NVIDIA-Certified-Professional Accelerated Data Science : NCP-ADS exam

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A) Convert categorical variables into int8 to optimize GPU memory usage.
B) Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.
C) Convert all numerical data to float64 for maximum precision in calculations.
D) Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.

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) Increase GPU clock speed manually to force higher processing power.
B) Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.
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 designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?

A) Using a different precision setting for each framework to maximize performance per framework's capabilities.
B) Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.
C) Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.
D) Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.

4. You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A) Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
B) Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
C) Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
D) Use dask.dataframe.repartition() to distribute data evenly across multiple 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) pandas
B) PySpark
C) Dask
D) cuDF

Solutions: