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

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1
A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

A. Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.
B. Limit the benchmark to small datasets since GPUs excel at parallel processing.
C. Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.
D. Focus only on processing speed without considering resource consumption differences between frameworks.

Question #2
A machine learning engineer is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?

A. Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually
B. Transfer data between GPUs using NumPy's .copy() function
C. Use Dask's single-threaded scheduler to avoid multi-threading overhead
D. Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster

Question #3
A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

A. Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
B. Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
C. Implement ETL processes using only SQL-based transformations within a relational database system.
D. Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.

Question #4
You are a data scientist working on a large-scale deep learning project that requires significant computational resources. You have the option to run your workloads on a cloud-based GPU instance.
Which of the following statements best describes a key benefit of using cloud-based GPUs for your workload?

A. Cloud-based GPUs provide consistent and predictable performance, identical to on-premise dedicated GPUs.
B. Cloud-based GPUs eliminate all data transfer bottlenecks and latencies when training models on large datasets.
C. Cloud-based GPUs enable scalable resource allocation, allowing you to dynamically increase or decrease GPU instances as needed.
D. Cloud-based GPUs are always more cost-effective than on-premise GPUs, regardless of workload size and duration.

Question #5
You are processing a large-scale transportation network graph using NVIDIA cuGraph. The graph is extremely large, consuming almost all available GPU memory. Performance is deteriorating, and some computations fail due to memory exhaustion.
What is the best approach to efficiently handle this large graph while keeping computations on the GPU?

A. Store the graph as a large Python dictionary and use cuGraph only for specific queries.
B. Use cuGraph's multi-GPU support via Dask-cuGraph to distribute the graph across multiple GPUs.
C. Convert the graph into a NetworkX graph and process it on the CPU to reduce GPU memory usage.
D. Manually split the graph into chunks and process each chunk separately without any coordination.

Solutions: