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.

360+

Questions

Pruebas de Práctica

25Pro

Hands-on labs

Lifetime

Updates

Qué Incluye

Practice

6 exam-realistic practice tests

360+ questions

Hands-on

Labs· 25

NVIDIA-Certified Associate: Accelerated Data Science

Qué Incluye

6 exam-realistic practice tests

Labs· 25

GPU Container Lifecycle: Build, Test, Ship, Rollback

GPU Observability: From nvidia-smi to a Production Monitoring Stack

MLflow Experiment Tracking: From Single Run to Team Workflow

Nsight Systems Profiling: Finding the Bottleneck That Costs You 40% of Your GPU

Batch Size & Precision Sweep: Finding Your Sweet Spot

GPU-Accelerated Data Science with RAPIDS

Reproducible Training: The Flags, The Cost, The Artifacts

Terminal and Git for AI Work: Review and Undo What an AI Assistant Changes

Your First Machine Learning Model: Predict Churn with scikit-learn

Metrics That Match the Business: Choose a Threshold from a Cost Table

Data Cleaning and Feature Engineering: Predict Rents From a Messy Export

Overfitting and Cross-Validation: Find What Really Makes a Better Cup

Gradient Boosting with XGBoost: Predict Late Deliveries and Explain Every Prediction

Serve an ML Model on CPU: ONNX Export, FastAPI, Dynamic Batching and a p95 Latency Budget

ML Model Monitoring: Data Drift with PSI, Delayed Labels, Alert Rules and Root Cause

Customer Segmentation with k-means and PCA: Scaling, Choosing k, Stability and Deployment

Imbalanced Data and Anomaly Detection: Average Precision, Rebalancing, Alert Budgets and Unseen Failures

Time-Series Forecasting: Baselines, Backtesting, Calendar Features, Prediction Intervals and the Newsvendor Rule

Explain a Model: Permutation Importance, Target Leakage, Partial Dependence and Exact Shapley Values

Incident Drill: Debug a Failing ML Service from Logs and Metrics

Publish a Model: Card, Evaluation and a Versioned Release

Training Pipeline Orchestration: Ordering, Retries, Failure Isolation and Resume

Data and Model Versioning: Content Addressing, Lineage and Garbage Collection

CI for Models: Data Contracts, Behavioral Tests, Golden Sets and a Merge Gate

Feature Store Basics: Point-in-Time Correctness and Training-Serving Skew

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