NVIDIA-Certified-Professional Accelerated Data Science: NCP-ADS Exam
"NVIDIA-Certified-Professional Accelerated Data Science", also known as NCP-ADS exam, is a NVIDIA Certification. With the complete collection of questions and answers, ExamcollectionPass has assembled to take you through 303 Q&As to your NCP-ADS Exam preparation. In the NCP-ADS exam resources, you will cover every field and category in NVIDIA-Certified Professional Certification helping to ready you for your successful NVIDIA Certification.
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- Exam Code: NCP-ADS
- Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
- Certification Provider: NVIDIA
- Corresponding Certification: NVIDIA-Certified Professional
- Updated: Oct 14, 2026
- No. of Questions: 303 Questions & Answers with Testing Engine
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 15% | - GPU-accelerated ML frameworks and algorithms - Distributed training strategies - Model training and hyperparameter tuning - Model evaluation and validation |
| Data Analysis | 14% | - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Data visualization and graph analytics |
| Data Manipulation and Software Literacy | 19% | - Data processing libraries selection and usage - Dependency management and containerization - Performance profiling and optimization tools - GPU-accelerated ETL workflows |
| GPU and Cloud Computing | 16% | - Resource management and scaling strategies - Cloud GPU environments and deployment - GPU architecture and acceleration principles - CRISP-DM and data science methodology |
| Data Preparation | 17% | - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization - Data validation and quality assurance - Data cleaning, preprocessing and transformation |
| MLOps | 19% | - Model deployment and serving - End-to-end workflow management - Pipeline automation and orchestration - Monitoring, logging and maintenance |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are deploying a deep learning model on an edge device with 8GB of available RAM. The model's estimated peak memory usage, including model weights, intermediate tensors, and batch data, is 9.5GB.
What is the best course of action to ensure successful deployment while maintaining performance?
- A. Increase the device's swap space to compensate for insufficient RAM
- B. Reduce the batch size during inference
- C. Reduce the number of model parameters by removing layers from the architecture
- D. Offload some computation to cloud-based processing
Correct Answer: B 🗳️
A data scientist is working with large-scale tabular datasets and wants to optimize data ingestion and storage for accelerated processing on NVIDIA GPUs. The scientist is considering different file formats and storage optimizations to maximize performance in a RAPIDS-based workflow.
Which of the following approaches is the most suitable for optimizing both storage and processing performance?
- A. Convert datasets into CSV format and store them in local disk storage.
- B. Use JSON format for easy readability and process it directly in cuDF.
- C. Store data in Parquet format and load it using cuDF in RAPIDS.
- D. Load data directly into NumPy arrays before using RAPIDS cuDF for processing.
Correct Answer: C 🗳️
You have trained a machine learning model and are deploying it into production in a cloud-based environment. You need to ensure the model is continuously monitored for performance, including drift in input data and prediction quality.
Which of the following techniques should you use to effectively monitor the model in production? (Select two)
- A. Set up continuous model retraining pipelines triggered by data drift or performance degradation.
- B. Use manual inspection of model predictions periodically to assess accuracy in production.
- C. Implement model versioning and rollback procedures to maintain consistency in production models.
- D. Restrict access to the model's API to minimize the number of calls and reduce monitoring overhead.
Correct Answer: A,C 🗳️
You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.
Which of the following is the most likely reason for this issue, as indicated by the profiling data?
- A. The GPU lacks sufficient VRAM, causing frequent memory swaps to system RAM.
- B. DLProf detected a high level of tensor core utilization, which generally indicates poor performance.
- C. The batch size is too large, leading to excessive memory allocation failures.
- D. The model contains a large number of small, inefficient kernel launches that introduce overhead.
Correct Answer: D 🗳️
You are setting up a deep learning model for training on a multi-GPU cluster. You want to maximize training efficiency while maintaining model convergence.
Which of the following strategies is most effective in ensuring efficient multi-GPU training?
- A. Use Model Parallelism, where different layers of the model are placed on different GPUs to reduce communication overhead.
- B. Train each GPU independently on a different dataset to reduce the communication bottleneck.
- C. Reduce batch size to ensure each GPU receives only a small portion of the training data.
- D. Use Data Parallelism, where each GPU gets a different portion of the dataset, but gradients are averaged across all GPUs.
Correct Answer: D 🗳️
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