NVIDIA NCP-ADS : NVIDIA-Certified-Professional Accelerated Data Science
- Exam Code: NCP-ADS
- Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
- Updated: Oct 09, 2026
- Q & A: 303 Questions and Answers
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Machine Learning | 15% | - Model Development and Optimization
|
| MLOps | 19% | - Deployment and Monitoring
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.
To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?
- A. Disable CUDA and use only OpenMP to parallelize computations across CPU cores.
- B. Use a cloud instance with multiple GPUs and enable mixed-precision training.
- C. Increase the number of CPU cores and distribute training across multiple CPU threads.
- D. Store all training data in RAM and load it directly to the CPU for processing.
Correct Answer: B 🗳️
You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?
- A. Acquiring and exploring the dataset to assess quality, completeness, and potential biases.
- B. Deploying the model into a production environment for real-time inference.
- C. Selecting the most appropriate machine learning algorithm for the prediction task.
- D. Identifying and preparing feature engineering strategies to improve model accuracy.
Correct Answer: A 🗳️
You are working with a deep learning model for real-time inference on an NVIDIA GPU. Your objective is to optimize inference speed while maintaining acceptable model accuracy.
Which of the following techniques provides the best balance between inference performance and accuracy?
- A. Use TensorRT for model optimization, including precision quantization and layer fusion
- B. Disable batch inference to ensure each sample is processed independently for better accuracy
- C. Use double-precision (FP64) computations to prevent numerical instability in inference
- D. Increase the number of neurons in each layer to improve the model's accuracy
Correct Answer: A 🗳️
You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?
- A. RAPIDS + Dask for distributed GPU-accelerated ETL
- B. Pandas with multiprocessing
- C. Apache Spark with its default CPU-based execution
- D. Hadoop MapReduce
Correct Answer: A 🗳️
You are analyzing a dataset containing millions of transaction records to detect fraudulent transactions. The dataset is highly imbalanced, with fraudulent cases making up only 0.5% of the data.
Which data analysis strategy will most effectively handle the imbalance while minimizing the risk of overfitting?
- A. Stratified random sampling
- B. Random oversampling of the minority class
- C. SMOTE (Synthetic Minority Over-sampling Technique)
- D. Random undersampling of the majority class
Correct Answer: D 🗳️
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