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NVIDIA NCP-ADS Exam Overview:
| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional Accelerated Data Science |
| Exam Number: | NCP-ADS |
| Exam Duration: | 120 minutes |
| Related Certifications: | NVIDIA-Certified Professional: Accelerated Data Science |
| Passing Score: | ~70% |
| Available Languages: | English |
| Exam Price: | $200 USD |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple-choice |
| Real Exam Qty: | 60-70 |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online, remotely proctored |
| Pre Condition: | 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. |
| Official Syllabus URL: | https://www.nvidia.com/en-us/learn/certification/accelerated-data-science-professional |
NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps | 19% | - Experiment tracking
|
| Topic 2: Machine Learning | 15% | - Deep learning frameworks integration
|
| Topic 3: Data Preparation | 17% | - Feature engineering
|
| Topic 4: GPU and Cloud Computing | 16% | - GPU resource management
|
| Topic 5: Data Analysis | 14% | - Time-series analysis
|
| Topic 6: Data Manipulation and Software Literacy | 19% | - Distributed computing with Dask
|
NCP-ADS (NVIDIA) Exam FAQ: Trusted Answers
NVIDIA-Certified-Professional Accelerated Data Science is an official NVIDIA certification exam, registered under the code NCP-ADS. Passing it awards the NVIDIA-Certified Professional certification, a credential at the Professional level. It also connects to NVIDIA-Certified Professional: Accelerated Data Science. The exam is demanding by design, and that difficulty is precisely what makes the credential meaningful for career development.
The NVIDIA-Certified-Professional Accelerated Data Science exam presents 60-70 questions within 120 minutes. That is a brisk pace, and the candidates who handle it best are the ones who rehearsed it. Use the Lead2PassExam engine for full timed simulations, practice flagging and returning, and arrive on exam day with a pacing strategy already proven.
Passing NVIDIA-Certified-Professional Accelerated Data Science takes ~70%, and official registration costs $200 USD. Retakes bill the full $200 USD again, so preparation is the least expensive insurance available. Let your Lead2PassExam practice scores guide the timing: book when you clear the requirement consistently, not occasionally.
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.
Policies get revised, so confirm the current requirements before you register on the official exam page.
Yes. Lead2PassExam offers a free demo of the NVIDIA-Certified-Professional Accelerated Data Science questions, so you can verify the quality personally before purchasing. Your purchase then includes a one-year service warranty: updates are free for 365 days, and after expiry you can extend the update service at a 50% discount.
Your money is protected by a 100% money-back guarantee with defined conditions. Take the NVIDIA-Certified-Professional Accelerated Data Science exam within 60 days of purchase; if you fail, you may claim a full refund, provided the exam matches your product. Attempts within 3 days of purchase are ineligible, as are downloaded-but-unused products, free materials, and expired orders; the candidate name must match the payer name. Submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are processed within 7 days. You may instead wait for the update version or change to other exam material: exchange for two other exam products of equal value, free, with your original purchase keeping its update service.
Delivery is instant: files unlock for download at payment and are emailed within one minute. If nothing arrives within 2 hours, check spam and contact customer service, online 7/24 even on official holidays. Installation is unlimited across your computers.
NVIDIA-Certified-Professional Accelerated Data Science is organized into 6 official domains. The most heavily weighted are GPU and Cloud Computing (16%), Machine Learning (15%), and MLOps (19%). The full breakdown appears above on this page; study the weightings and your preparation priorities set themselves.
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?
- A. Prefetching data with the CPU while training on the GPU
- B. Using mixed-precision training with Tensor Cores
- C. Running training exclusively on CPU
- D. Disabling data parallelism
Correct Answer: B 🗳️
A research team is analyzing a large transportation network and wants to identify groups of locations that are closely connected based on travel routes.
Which NVIDIA cuGraph function would be the most effective for this task?
- A. Use cuGraph's pagerank() to rank the most connected locations.
- B. Use cuGraph's shortest_path() to compute all shortest paths and derive clusters manually.
- C. Use cuGraph's louvain() function to detect communities within the graph.
- D. Apply cuGraph's bfs() (Breadth-First Search) to find clusters of locations.
Correct Answer: C 🗳️
A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?
- A. Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.
- B. Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
- C. Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.
- D. Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.
Correct Answer: C 🗳️
You are working with a large dataset containing numeric and categorical features, which will be processed using NVIDIA RAPIDS cuDF for accelerated analytics.
To optimize performance while minimizing memory usage, which data type is the most appropriate for storing a categorical variable with a small number of unique values?
- A. int64 - Provides high precision and avoids potential overflow.
- B. category - Optimizes storage and computation for categorical data in cuDF.
- C. bool - Minimizes memory usage and supports efficient operations for categorical data.
- D. float32 - Reduces memory consumption compared to float64 while maintaining precision.
Correct Answer: B 🗳️
Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?
- A. TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.
- B. TensorRT automatically builds training models from raw data without requiring pre-trained models.
- C. TensorRT is mainly used for data visualization and not for model inference.
- D. TensorRT is only compatible with image classification models and does not support other model types.
Correct Answer: A 🗳️
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