
D) Reduce the dataset size to a smaller sample to speed up processing.

3. You are monitoring a GPU-accelerated ETL pipeline using RAPIDS cuDF and Dask-cuDF. You suspect that a bottleneck is causing the pipeline to slow down.
Which of the following methods is the most effective way to diagnose performance bottlenecks in your data processing pipeline?

A) Monitor CPU usage in the system to detect high CPU load that might indicate a bottleneck
B) Increase the batch size of data loading without checking GPU memory usage
C) Use print() statements in the code to manually track execution times of different operation
D) Use NVIDIA Nsight Systems to profile GPU utilization and identify potential kernel execution inefficiencies

4. You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A) Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.
B) Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.
C) Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.
D) Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.

5. You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.
Which of the following libraries is best suited for this task?

A) cuDF
B) pandas
C) PySpark
D) Dask

Solutions: