Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps | 19% | - Deployment and Monitoring
|
| Topic 2: Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Topic 3: GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Topic 4: Machine Learning | 15% | - Model Development and Optimization
|
| Topic 5: Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Topic 6: Data Preparation | 17% | - Data Cleaning and Transformation
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?
- A. Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
- B. Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
- C. Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
- D. Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution
Correct Answer: C 🗳️
You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?
- A. Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.
- B. Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.
- C. Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.
- D. Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.
Correct Answer: A 🗳️
Which of the following scenarios are most appropriate for using GPU acceleration when working with large-scale datasets in machine learning? (Select two)
- A. Running a large ensemble of simple models (e.g., random forests) on a dataset of 10 million rows.
- B. Performing exploratory data analysis (EDA) on a dataset of 100,000 rows with 10 features.
- C. Using decision trees for a classification task with a dataset of 1 million rows and 20 features.
- D. Training a large-scale natural language processing (NLP) model on text data with billions of words.
- E. Running a deep learning model for image classification with millions of labeled images.
Correct Answer: D,E 🗳️
You are optimizing a deep learning model that runs on an NVIDIA GPU and notice that inference latency is unexpectedly high. You decide to use DLProf to analyze the model's execution profile. After running the profiler, you find that a significant portion of execution time is spent on a single GPU kernel.
Which of the following actions would best help you identify and optimize this performance bottleneck?
- A. Modify the neural network architecture to use more convolutional layers, as this generally improves execution speed on NVIDIA GPUs.
- B. Reduce the batch size to minimize the time spent on memory-bound operations and improve kernel efficiency.
- C. Switch to a CPU-based execution environment, as it will eliminate any potential GPU bottlenecks.
- D. Use DLProf's Tensor Core Analysis feature to determine if Tensor Cores are being utilized effectively.
Correct Answer: D 🗳️
You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?
- A. Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.
- B. Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.
- C. Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.
- D. Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.
Correct Answer: A 🗳️
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