NVIDIA-Certified-Professional Accelerated Data Science : NCP-ADS Exam

Exam Code: NCP-ADS
Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Updated: Oct 10, 2026
Q & A: 303 Questions and Answers

About NVIDIA-Certified-Professional Accelerated Data Science : NCP-ADS Exam Questions

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning	15%	- Distributed training strategies - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning
Topic 2: Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Data processing libraries selection and usage - Performance profiling and optimization tools - Dependency management and containerization
Topic 3: MLOps	19%	- Pipeline automation and orchestration - End-to-end workflow management - Model deployment and serving - Monitoring, logging and maintenance
Topic 4: Data Preparation	17%	- Data cleaning, preprocessing and transformation - Data validation and quality assurance - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification
Topic 5: Data Analysis	14%	- Data visualization and graph analytics - Distributed and parallel data processing - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA)
Topic 6: GPU and Cloud Computing	16%	- GPU architecture and acceleration principles - CRISP-DM and data science methodology - Cloud GPU environments and deployment - Resource management and scaling strategies

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1

You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

A. Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.
B. Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.
C. Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.
D. Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.

Reveal Solution Discussion 0

Correct Answer: A,B 🗳️

Question #2

Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)

A. MLOps replaces the need for data preprocessing during training and deployment
B. MLOps ensures that models trained on GPUs can only run on GPUs during deployment
C. MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows
D. MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance

Reveal Solution Discussion 0

Correct Answer: C,D 🗳️

Question #3

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. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
B. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.
C. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.

Reveal Solution Discussion 0

Correct Answer: C 🗳️

Question #4

You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.
Which of the following approaches correctly normalizes the column using cuDF?

A. df["price"] = df["price"] / df["price"].max()
B. df["price"] = (df["price"] - df["price"].mean()) / df["price"].std()
C. df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )
D. df["price"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())

Reveal Solution Discussion 0

Correct Answer: D 🗳️

Question #5

You are setting up a deep learning model for training on a multi-GPU cluster. You want to maximize training efficiency while maintaining model convergence.
Which of the following strategies is most effective in ensuring efficient multi-GPU training?

A. Use Data Parallelism, where each GPU gets a different portion of the dataset, but gradients are averaged across all GPUs.
B. Use Model Parallelism, where different layers of the model are placed on different GPUs to reduce communication overhead.
C. Reduce batch size to ensure each GPU receives only a small portion of the training data.
D. Train each GPU independently on a different dataset to reduce the communication bottleneck.

Reveal Solution Discussion 0

Correct Answer: A 🗳️

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