Try NVIDIA : NCP-ADS valid & accurate questions and answers
Last Updated: Oct 22, 2026
No. of Questions: 303 Questions & Answers with Testing Engine
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| MLOps | 19% | - Deployment and Monitoring
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Machine Learning | 15% | - Model Development and Optimization
|
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NVIDIA states the following prerequisites for the NVIDIA-Certified-Professional Accelerated Data Science: 2–3 years of experience in accelerated data science, machine learning, and GPU computing.
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According to the latest exam information, the NCP-ADS exam contains 60-70 questions and lasts 120 minutes minutes. Simulating those conditions at home with the PC test engine is excellent preparation for the real environment.
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Which of the following tools can be used for profiling deep learning models to identify performance bottlenecks and optimize execution on NVIDIA GPUs? (Select two)
- A. DLProf
- B. Python's cProfile
- C. TensorBoard
- D. NVIDIA Nsight Systems
Correct Answer: A,D 🗳️
You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?
- A. Convert the dataset to a NumPy array and manually replace missing values with the mean
- B. Use fillna() with a fixed value on the GPU using cuDF
- C. Apply a deep learning-based imputation model before moving data to the GPU
- D. Drop all rows containing missing values using Pandas before transferring data to the GPU
Correct Answer: B 🗳️
You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?
- A. The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.
- B. The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.
- C. The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
- D. The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.
Correct Answer: A 🗳️
Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?
- A. Model Building
- B. Data Understanding
- C. Data Preparation
- D. Business Understanding
Correct Answer: D 🗳️
You are working on a machine learning project that requires selecting the optimal data types for each feature in your dataset to maximize performance and efficiency in an MLOps pipeline.
Which of the following data types is most suitable for GPU-accelerated machine learning workflows when working with large datasets on NVIDIA platforms?
- A. Int32
- B. Float64
- C. String
- D. Float32
Correct Answer: D 🗳️
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