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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| Machine Learning | 15% | - Model Development and Optimization
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| MLOps | 19% | - Deployment and Monitoring
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You need to deploy a containerized machine learning model that utilizes NVIDIA GPUs on a cloud- based Kubernetes cluster.
Which of the following steps is essential for ensuring proper GPU utilization inside a Docker container?
- A. Run the container using the default Docker runtime without any additional configurations
- B. Use the NVIDIA Container Toolkit (nvidia-docker2) to allow GPU access within Docker containers
- C. Install GPU drivers inside the container to ensure access to the host's hardware
- D. Use a standard Python-based container image instead of an NVIDIA GPU-optimized image
Correct Answer: B 🗳️
Which of the following is the best approach for performing benchmarking and optimizing GPU- accelerated workflows for MLOps using Nvidia technologies?
- A. Use Nvidia's nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.
- B. Rely exclusively on the nvidia-smi tool for monitoring GPU utilization and memory usage across multiple GPUs without making any other performance adjustments.
- C. Use Nvidia's nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.
- D. Use TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.
Correct Answer: C 🗳️
You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations.
You need to monitor the pipeline's performance to identify bottlenecks.
Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)
- A. NVIDIA TensorRT Profiling
- B. NVIDIA DLA (Deep Learning Accelerator)
- C. NVIDIA Nsight Systems
- D. NVIDIA nvidia-smi
- E. NVIDIA CUDA Profiler (nvprof)
Correct Answer: C,E 🗳️
A data scientist is using RAPIDS cuML to build a predictive model on a large dataset containing numerical and categorical features.
To optimize feature engineering for accelerated GPU processing, which of the following is the best approach?
- A. Normalize numerical features using min-max scaling implemented with cuDF, leveraging GPU acceleration.
- B. Convert all categorical variables to string format for efficient processing in cuDF.
- C. Convert all numerical features to float64 to maximize precision for feature transformation.
- D. Apply one-hot encoding on high-cardinality categorical features directly in cuDF without any transformation.
Correct Answer: A 🗳️
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. Focus only on processing speed without considering resource consumption differences between frameworks.
- 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. Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.
Correct Answer: D 🗳️
