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

  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
  • Updated: Nov 05, 2026     Q & A: 303 Questions and Answers

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Following are some reference material for actual NVIDIA NCP-ADS exam test

NVIDIA NCP-ADS Exam Syllabus Topics:

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

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1

Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

  • A. Training dataset bias.
  • B. Number of model parameters.
  • C. GPU utilization and memory usage.
  • D. Hyperparameter tuning results.
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Question #2

A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?

  • A. Using a batch size of 1 to minimize memory usage
  • B. Loading all dataset samples into GPU memory at the start of training
  • C. Allocating a fixed batch size without monitoring memory usage
  • D. Using dynamic batch sizing based on available GPU memory
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #3

You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

  • A. df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
  • B. df = (df - df.min()) / (df.max() - df.min())
  • C. df = (df - df.mean()) / df.std()
  • D. 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #4

You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

  • A. Int64
  • B. String
  • C. Float64
  • D. Int32
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Question #5

You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.
Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?

  • A. RAPIDS
  • B. TensorFlow
  • C. TensorRT
  • D. CUDA
Reveal Solution  Discussion  0

Correct Answer: A  🗳️

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