What is the NVIDIA NCA-ADS certification?

NCA-ADS (NVIDIA-Certified Associate: Accelerated Data Science) is an entry-level NVIDIA certification that validates proficiency with GPU-accelerated tools and libraries for data science workflows, such as RAPIDS cuDF, cuML and Dask. NVIDIA recommends 1 to 2 years of experience in accelerated data science.

How many questions does the NCA-ADS exam have and how long is it?

NVIDIA lists 50 to 60 questions in 60 minutes, taken online with remote proctoring through a Certiverse account. The exam is in English, and NVIDIA does not publish the passing score.

How much does the NCA-ADS exam cost?

The exam costs $125 per attempt. The certification is valid for two years from issuance, and you recertify by retaking the exam. Passing earns a digital badge and an optional certificate.

Which topics does NCA-ADS cover?

Eight sections: Data Manipulation and Preparation (23%), Machine Learning With NVIDIA RAPIDS (16%), Data Science Pipelines and Workflow Automation (13%), Descriptive Analysis and Visualization (13%), Foundations of Accelerated Data Science (12%), Introductory MLOps Practices (10%), Advanced Data Structures (7%) and Software and Environment Management (6%).

How is NCA-ADS different from NCP-ADS?

NCA-ADS is NVIDIA's associate (entry-level) data science exam and asks for 1 to 2 years of experience. NCP-ADS (NVIDIA-Certified Professional: Accelerated Data Science) is the professional-level exam and asks for 2 to 3 years of hands-on experience. Preporato has practice exams for both.

What do the Preporato practice exams for NCA-ADS include?

Six timed practice exams of 60 questions each, 360 in total, with each exam weighted like NVIDIA's eight sections. Every option has an explanation, and every question links to the documentation behind it, from RAPIDS and XGBoost to scikit-learn, pandas, Dask and MLflow. Each exam also includes six Select TWO questions for extra depth. The first five questions of each exam are free to try.

How should I prepare for NCA-ADS?

NVIDIA's learning path lists three self-paced courses: Accelerating End-to-End Data Science Workflows, Best Practices in Feature Engineering for Tabular Data with GPU Acceleration, and Accelerated Machine Learning for Time Series Forecasting, plus a study guide. Then take timed practice exams, review every explanation, run the workflows yourself in hands-on labs, and book the exam once you score 80% or more on several practice exams.

Are there hands-on labs for NCA-ADS on preporato.com?

Yes. Preporato Pro includes hands-on labs for skills in NVIDIA's NCA-ADS topic list: data cleaning and feature engineering, class imbalance, PCA and clustering; a RAPIDS pipeline with cuDF and cuML on a GPU; scikit-learn and XGBoost models with cross-validation, metrics and decision thresholds; time series backtesting; MLflow tracking, drift monitoring, versioning and model serving; GPU profiling and monitoring; and Docker and Git. They run in the browser with nothing to install, the GPU labs on NVIDIA GPU pods, and every step is checked automatically. Descriptive statistics, visualization and graph analytics have no lab yet.

NVIDIA· Associate

NVIDIA-Certified Associate: Accelerated Data Science

NCA-ADS

The NCA-ADS exam tests entry-level skills for GPU-accelerated data science: preparing data with cuDF and pandas, machine learning with cuML and XGBoost, pipelines and automation, analysis and visualization, MLOps basics, time series and graphs, and environment management. On preporato.com you prepare with timed practice exams, an explanation for every question and hands-on labs.

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Practice exam #6 across all eight NCA-ADS sections, including point-in-time joins, quantile regression in XGBoost, feature attribution drift and graph layouts.

60 questions

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Why Get This Certification?

Skills You'll Validate

Preparing data with cuDF and pandas: joins, cleaning, GPU ETL with Dask or Spark, feature engineering, class imbalance and Parquet
Training and evaluating regression, classification and clustering models with cuML and XGBoost, with cross-validation and tuning
Designing reproducible, scalable pipelines with RAPIDS and Dask, including feature selection and overfitting control
Exploring data with descriptive statistics, visualizations and hypothesis tests
Explaining GPU acceleration: CPU versus GPU workloads, memory transfers and distributed frameworks
+3 more skills

Career Benefits

Target Roles

Data ScientistData AnalystData EngineerMachine Learning Engineer

Exam Topics & Domains

The certification exam evaluates your knowledge across 8 key competency areas:

Technologies & Tools Covered

NVIDIA RAPIDS cuDF and cudf.pandasNVIDIA cuML and cuml.accelNVIDIA cuGraph and nx-cugraphXGBoostDask and Dask-CUDARAPIDS Accelerator for Apache SparkPolars GPU enginepandas, NumPy and CuPyscikit-learn and imbalanced-learnApache ParquetMLflowWeights & BiasesNVIDIA Triton Inference ServerConda, pip and DockerGit

What You'll Be Able To Do

After earning this certification, you'll be able to:

Join, clean and transform large datasets on the GPU with cuDF, Dask or Spark, and store them efficiently in Parquet
Train, tune and validate cuML and XGBoost models with the right cross-validation and metrics
Design pipelines that avoid leakage, scale across GPUs and give reproducible results
Explore data with descriptive statistics, plots and hypothesis tests, and choose the right chart for a question
Decide which workloads benefit from GPUs and keep data transfers between CPU and GPU low
Track experiments, save and serve models, and monitor them for drift
Work with time series and graphs: time-aware splits, irregular timestamps, centrality and layouts
Set up reproducible Conda, pip and Docker environments, check GPU drivers and CUDA versions, and use Git

Why This Certification Matters

Skills You'll Validate

Preparing data with cuDF and pandas: joins, cleaning, GPU ETL with Dask or Spark, feature engineering, class imbalance and Parquet
Training and evaluating regression, classification and clustering models with cuML and XGBoost, with cross-validation and tuning
Designing reproducible, scalable pipelines with RAPIDS and Dask, including feature selection and overfitting control
Exploring data with descriptive statistics, visualizations and hypothesis tests
Explaining GPU acceleration: CPU versus GPU workloads, memory transfers and distributed frameworks
Tracking experiments with MLflow and Weights & Biases, saving models, and monitoring drift and performance
+2 more skills

Career Benefits

Target Roles

Data ScientistData AnalystData EngineerMachine Learning Engineer+2

Industries Actively Hiring

Financial ServicesRetail and E-commerceHealthcareTelecommunicationsManufacturingSoftware and SaaS

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