Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional
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Check Updated on: Oct 22, 2026
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Manipulation and Software Literacy | 19% | - Distributed computing with Dask
|
| Topic 2: MLOps | 19% | - Containerization and environment management
|
| Topic 3: Data Preparation | 17% | - Feature engineering
|
| Topic 4: Data Analysis | 14% | - Time-series analysis
|
| Topic 5: Machine Learning | 15% | - Model training with GPU acceleration
|
| Topic 6: GPU and Cloud Computing | 16% | - Cloud GPU environments
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?
- A. Using cloud-based, non-GPU infrastructure for data processing
- B. Relying on traditional CPU-only processing frameworks for big data tasks.
- C. Utilizing a distributed data processing framework like Apache Spark with GPU acceleration from the NVIDIA RAPIDS library.
- D. Using a single-node GPU-based solution for data processing.
Correct Answer: C 🗳️
You are working on a machine learning problem that involves training a deep learning model on a dataset with billions of records. The dataset is stored in a distributed cloud storage system.
Given the need for acceleration, which is the most effective approach?
- A. Reduce the dataset to a small representative sample to avoid the need for specialized acceleration.
- B. Load the entire dataset into RAM on a single powerful CPU-based machine before starting model training.
- C. Use GPU acceleration with libraries like RAPIDS AI or TensorFlow to leverage parallel processing.
- D. Store the dataset in a relational database and query it sequentially using SQL before training the model.
Correct Answer: C 🗳️
Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?
- A. Modify the training dataset during model execution
- B. Increase batch size to improve accuracy
- C. Automatically adjust the learning rate based on the model's convergence
- D. Visualize GPU memory utilization over time
Correct Answer: D 🗳️
A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?
- A. Using a single system-wide installation of CUDA and forcing all projects to use the same version
- B. Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
- C. Manually installing each package and its dependencies using pip
- D. Using only virtual environments (venv) without managing GPU dependencies separately
Correct Answer: B 🗳️
A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
What is the most likely cause of this issue?
- A. The batch size is too large, leading to excessive GPU memory utilization and slow processing.
- B. The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.
- C. The GPU is not powerful enough to process the deep learning model efficiently.
- D. The CUDA cores are overheating, leading to automatic throttling of computations.
Correct Answer: B 🗳️
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