NVIDIA-Certified-Professional Accelerated Data Science : NCP-ADS

Exam Code: NCP-ADS

Exam Name: NVIDIA-Certified-Professional Accelerated Data Science

Updated: Sep 12, 2026

Q & A: 303 Questions and Answers

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Performance benchmarking and optimization 3. Memory and capacity evaluation
Topic 2: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. Distributed data processing frameworks (Dask) 3. GPU-accelerated ETL design and implementation
Topic 3: Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Multi-GPU training comparison 3. Feature engineering 4. Memory optimization techniques (mixed precision, batching)
Topic 4: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Use cuGraph for graph analytics 2. Perform time series analysis and visualization 3. Detect anomalies in time series datasets
Topic 5: Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing
Topic 6: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. CRISP-DM workflow execution 2. Docker and Conda environment management 3. Benchmarking GPU workflows

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1

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. Selecting the most appropriate machine learning algorithm for the prediction task.
B. Acquiring and exploring the dataset to assess quality, completeness, and potential biases.
C. Deploying the model into a production environment for real-time inference.
D. Identifying and preparing feature engineering strategies to improve model accuracy.

Answer: B

Question #2

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. Load data directly into NumPy arrays before using RAPIDS cuDF for processing.
B. Convert datasets into CSV format and store them in local disk storage.
C. Store data in Parquet format and load it using cuDF in RAPIDS.
D. Use JSON format for easy readability and process it directly in cuDF.

Answer: C

Question #3

You are tasked with selecting the optimal data processing library for an AI project that involves handling varying dataset sizes. The project must be flexible enough to scale from small datasets (a few GBs) to large datasets (hundreds of GBs or more) using NVIDIA technologies.
Which of the following libraries would you choose for optimal performance at both small and large scales?

A. cuDF
B. Dask
C. CUDA Toolkit
D. PyTorch

Answer: A

Question #4

When using cuDF from NVIDIA RAPIDS for GPU-accelerated data manipulation, which of the following operations will not efficiently utilize the GPU?

A. Merging two large datasets on a common column.
B. Filtering a large dataset based on a condition (e.g., column > 1000).
C. Applying a Python lambda function row-wise to a DataFrame.
D. Performing a group-by operation followed by an aggregation.

Answer: C

Question #5

You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A. Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.
B. Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.
C. Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.
D. Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.

Answer: C

NVIDIA-Certified-Professional Accelerated Data Science : NCP-ADS

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