PDF Version DemoA NVIDIA credential carries real weight with employers, and the NVIDIA-Certified-Professional Accelerated Data Science exam is the step that earns it. Getcertkey makes that step shorter with 303 expert-prepared practice questions for the NCP-ADS exam.
NVIDIA NCP-ADS Exam Overview:
| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional: Accelerated Data Science |
| Exam Number: | NCP-ADS |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Available Languages: | English, Chinese |
| Exam Format: | Multiple-choice, Scenario-based multiple-choice |
| Related Certifications: | NVIDIA-Certified Associate: Accelerated Data Science (NCA-ADS) |
| Real Exam Qty: | 60-70 |
| Exam Price: | 1580 CNY (~$200 USD) |
| Recommended Training: | Fundamentals of Accelerated Data Science Accelerating End-to-End Data Science Workflows (DLI) |
| Exam Registration: | NVIDIA Training & Certification Portal |
| 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 |
|---|---|---|
| Topic 1: Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Topic 2: Machine Learning | 15% | - Model Development and Optimization
|
| Topic 3: GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Topic 4: Data Preparation | 17% | - Data Cleaning and Transformation
|
| Topic 5: Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Topic 6: MLOps | 19% | - Deployment and Monitoring
|
NVIDIA-Certified-Professional Accelerated Data Science Exam FAQ: What Candidates Ask Most
What is the NVIDIA NCP-ADS exam?
The NCP-ADS exam is the official NVIDIA exam behind the NVIDIA-Certified Professional: Accelerated Data Science (NCP-ADS) certification, validating the skills measured by the NVIDIA-Certified-Professional Accelerated Data Science credential. It sits at the Professional level of the NVIDIA certification program. It also connects to NVIDIA-Certified Associate: Accelerated Data Science (NCA-ADS), so the knowledge you build here carries over to those tracks as well.
How many questions are on the NCP-ADS exam, and how much time do I get?
The NCP-ADS exam contains 60-70 questions to be completed within 120 minutes. Before exam day, divide the available time by the question count to work out a comfortable per-question pace, and mark any item that eats into it so you can return later instead of getting stuck. Timed sessions in the Getcertkey test engines make that pacing automatic — run at least two full-length mock exams under the clock so time pressure never becomes the reason you drop points.
Are there any prerequisites for the NCP-ADS exam?
2–3 years of experience in accelerated data science, machine learning, and GPU computing Requirements can change when NVIDIA revises its certification program, so confirm the current eligibility rules on the official exam page before you register.
How do I register for the NCP-ADS exam?
You can book the NVIDIA-Certified-Professional Accelerated Data Science exam through the official registration channels below:
As for delivery, the exam is offered in the following format: Proctored exam (online or authorized test center depending on region). Choose the option that suits you best when you book your seat.
What official training is recommended for the NCP-ADS exam?
NVIDIA recommends the following training resources for the NVIDIA-Certified-Professional Accelerated Data Science exam:
Official courses build the foundation; the 303 practice questions from Getcertkey then show you how that knowledge is examined, so the two work best together.
Can I try the NCP-ADS practice questions before I buy?
Yes. Getcertkey provides a free NCP-ADS PDF demo so you can review the question style and answer quality before purchasing. Every purchase also includes 365 days of free updates — if NVIDIA revises the exam during that period, the updated material reaches you at no cost. Once the free-update year ends, you can extend your update service at a 50% discount.
What if I fail the NCP-ADS exam, and how is my order delivered?
Every NVIDIA-Certified-Professional Accelerated Data Science purchase on Getcertkey is covered by a 100% money-back guarantee with clear conditions: if you take the corresponding exam within 60 days of your purchase and do not pass, you can claim a full refund by submitting a scanned copy of your exam enrollment slip and your official score report as a PDF within two days of the exam date; claims are processed within seven days of submission. The guarantee does not apply to exams taken within three days of purchase, to material that was downloaded but never used in an exam attempt, or to free products and expired orders, and the candidate name must match the payer name. If you would rather not take a refund, you can instead exchange your purchase for two free exam preparation products of equal value and keep the update service on your original product.
Delivery is instant: your download is sent to your email within one minute of payment, with no limit on how many computers you may install the material on. If nothing arrives within two hours, check your spam folder and contact customer service for help.
What topics are covered in the NCP-ADS exam?
The NVIDIA-Certified-Professional Accelerated Data Science exam blueprint is organized into 6 domains. The first three are:
- MLOps — 19% of the exam
- Data Analysis — 14% of the exam
- Data Manipulation and Software Literacy — 19% of the exam
For the complete domain-by-domain breakdown, scroll up to the full exam topics outline above and use it to plan how you distribute your study time.
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Question #1
You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?
A. Categorical (grouping transactions into bins such as "low", "medium", "high").
B. Boolean (True for transactions above $100, False otherwise).
C. Floating-point (with precision to store cents or fractions of a cent).
D. Integer (rounding all transaction amounts to whole dollars).
Question #2
You are preparing a dataset for training a machine learning model using NVIDIA RAPIDS cuML. The dataset contains a feature representing timestamps in nanoseconds.
To optimize GPU performance while ensuring precision, which data type should you choose?
A. datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.
B. object - Allows flexibility in storing timestamps as strings for easier parsing.
C. int32 - Uses less memory and can store high-precision timestamps efficiently.
D. bool - Provides a highly efficient way to store timestamps as binary values.
Question #3
You are processing a large dataset using NVIDIA Dask-cuDF to distribute GPU-accelerated computation across multiple nodes. Users report inconsistent execution times, with some jobs taking significantly longer than expected.
Which of the following actions would best help diagnose the performance bottleneck?
A. Use Dask's dashboard and NVTX markers to analyze task execution times and GPU utilization.
B. Limit GPU memory usage to force more frequent spilling to disk and observe performance differences.
C. Switch to using Pandas with Dask to compare execution speed differences.
D. Reduce the number of Dask workers to minimize parallel execution overhead.
Question #4
After profiling a deep learning model using NVIDIA DLProf, you notice that a specific GEMM (General Matrix Multiplication) operation takes significantly longer than expected. The profiler output reveals that tensor cores are underutilized despite having an Ampere-based GPU with Tensor Cores enabled.
Which of the following actions is the MOST appropriate to improve performance?
A. Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.
B. Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.
C. Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.
D. Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
Question #5
Which of the following hardware components is most critical for accelerating the training of deep learning models?
A. Graphics Processing Unit (GPU)
B. Random Access Memory (RAM)
C. Central Processing Unit (CPU)
D. Solid State Drive (SSD)
Solutions:
| Question #1 Correct Answer: C | Question #2 Correct Answer: A | Question #3 Correct Answer: A | Question #4 Correct Answer: D | Question #5 Correct Answer: A |
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