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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| GPU and Cloud Computing | 16% | - GPU architecture and fundamentals
|
| Machine Learning | 15% | - Deep learning frameworks integration
|
| MLOps | 19% | - Experiment tracking
|
| Data Preparation | 17% | - Data cleaning and quality handling
|
| Data Analysis | 14% | - Visualization
|
| Data Manipulation and Software Literacy | 19% | - Software literacy and development tools
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are working with a dataset in a cloud-based GPU environment that contains a column country representing the country of origin for customers. The column contains only 10 unique country values, but the dataset has millions of rows.
Which of the following is the most memory-efficient approach to handle the country column in a cuDF DataFrame?
- A. df['country'] = df['country'].astype('category')
- B. df['country'] = df['country'].astype('string')
- C. df['country'] = df['country'].astype('object')
- D. df['country'] = df['country'].astype('int32')
Correct Answer: A 🗳️
A company is processing large log files from a cloud application, accumulating over 5TB of data daily. The data processing pipeline must be GPU-accelerated to extract insights quickly.
Which of the following is the most effective approach to handle high-volume log processing using NVIDIA technologies?
- A. Leverage Dask-cuDF to distribute the dataset across multiple GPUs, ensuring efficient parallel processing.
- B. Store logs as Pandas DataFrames and use multiprocessing to parallelize operations across CPU cores.
- C. Use cuDF with explicit memory management to load and process the entire dataset into a single GPU.
- D. Use RAPIDS cuML for performing log file processing, taking advantage of its optimized ML algorithms.
Correct Answer: A 🗳️
A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
Which of the following approaches would best help identify the source of the bottleneck?
- A. Use nsys (NVIDIA Nsight Systems) to profile GPU kernel execution and memory transfer times.
- B. Disable GPU parallelism and run computations on the CPU to compare performance.
- C. Rely on nvidia-smi alone to check GPU utilization without considering other profiling tools.
- D. Increase the batch size of data being processed without analyzing memory utilization.
Correct Answer: A 🗳️
You are developing an accelerated ETL workflow that requires data transformations such as filtering, aggregating, and joining large datasets. You decide to leverage NVIDIA GPUs to accelerate the transformation phase of your ETL pipeline.
Which of the following approaches will provide the greatest performance improvements when working with large-scale tabular datasets?
- A. Performing transformations using SQL-based queries on CPU
- B. Using TensorFlow for data transformation tasks
- C. Relying on traditional pandas for in-memory transformations
- D. Using RAPIDS cuDF to perform transformations on a GPU
Correct Answer: D 🗳️
You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?
- A. Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
- B. Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.
- C. Store the entire dataset in a single large file to minimize the number of files in the directory.
- D. Split the dataset into multiple files based on a logical partitioning key, such as a date column.
Correct Answer: D 🗳️
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