NCA-ADS (NVIDIA-Certified Associate: Accelerated Data Science) is NVIDIA's entry-level exam for data science on GPUs. NVIDIA describes it as a credential that "validates a candidate's proficiency in leveraging GPU-accelerated tools and libraries for data science workflows." Its study guide pictures the person it certifies as an early-career practitioner who "uses GPU-accelerated tools to prepare data, build machine learning models, and construct reproducible pipelines on modern data platforms," taking raw data all the way to evaluated models ready for deployment.
Exam Quick Facts
The NCA-ADS article series
- 20 NCA-ADS practice questions with explanations, spread across all eight sections
- The 4-week NCA-ADS study plan, built on NVIDIA's suggested reading
- NCA-ADS practice tests: what is available and how to use it
Who NCA-ADS is for
The exam page names its audiences: data scientists, data analysts, data engineers, machine learning engineers, AI DevOps engineers, software engineers, solution architects, deep learning performance engineers and researchers. NVIDIA asks for one to two years of experience in accelerated data science, using GPU-based tools to process and analyze large datasets and to speed up machine learning, ETL and analytics workloads.
The study guide lists the qualifications it recommends:
- Python for data analysis, including NumPy, pandas and Jupyter notebooks
- Exposure to GPU-accelerated tools such as RAPIDS, cuDF, cuML and GPU-enabled XGBoost
- Hands-on experience with at least one workflow that runs from data ingestion and cleaning through feature engineering to model training and evaluation
- Basic experience with experiment tracking, configuration management or model versioning
- Comfort with Linux, conda or pip environments and basic Git
That describes a data analyst or data scientist who works in pandas and scikit-learn every day and has started running the same work on GPUs, or a data engineer who builds pipelines and wants the modeling and MLOps basics on paper.
Try 15 NCA-ADS practice questions free
They come from the NCA-ADS practice tests, and each option shows its explanation once you answer.
- 6 timed practice exams
- Every answer explained
- A score for each domain
- $19.99 one-time
How the exam works
The exam has 50 to 60 questions in 60 minutes. You take it online with a remote proctor, through a Certiverse account, and it costs $125 and runs in English. The certification is valid for two years from issuance, and you recertify by taking the exam again. Passing earns a digital badge and an optional certificate.
As of October 2026, NVIDIA publishes neither a passing score nor sample questions for NCA-ADS. The topic list and the suggested reading in the study guide are the best guide to what the questions cover.
The eight exam sections
| Section | Weight |
|---|---|
| Data Manipulation and Preparation | 23% |
| Machine Learning With 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% |
| Software and Environment Management | 6% |
Data Manipulation and Preparation (23%)
The largest section covers getting data ready on the GPU. NVIDIA lists data integration, joining and manipulation with cuDF and pandas; data cleaning, quality handling and governance compliance; GPU-accelerated ETL with RAPIDS, Dask or Spark; feature engineering for numerical and categorical variables; class imbalance and synthetic data; dimensionality reduction and sampling; and efficient processing and storage with Parquet. In practice that means knowing pandas well enough to spot where cuDF behaves differently, knowing when one GPU is enough and when Dask or the RAPIDS Accelerator for Apache Spark has to spread the work, and knowing how a columnar format such as Parquet lets a job read only the columns and row groups it needs.
Machine Learning With RAPIDS (16%)
This section is about training models and judging them: GPU-accelerated training with cuML and XGBoost, regression, classification and clustering, evaluation and comparison, hyperparameter tuning, cross-validation, and metrics read from a confusion matrix. cuML's API closely mirrors scikit-learn's, and cuml.accel runs unchanged scikit-learn code on the GPU, so most of the difficulty is machine learning judgment: which splitter suits grouped or time-ordered data, which metric still means something when one class is rare, and what a gap between training and validation scores tells you.
Data Science Pipelines and Workflow Automation (13%)
Here the steps become a pipeline that someone else can rerun. The topics are pipeline design, feature engineering, selection and transformation, mitigating underfitting and overfitting, dataset augmentation and integration, automation and scaling, and reproducible pipelines with RAPIDS and Dask. The study guide adds a data flywheel, in which production data keeps improving the deployed models. Leakage runs through the whole section: preprocessing fitted on every row before validation, or features that would not exist at prediction time.
Descriptive Analysis and Visualization (13%)
This section covers exploratory data analysis and descriptive statistics, visualization, choosing a plot for a given goal, hypothesis testing and statistical significance, and interpreting patterns, trends and relationships. NVIDIA's reading pairs the pandas documentation with GPU tools for large data: cuDF for exploration, and hvPlot, Datashader, cuxfilter and Plotly Dash for charts and dashboards over millions of points. The statistics are the familiar ones: which test fits paired or independent samples, how significance differs from the size of an effect, and when a mean misleads.
Foundations of Accelerated Data Science (12%)
The foundations section holds the concepts the other sections rely on: Python for data analysis (NumPy, pandas, Jupyter), what GPU acceleration does well, CPU versus GPU workloads and the cost of moving data between them, the end-to-end workflow from ingestion through ETL and cleaning to transformation, distributed versus GPU-accelerated frameworks, and model parameters, tuning and over- and underfitting. Much of it comes down to recognizing when a GPU helps: large, parallel operations on whole columns speed up, while small tables, row-by-row Python loops and frequent copies between host and device give the advantage away.
Introductory MLOps Practices (10%)
This section covers models after training: monitoring and optimizing pipelines for performance and reliability, tracking experiments with MLflow, Weights & Biases or custom tools, saving and loading models and generating predictions, monitoring production models for drift and degradation, managing artifacts and configurations for reproducibility, and benchmarking workflows to choose hardware. The suggested reading comes from NVIDIA, Prometheus, Datadog, MLflow and DagsHub.
Advanced Data Structures (7%)
Two kinds of data with rules of their own. For time series: handling, splitting and forecast evaluation, and missing or irregular timestamps with cuDF interpolation. For graphs: representing data as a graph, analyzing it, ranking nodes by importance and visualizing relationships, with cuGraph doing the GPU work. The exam page also lists CPU versus GPU performance for temporal analytics.
Software and Environment Management (6%)
The smallest section keeps the work reproducible: environment files, Python environments with conda, pip or Docker, managing dependencies in multi-user environments, checking a GPU environment (driver, CUDA and RAPIDS compatibility, nvidia-smi, device visibility) and resolving a dependency conflict, and the basics of Git.
Three questions from the NCA-ADS practice tests
A new team member trains a cuML random forest and asks why some numbers come from training and others are chosen before training: the split thresholds inside the trees, and settings such as n_estimators and max_depth. Which statement is correct?
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Correct. scikit-learn defines hyperparameters as parameters not learned within the estimator, passed to its constructor. The model's internal values, such as split thresholds, are learned from the data.
A. Being readable after training does not make a value a hyperparameter. Thresholds are learned from the data.
B. Fitting does not change n_estimators or max_depth. They stay as set, which is why hyperparameter search tries different values.
D. This reverses the two. The number of trees and depth limit are constructor arguments; thresholds come from the data.
An analyst runs these lines in a notebook connected to a Dask-CUDA cluster: ddf = dask_cudf.read_parquet("events/*.parquet") daily = ddf.groupby("day").clicks.sum() The cell finishes in 0.2 seconds, nvidia-smi shows no GPU activity, and printing daily shows a structure with npartitions instead of numbers. What should the analyst do to get the daily totals?
[object Object]
Correct. Dask DataFrame is lazy by default: read_parquet and groupby only build a graph. compute() runs it on the workers and returns a concrete cuDF result.
A. Dask aggregates across partitions as part of the graph. Repartitioning is also lazy, so the totals would still not appear.
C. dask_cudf runs groupby aggregations on the GPU partition by partition. Reading everything into one cuDF frame may not fit in GPU memory and is not needed here.
D. A busy cluster makes work wait; it does not return a placeholder in 0.2 seconds. Nothing was submitted because nothing asked for the result.
A disease screening model is evaluated with sklearn.metrics.confusion_matrix(y_true, y_pred), labels [0 = healthy, 1 = sick]. The output is: [[880, 20], [ 45, 55]] A draft report says "20 sick patients were missed by the screen." Which correction is right?
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Correct. scikit-learn puts true labels in rows and predictions in columns. C[1,0] = 45 counts sick patients predicted healthy (false negatives); C[0,1] = 20 counts healthy patients flagged sick (false positives).
A. 55 sits on the diagonal at C[1,1]: sick patients the screen correctly flagged as sick.
B. Row 0 holds patients whose true label is healthy, and column 1 is the prediction sick. That cell counts false alarms.
C. The off-diagonal cells are two different errors: 20 false positives and 45 false negatives. Only the false negatives are missed sick patients.
Full NCA-ADS set: 6 timed exams, every answer explained, $19.99 one-time.
See the practice testsNCA-ADS or NCP-ADS?
NVIDIA offers accelerated data science at two levels, and the experience it asks for is the clearest guide to which one to take.
| NCA-ADS | NCP-ADS | |
|---|---|---|
| Level and experience NVIDIA asks for | Associate; one to two years in accelerated data science | Professional; two to three years of hands-on experience |
| Scope | The whole workflow at working depth, with statistics, MLOps basics, time series, graphs and environment management | Ownership of GPU pipelines: data manipulation and software literacy, data preparation, GPU and cloud computing, machine learning, data analysis and MLOps |
If you use pandas and scikit-learn every day and have begun moving that work to RAPIDS, NCA-ADS matches where you are. Once you run accelerated pipelines in production and make the GPU and cloud choices for them, NCP-ADS asks for that experience. The NCP-ADS complete guide covers the professional exam.
How to prepare
Start from the study guide. For every section, NVIDIA's study guide lists suggested reading: mostly NVIDIA technical blog posts, plus documentation from pandas, scikit-learn, Prometheus, MLflow, conda and others. Those readings define the vocabulary of the topic list, for example GPU-accelerated DataFrames for pandas users, scikit-learn's common pitfalls and monitoring machine learning models in production.
Take the courses in NVIDIA's learning path. The exam page lists three: Accelerating End-to-End Data Science Workflows, a self-paced course that NVIDIA also runs as an instructor-led workshop, Best Practices in Feature Engineering for Tabular Data With GPU Acceleration and Accelerated Machine Learning for Time Series Forecasting. The study guide recommends the first course for every section, adds the feature engineering course for data preparation and the time series course for advanced data structures.
Run the workflow yourself. The study guide asks for hands-on experience with at least one complete workflow. Preporato Pro includes labs for many skills in the 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 and model serving, GPU profiling, and Docker and Git. They run in the browser with nothing to install, and every step is checked automatically. Descriptive statistics, visualization and graph analytics have no lab yet.
NCA-ADS skills you can practice in the browser
One lab each for a GPU pipeline, data cleaning, XGBoost, cross-validation, experiment tracking and forecasting. They come with Pro, and the four labs that run without a GPU let you try their first step before you subscribe.
GPU-Accelerated Data Science with RAPIDS
Rewrite a pandas + sklearn data-science pipeline on GPU using cuDF and cuML, benchmark each stage against the CPU baseline, and run an end-to-end filter -> feature-engineer -> predict pipeline that never leaves the GPU.
Data Cleaning and Feature Engineering: Predict Rents From a Messy Export
Take a messy real-world export to a model that beats the baseline by 70%: fix units, spellings and mixed rent periods, compare against no-model baselines, build a scikit-learn pipeline with imputation, missing-value indicators, scaling and one-hot encoding, model a log target, run an ablation to keep only features that help, and look at the test set once.
Gradient Boosting with XGBoost: Predict Late Deliveries and Explain Every Prediction
Beat a logistic regression with gradient-boosted trees on messy delivery data, watch extra rounds memorise the training set and stop them early, tune tree shape with a randomised search, compare split-count, gain and permutation importance against a planted noise column, and explain single predictions with per-feature contributions.
Overfitting and Cross-Validation: Find What Really Makes a Better Cup
Watch a model memorise 150 training rows, see how much a single validation split can lie, choose models by cross-validation, tame 60 noise columns with Ridge and Lasso, prove on pure noise that feature selection outside the pipeline fakes a good score, and finish with a grid search and one honest look at the test set.
MLflow Experiment Tracking: From Single Run to Team Workflow
Wire the four load-bearing pieces of MLflow into a real training loop — tracked runs with params and metrics, a registered model with stage transitions, a multi-run sweep + search, and a production spec (server, k8s Job, tags, autolog).
Time-Series Forecasting: Baselines, Backtesting, Calendar Features, Prediction Intervals and the Newsvendor Rule
Forecast a bakery's daily sales two weeks ahead. Beat naive and seasonal-naive baselines, backtest with rolling origins that never see the future, build calendar and lag features a 14-day forecast can actually know, turn backtest errors into prediction intervals and check their coverage, and choose how much to bake from the cost of a loaf left over versus a customer turned away.
Practice under time. Sixty minutes for up to 60 questions leaves about a minute per question. Timed practice tests show which sections cost you points while there is still time to fix them. The free NCA-ADS practice questions on the certificate page come from the full preporato.com practice tests, and the 4-week study plan turns the steps above into a schedule.
Frequently asked questions
How hard is the NCA-ADS exam?
It is an associate exam whose difficulty comes from breadth. Eight sections run from data preparation and machine learning through pipelines, statistics and GPU concepts to MLOps, time series, graphs and environment management. NVIDIA also asks for one to two years of hands-on experience with GPU-based data science tools, so plan to have used the tools as well as read about them.
Are there prerequisites for NCA-ADS?
The exam page asks for one to two years of experience in accelerated data science. NVIDIA lists no certification that you must hold first.
What is the passing score for NCA-ADS?
NVIDIA does not publish one, as of October 2026. Aim for consistent results of 80% or more on timed practice tests before you book, so that a hard draw of questions on the day still leaves a margin.
Does NVIDIA publish sample questions for NCA-ADS?
Not as of October 2026. The 20 NCA-ADS practice questions on this blog and the free questions on the certificate page are written from NVIDIA's topic list, with an explanation for every option.
How long does it take to prepare for NCA-ADS?
Our study plan assumes four weeks of part-time study for someone who already uses pandas and scikit-learn. Plan for longer if Python data work or GPUs are new to you.
Should I take NCA-ADS or NCP-ADS first?
Start with NCA-ADS if you have one to two years of experience with GPU-accelerated data science. NCP-ADS asks for two to three years of hands-on experience and goes deeper into running accelerated pipelines in production.
How long is the NCA-ADS certification valid?
Two years from issuance. You recertify by taking the exam again.
Are there hands-on labs for NCA-ADS?
Yes, with Preporato Pro: labs for data cleaning and features, a RAPIDS pipeline on a GPU, scikit-learn and XGBoost with cross-validation, time series backtesting, MLflow tracking, drift monitoring, and Docker and Git, each checked step by step in the browser. Descriptive statistics, visualization and graph analytics have no lab yet.
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