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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?

A) Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.
B) Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
C) Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.
D) Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.

2. A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

A) Perform feature engineering on CPUs using pandas before transferring data to the GPU for training.
B) Use cuDF to perform feature transformations like normalization and one-hot encoding directly on the GPU.
C) Convert all categorical variables into string representations to preserve their original format for later analysis.
D) Store all numerical features in float64 format to prevent rounding errors during transformations.

3. A company is processing large log files from a cloud application, accumulating over 5TB of data daily. The data processing pipeline must be GPU-accelerated to extract insights quickly.
Which of the following is the most effective approach to handle high-volume log processing using NVIDIA technologies?

A) Use cuDF with explicit memory management to load and process the entire dataset into a single GPU.
B) Store logs as Pandas DataFrames and use multiprocessing to parallelize operations across CPU cores.
C) Use RAPIDS cuML for performing log file processing, taking advantage of its optimized ML algorithms.
D) Leverage Dask-cuDF to distribute the dataset across multiple GPUs, ensuring efficient parallel processing.

4. You are training a deep learning model on a large dataset and are deciding whether to use a single GPU or multiple GPUs.
Which of the following are true considerations when comparing single-GPU and multi-GPU training setups? (Select two)

A) Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.
B) Single-GPU training is limited by the VRAM (video memory) on the GPU, so larger models or datasets may require multi-GPU setups.
C) Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.
D) Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
E) Multi-GPU setups perform better only when the batch size is reduced.

5. You are working with a large dataset containing numeric and categorical features, which will be processed using NVIDIA RAPIDS cuDF for accelerated analytics.
To optimize performance while minimizing memory usage, which data type is the most appropriate for storing a categorical variable with a small number of unique values?

A) bool - Minimizes memory usage and supports efficient operations for categorical data.
B) int64 - Provides high precision and avoids potential overflow.
C) category - Optimizes storage and computation for categorical data in cuDF.
D) float32 - Reduces memory consumption compared to float64 while maintaining precision.

Solutions: