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NVIDIA NCP-ADS Q&A - in .pdf
- Exam Code: NCP-ADS
- Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
- Updated: Oct 21, 2026
- Q & A: 303 Questions and Answers
- Convenient, easy to study.
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- Updated: Oct 21, 2026
- Q & A: 303 Questions and Answers
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- Exam Code: NCP-ADS
- Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
- Updated: Oct 21, 2026
- Q & A: 303 Questions and Answers
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| GPU and Cloud Computing | 16% | - GPU architecture and fundamentals
|
| MLOps | 19% | - Model deployment and serving
|
| Data Preparation | 17% | - Data loading and preprocessing
|
| Machine Learning | 15% | - Model training with GPU acceleration
|
| Data Manipulation and Software Literacy | 19% | - Distributed computing with Dask
|
| Data Analysis | 14% | - Exploratory data analysis
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Question #1
You are conducting rapid experimentation on an NVIDIA GPU to determine the best trade-off between model accuracy and inference latency.
Which approach is the most efficient for systematically evaluating multiple configurations?
- A. Train each possible model variation from scratch to evaluate accuracy and performance differences
- B. Use automated hyperparameter tuning tools like Optuna or Ray Tune with mixed precision training
- C. Test different model configurations on a CPU first before moving to the GPU for final evaluation
- D. Reduce training epochs significantly to save time, even if the model is underfitting
Correct Answer: B 🗳️
Question #2
Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)
- A. Storing data directly on an HDD and processing with GPUs
- B. Using cuDF for GPU-accelerated DataFrame operations
- C. Leveraging Dask for distributed GPU-based parallel computing
- D. Using TensorFlow with custom data pipelines for data loading
Correct Answer: B,C 🗳️
Question #3
A data science team is using NVIDIA GPUs to accelerate their AI workflow for a fraud detection system. They follow the CRISP-DM methodology to ensure a structured approach to the project.
During the Data Preparation phase, what is the most effective way to leverage NVIDIA technologies?
- A. Manually clean and transform data using traditional CPU-based processing tools like pandas, then transfer the data to the GPU only for training.
- B. Ignore feature engineering and feed raw data directly into the model, relying on deep learning to extract relevant features.
- C. Use NVIDIA RAPIDS to preprocess large-scale transaction datasets efficiently on GPUs before training the model.
- D. Perform all data transformation on a single CPU core to ensure stability before deploying to GPU- accelerated training.
Correct Answer: C 🗳️
Question #4
You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?
- A. Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.
- B. Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
- C. Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.
- D. Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
Correct Answer: D 🗳️
Question #5
A machine learning engineer is working with a financial dataset that contains multiple numerical features, including income, loan amount, and transaction frequency. Some features are normally distributed, while others have a highly skewed distribution with extreme outliers.
Which of the following approaches best ensures uniformity across features before training a model?
- A. Apply log transformation to skewed features before standardizing them with z-score normalization
- B. Scale all numerical features using min-max normalization
- C. Remove outliers before applying standardization
- D. Use one-hot encoding to transform numerical features into categorical representations
Correct Answer: A 🗳️
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