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NCP Accelerated Data Science certification — Requirements & Free Practice

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NCP Accelerated Data Science certification

Professional-level NVIDIA credential validating GPU-accelerated data science workflow proficiency; passing the examination earns a digital badge and optional certificate, valid for two years from issuance.

Licensing authority: NVIDIA · Application information

Initial by examination
NVIDIA Accelerated Data Science certification exam
Account And Identity
The FAQ requires government-issued photo/signature ID matching registration and the NVIDIA account information on the Proctor Validation Screen.
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Published Prerequisites
Two to three years of hands-on experience in accelerated data science. Strong foundation in machine learning and GPU-accelerated computing. Experience in GPU-based optimization strategies and accelerated data manipulation techniques. Deep understanding of end-to-end data science workflows, from data preparation and cleansing to model development and deployment, with a focus on leveraging GPU acceleration for enhanced performance and efficiency.
Qualification Interpretation
The exam page calls these Prerequisites; the adopted guide labels the same qualifications Recommended Qualifications and Experience. Preserve this published experience profile without inventing an admission audit, prerequisite certification or another required examination.
Registration
Register through the specific NVIDIA-linked Certiverse NCP-ADS checkout; schedule and pay the fee. The FAQ permits scheduling up to 60 days ahead and one exam per sitting. An actual future seat is not verified.
Remote Administration
Install the testing partner's secure browser and permit proctor monitoring and recording. Remote testing includes login, multi-factor identity verification and a 360-degree environment security assessment; breaks are not permitted.
Renewal
Certification validity is two years from issuance; recertification is by retaking the exam. This holder-specific period does not establish calendar validity dates for the credential program or exam version.
Scope
Initial award through the single English NCP-ADS online remotely proctored examination; the award is a digital badge and optional certificate specifying Professional level and Accelerated data science topic.
Training
The study guide labels Recommended Training (Optional); the courses and suggested readings are preparation resources, not separate examination or mandatory course requirements.

NVIDIA Accelerated Data Science certification exam

Provider
NVIDIA; registration through the NVIDIA-linked Certiverse portal
Format
English, online and remotely proctored, question-based pass/fail examination; numerical score and item-type shares are not published in the reviewed materials
Exam questions
Total questions: 60–70.
Time limit
120 minutes
Exam fee
USD 200
Examination outline
  • Data Analysis (14%)
    • Detect anomalies in a time-series dataset
    • Conduct time-series analysis
    • Create and analyze graph data using something like cuGraph
    • Identify how much data is big data (or when to use which acceleration method)
    • Perform exploratory data analysis (EDA)
    • Visualize time-series data
  • Data Manipulation and Software Literacy (19%)
    • Design and implement ETL workflows using accelerated ETL processes
    • Implement data caching to reduce shuffle
    • Use distributed data processing frameworks to process big data
    • Implement data parallelism using Dask for multi-GPU scaling
    • Profile deep learning models using tools such as DLProf
    • Determine the optimal data processing libraries to use for varying dataset sizes
    • Transform and standardize data using cuDF
    • Manage and allocate GPU memory effectively to improve performance, utilizing strategies to optimize memory
    • Scale inference for large datasets in both single-node and multi-node systems
  • Data Preparation (17%)
    • Perform data cleansing and preprocessing using cuDF and pandas
    • Transform and standardize data
    • Standardize data as needed to ensure uniformity across features
    • Identify and acquire datasets
    • Monitor data processing pipelines to recognize bottlenecks
    • Process, organize, and store datasets
    • Generate synthetic data to augment datasets using cuDF and NVIDIA RAPIDS
  • GPU and Cloud Computing (16%)
    • Analyze graph data using GPU-accelerated tools like cuGraph
    • Optimize performance of the data science process through GPU acceleration
    • Describe, follow, and execute the CRISP-DM process
    • Utilize dependency management frameworks, such as Docker and Conda to manage software-versioning conflicts
    • Determine the optimal data type choice for each feature
    • Compare frameworks' performance by designing and implementing a benchmark
  • Machine Learning (15%)
    • Split data, choosing the best split type and method
    • Standardize data as needed to ensure uniformity across features
    • Identify how much data is big data (or when to use which acceleration method)
    • Perform rapid experimentation to find the balance between model accuracy and inference performance
    • Optimize hyperparameters of machine learning models
    • Train machine learning models for both single-GPU and multi-GPU scenarios
    • Use GPU memory optimization techniques, such as batching and mixed precision, to train machine learning models
    • Profile deep learning models using tools such as DLProf
  • MLOps (19%)
    • Determine the optimal data type choice for each feature
    • Assess and verify the size in memory of a dataset
    • Compare the required memory with the available memory on a device
    • Perform benchmarking and optimize different GPU-accelerated workflows
    • Deploy and monitor models in production
Examination sources