NCP-ADS Practice Questions - NVIDIA-Certified Professional Accelerated Data Science Exam - NCP-ADS Practice Questions - NVIDIA-Certified Professional Accelerated Data Science Exam

Question 1

What is RAPIDS and what primary problem does it solve for data science workflows?

RAPIDS is an open-source GPU-accelerated data science library suite that runs pandas, scikit-learn, and graph analytics workloads on NVIDIA GPUs, dramatically reducing execution time RAPIDS is a distributed computing framework for streaming data that replaces Apache Kafka RAPIDS is NVIDIA's cloud-based ML platform for managing model training experiments RAPIDS is a CPU-optimized version of scikit-learn designed for large cluster deployments

Correct Answer:

RAPIDS is an open-source GPU-accelerated data science library suite that runs pandas, scikit-learn, and graph analytics workloads on NVIDIA GPUs, dramatically reducing execution time

Question 2

Which Python API does cuDF mirror, making it easy to migrate existing data processing code to GPU?

pandas NumPy PySpark Dask

Correct Answer:

pandas

Question 3

What is cuML and which machine learning library does it mirror?

cuML is the GPU-accelerated machine learning library in RAPIDS that mirrors the scikit-learn API cuML is a CUDA wrapper around TensorFlow for deep learning workloads cuML is the RAPIDS library for graph analytics, mirroring NetworkX cuML is a GPU-accelerated version of XGBoost with a custom RAPIDS API

Correct Answer:

cuML is the GPU-accelerated machine learning library in RAPIDS that mirrors the scikit-learn API

Question 4

How does cuDF's `read_csv()` achieve higher throughput than pandas `read_csv()`?

cuDF uses GPU parallelism to parse and load CSV data into GPU memory using CUDA kernels, leveraging thousands of GPU cores for simultaneous data processing cuDF skips data validation and type inference to load data faster with fewer operations cuDF reads multiple CSV files simultaneously from different network drives cuDF pre-compiles CSV schema metadata from previous reads to skip parsing on subsequent loads

Correct Answer:

cuDF uses GPU parallelism to parse and load CSV data into GPU memory using CUDA kernels, leveraging thousands of GPU cores for simultaneous data processing

Question 5

What does `nvidia-smi` display when run on a system with a GPU running RAPIDS workloads?

GPU utilization percentage, VRAM usage, temperature, power draw, running processes, and driver/CUDA version The number of RAPIDS DataFrames currently in GPU memory and their sizes The model accuracy metrics for ML jobs running on the GPU The network bandwidth between the CPU and GPU via PCIe

Correct Answer:

GPU utilization percentage, VRAM usage, temperature, power draw, running processes, and driver/CUDA version

Question 1

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Prepare with the NCP-ADS Practice Questions - NVIDIA-Certified Professional Accelerated Data Science Exam practice quiz. This question bank includes 100 questions covering rapids, data, cuml, cudf, and science. Use it to review important concepts, identify knowledge gaps, and build confidence for the related exam, course, or assessment.

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NCP-ADS Practice Questions - NVIDIA-Certified Professional Accelerated Data Science Exam

This practice set contains 100 questions from the matching question bank and focuses on rapids, data, cuml, cudf, and science. Work through each question carefully, review the provided solutions, and revisit topics that need more study before your next attempt.

This is an independent study resource intended for practice and review; it is not an official examination or an endorsement by any organization named in the title.

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NCP-ADS Practice Questions - NVIDIA-Certified Professional Accelerated Data Science Exam

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