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, PrepPDF 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 11, 2026
- No. of Questions: 303 Questions & Answers with Testing Engine
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation | 17% | - Feature engineering and data type optimization - Data validation and quality assurance - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification |
| Topic 2: Machine Learning | 15% | - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning - Distributed training strategies |
| Topic 3: MLOps | 19% | - End-to-end workflow management - Model deployment and serving - Pipeline automation and orchestration - Monitoring, logging and maintenance |
| Topic 4: Data Manipulation and Software Literacy | 19% | - GPU-accelerated ETL workflows - Dependency management and containerization - Data processing libraries selection and usage - Performance profiling and optimization tools |
| Topic 5: Data Analysis | 14% | - Time-series analysis and anomaly detection - Distributed and parallel data processing - Data visualization and graph analytics - Exploratory Data Analysis (EDA) |
| Topic 6: GPU and Cloud Computing | 16% | - Resource management and scaling strategies - CRISP-DM and data science methodology - GPU architecture and acceleration principles - Cloud GPU environments and deployment |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?
- A. Perform the join operation entirely in a CPU-based Spark environment for better stability
- B. Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
- C. Convert both DataFrames to Pandas before performing the join for compatibility
- D. Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF
Correct Answer: B 🗳️
You are working with a large dataset containing millions of rows, and you need to store it efficiently for fast read and write operations while maintaining compatibility with CuDF and pandas.
Which of the following file formats is the best choice for efficient columnar storage and GPU-accelerated processing?
- A. Parquet
- B. TXT
- C. CSV
- D. JSON
Correct Answer: A 🗳️
You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)
- A. Measure both GPU utilization and network latency between the cloud platform and your data storage location.
- B. Limit the benchmarking tests to a single model architecture for each cloud platform.
- C. Run each model on the same GPU type and instance size across both cloud platforms.
- D. Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.
Correct Answer: A,C 🗳️
A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?
- A. Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.
- B. Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
- C. Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.
- D. Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.
Correct Answer: C 🗳️
A machine learning engineer wants to accelerate the training of a large deep learning model while optimizing GPU utilization.
Which of the following strategies provides the best performance improvement using NVIDIA technologies?
- A. Disable mixed-precision training since it reduces numerical accuracy and does not significantly impact performance.
- B. Load all training data into GPU memory at once to minimize CPU-GPU data transfer time.
- C. Use NVIDIA DALI (Data Loading Library) to preprocess and augment data on the GPU before feeding it to the model.
- D. Use CPU-based data augmentation techniques before transferring the data to the GPU for training.
Correct Answer: C 🗳️
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