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Updated: Oct 15, 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 |
| Available Languages: | English |
| Exam Format: | Multiple-choice |
| Related Certifications: | NVIDIA-Certified Professional: Accelerated Data Science |
| Exam Duration: | 120 minutes |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 60-70 |
| Exam Price: | $200 USD |
| Passing Score: | ~70% |
| Sample Questions: | NVIDIA NCP-ADS Sample Questions |
| Exam Way: | Online, remotely proctored |
| Pre Condition: | Two to three years of hands-on experience in accelerated data science. Strong foundation in machine learning and GPU-accelerated computing. Experience in GPU-based optimization strategies and accelerated data manipulation techniques. Deep understanding of end-to-end data science workflows, from data preparation and cleansing to model development and deployment. |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/accelerated-data-science-professional |
NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Analysis | 14% | - Time-series analysis
|
| Data Manipulation and Software Literacy | 19% | - GPU-accelerated data manipulation using cuDF
|
| MLOps | 19% | - Model monitoring and management
|
| Data Preparation | 17% | - Data cleaning and quality handling
|
| GPU and Cloud Computing | 16% | - Cloud GPU environments
|
| Machine Learning | 15% | - Feature engineering and hyperparameter tuning
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
A data scientist wants to process a large dataset using multiple GPUs on an NVIDIA-supported system. They decide to use Dask to enable parallelism.
Which of the following steps is most essential for leveraging Dask for multi-GPU scaling?
- A. Convert all data into Pandas DataFrames before distributing computations
- B. Use dask.array or dask.dataframe with dask_cuda.CUDACluster to distribute computations across multiple GPUs
- C. Train a deep learning model on a single GPU first, then switch to Dask for scaling
- D. Use ThreadPoolExecutor to manage parallel computations on GPUs
Correct Answer: B 🗳️
You need to deploy a machine learning model on a GPU-equipped system. The GPU has 16GB of VRAM, and the model requires approximately 12GB of memory during inference. However, additional system processes and other applications consume 5GB of VRAM.
What would happen if you attempt to run inference without making any optimizations, and how should you resolve the issue?
- A. Switching from a GPU to CPU inference will resolve memory issues without performance loss
- B. The model will run successfully but with reduced performance due to memory fragmentation
- C. The model will run without issues because 16GB of VRAM is sufficient for a 12GB model
- D. The model will fail to run due to out-of-memory (OOM) errors, and using a smaller batch size can help reduce memory usage
Correct Answer: D 🗳️
You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?
- A. Increase the batch size to fully utilize available GPU memory and reduce per-sample processing overhead.
- B. Reduce the number of layers in the model to decrease computation time.
- C. Use DLProf's Tensor Core Analysis to check if the model is leveraging Tensor Cores effectively.
- D. Enable mixed precision training to improve computational efficiency.
Correct Answer: C 🗳️
Which of the following methods are commonly used to handle missing data in data analysis? (Select two)
- A. Using the mode to impute missing values
- B. Ignoring missing data entirely
- C. Imputing with the mean or median of the feature
- D. Removing rows with missing data
- E. Using a fixed constant value for imputation
Correct Answer: C,D 🗳️
You are analyzing a dataset that contains missing values.
Which of the following techniques is most appropriate when dealing with missing numerical data in a dataset, ensuring minimal impact on model performance?
- A. Replacing missing values with a constant value (e.g., zero)
- B. Using k-nearest neighbors (KNN) imputation
- C. Replacing missing values with the mean of the column
- D. Removing rows with missing values
Correct Answer: B 🗳️
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