Learn with NVIDIA : NCP-ADS training material for 100% pass
Updated: Aug 21, 2026
No. of Questions: 303 Questions & Answers with Testing Engine
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NVIDIA NCP-ADS Exam Overview:
| Certification Vendor: | NVIDIA |
| Exam Name: | NVIDIA-Certified Professional: Accelerated Data Science |
| Exam Number: | NCP-ADS |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 60-70 |
| Available Languages: | Chinese, English |
| Related Certifications: | NVIDIA-Certified Associate: Accelerated Data Science (NCA-ADS) |
| Exam Price: | 1580 CNY (~$200 USD) |
| Exam Format: | Multiple-choice, Scenario-based multiple-choice |
| Exam Duration: | 120 minutes |
| Recommended Training: | Accelerating End-to-End Data Science Workflows (DLI) Fundamentals of Accelerated Data Science |
| Exam Registration: | NVIDIA Certification Support NVIDIA Training & Certification Portal |
| Sample Questions: | NVIDIA NCP-ADS Sample Questions |
| Exam Way: | Proctored exam (online or authorized test center depending on region) |
| Pre Condition: | 2–3 years of experience in accelerated data science, machine learning, and GPU computing |
| Official Syllabus URL: | https://www.nvidia.cn/training/certification/accelerated-data-science-professional/ |
NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| 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
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| MLOps | 19% | - Deployment and Monitoring
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. When utilizing GPU instances in a cloud environment for data science, which of the following are common considerations? (Select two)
A) GPU performance can be degraded by network latency in distributed training
B) GPUs are suitable for both training and inference tasks but not for preprocessing
C) Cost-effective GPU instances always provide the best performance
D) Cloud providers offer dedicated GPU instances for high-performance needs
2. A data scientist is processing a dataset that is too large to fit into the memory of a single GPU. They decide to use Dask with cuDF to leverage multiple GPUs for accelerated computation.
Which of the following approaches ensures efficient parallelism when working with dask_cudf?
A) Use Dask futures to asynchronously submit GPU tasks and collect results when completed.
B) Use a single-threaded scheduler to avoid contention when running on multiple GPUs.
C) Assign explicit chunks of data to each worker by manually dividing the dataset before passing it to Dask.
D) Convert the dataset into a Dask cuDF dataframe and use persist() to keep it in GPU memory.
3. You are working with a large dataset in a GPU-accelerated environment, and one of the columns, revenue, contains numeric values representing the annual revenue for companies. The revenue values are in the billions of dollars.
Which of the following is the most memory-efficient data type for the revenue column in a cuDF DataFrame?
A) df['revenue'] = df['revenue'].astype('float32')
B) df['revenue'] = df['revenue'].astype('int64')
C) df['revenue'] = df['revenue'].astype('float64')
D) df['revenue'] = df['revenue'].astype('uint32')
4. 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) Split the dataset into multiple files based on a logical partitioning key, such as a date column.
B) Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
C) Store the entire dataset in a single large file to minimize the number of files in the directory.
D) Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.
5. A team of data engineers is working on an Apache Spark-based distributed computing pipeline that leverages NVIDIA GPUs and RAPIDS. They notice that shuffle operations are causing significant slowdowns in performance.
Which optimization strategy should they implement to reduce shuffle impact?
A) Disable GPU memory caching to allow automatic CPU-based shuffle optimization.
B) Use Spark's default shuffle partitioning without any modification, as GPUs inherently optimize shuffle operations.
C) Store shuffle data in Apache Parquet format on disk for faster access and reduced memory overhead.
D) Use RAPIDS Spark-RAPIDS Plugin with GPU-accelerated caching to minimize redundant shuffle operations.
Solutions:
| Question # 1 Answer: A,D | Question # 2 Answer: D | Question # 3 Answer: C | Question # 4 Answer: A | Question # 5 Answer: D |
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