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Updated: Oct 10, 2026
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NVIDIA NCP-ADS Exam Overview:
| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional Accelerated Data Science |
| Exam Number: | NCP-ADS |
| Real Exam Qty: | 60-70 |
| Exam Duration: | 120 minutes |
| Exam Price: | $200 USD |
| Related Certifications: | NVIDIA-Certified Professional: Accelerated Data Science |
| Available Languages: | English |
| Passing Score: | ~70% |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple-choice |
| 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 Manipulation and Software Literacy | 19% | - Distributed computing with Dask
|
| GPU and Cloud Computing | 16% | - GPU architecture and fundamentals
|
| Machine Learning | 15% | - Deep learning frameworks integration
|
| Data Analysis | 14% | - Time-series analysis
|
| Data Preparation | 17% | - Feature engineering
|
| MLOps | 19% | - Model deployment and serving
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?
- A. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.
- B. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
- C. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.
Correct Answer: A 🗳️
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 working with a large dataset in RAPIDS cuDF and plan to standardize the numerical features using cuml.preprocessing.StandardScaler(). However, some columns contain missing values.
What is the best approach to handle the missing values before applying standardization?
- A. Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
- B. Replace missing values with zero before standardization.
- C. cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
- D. Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
Correct Answer: A 🗳️
You are designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?
- A. Using a different precision setting for each framework to maximize performance per framework's capabilities.
- B. Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.
- C. Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.
- D. Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.
Correct Answer: C 🗳️
Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?
- A. Utilizing a distributed data processing framework like Apache Spark with GPU acceleration from the NVIDIA RAPIDS library.
- B. Relying on traditional CPU-only processing frameworks for big data tasks.
- C. Using a single-node GPU-based solution for data processing.
- D. Using cloud-based, non-GPU infrastructure for data processing
Correct Answer: A 🗳️
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