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

NCP-ADS real exams

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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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
GPU and Cloud Computing16%- Resource management and scaling strategies
- Cloud GPU environments and deployment
- CRISP-DM and data science methodology
- GPU architecture and acceleration principles
MLOps19%- Monitoring, logging and maintenance
- Pipeline automation and orchestration
- End-to-end workflow management
- Model deployment and serving
Data Manipulation and Software Literacy19%- GPU-accelerated ETL workflows
- Performance profiling and optimization tools
- Dependency management and containerization
- Data processing libraries selection and usage
Data Preparation17%- Workflow monitoring and bottleneck identification
- Data cleaning, preprocessing and transformation
- Data validation and quality assurance
- Feature engineering and data type optimization
Data Analysis14%- Time-series analysis and anomaly detection
- Distributed and parallel data processing
- Data visualization and graph analytics
- Exploratory Data Analysis (EDA)
Machine Learning15%- Model evaluation and validation
- Distributed training strategies
- GPU-accelerated ML frameworks and algorithms
- Model training and hyperparameter tuning

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1

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?

  • A. Triton Inference Server
  • B. DLA (Deep Learning Accelerator)
  • C. cuML
  • D. cuBLAS
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #2

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)

  • A. NVIDIA TensorRT Profiling
  • B. NVIDIA nvidia-smi
  • C. NVIDIA DLA (Deep Learning Accelerator)
  • D. NVIDIA Nsight Systems
  • E. NVIDIA CUDA Profiler (nvprof)
Reveal Solution  Discussion  0

Correct Answer: D,E  🗳️

Question #3

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?

  • A. Use cuml.StandardScaler() from RAPIDS to normalize the dataset before feeding it into the model.
  • B. Apply batch normalization layers in the neural network to handle feature scaling dynamically during training.
  • C. Use cupy.linalg.norm() to normalize each feature vector individually to unit length.
  • D. Manually compute the feature mean and variance on the CPU and apply the transformation before training.
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

Question #4

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?

  • A. Modin with Ray backend
  • B. pandas
  • C. NumPy with CuPy acceleration
  • D. Dask DataFrame with Dask-CUDA
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #5

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?

  • A. Using TensorFlow's Dataset API to load and preprocess the data on GPUs
  • B. Using Apache Spark with PySpark for CPU-based distributed data processing
  • C. Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing
  • D. Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

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