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Exam Code: NCP-ADS
Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Updated: Sep 12, 2026
Q & A: 303 Questions and Answers
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| Section | Weight | Objectives |
|---|---|---|
| GPU and Cloud Computing | 16% | - Resource management and scaling strategies - Cloud GPU environments and deployment - CRISP-DM and data science methodology - GPU architecture and acceleration principles |
| MLOps | 19% | - Monitoring, logging and maintenance - Pipeline automation and orchestration - End-to-end workflow management - Model deployment and serving |
| Data Manipulation and Software Literacy | 19% | - GPU-accelerated ETL workflows - Performance profiling and optimization tools - Dependency management and containerization - Data processing libraries selection and usage |
| Data Preparation | 17% | - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation - Data validation and quality assurance - Feature engineering and data type optimization |
| Data Analysis | 14% | - Time-series analysis and anomaly detection - Distributed and parallel data processing - Data visualization and graph analytics - Exploratory Data Analysis (EDA) |
| Machine Learning | 15% | - Model evaluation and validation - Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning |
Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?
Correct Answer: A 🗳️
You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations.
You need to monitor the pipeline's performance to identify bottlenecks.
Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)
Correct Answer: D,E 🗳️
You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.
What is the best way to standardize the data efficiently to improve model convergence?
Correct Answer: A 🗳️
You are working on a large dataset (several terabytes in size) and need to perform data preprocessing, filtering, and transformations before training a machine learning model.
Given the dataset size and the requirement to optimize for GPU acceleration using NVIDIA technologies, which of the following is the most appropriate data processing library to use?
Correct Answer: D 🗳️
You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?
Correct Answer: C 🗳️
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