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 17, 2026
- No. of Questions: 303 Questions & Answers with Testing Engine
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| MLOps | 19% | - Model deployment and serving - Monitoring, logging and maintenance - Pipeline automation and orchestration - End-to-end workflow management |
| Data Analysis | 14% | - Distributed and parallel data processing - Data visualization and graph analytics - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) |
| GPU and Cloud Computing | 16% | - GPU architecture and acceleration principles - Resource management and scaling strategies - CRISP-DM and data science methodology - Cloud GPU environments and deployment |
| Data Preparation | 17% | - Workflow monitoring and bottleneck identification - Data validation and quality assurance - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization |
| Machine Learning | 15% | - Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning - Model evaluation and validation |
| Data Manipulation and Software Literacy | 19% | - Dependency management and containerization - Performance profiling and optimization tools - GPU-accelerated ETL workflows - Data processing libraries selection and usage |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?
- A. Increase batch size to improve accuracy
- B. Automatically adjust the learning rate based on the model's convergence
- C. Modify the training dataset during model execution
- D. Visualize GPU memory utilization over time
Correct Answer: D 🗳️
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 🗳️
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 is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?
- A. Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually
- B. Use Dask's single-threaded scheduler to avoid multi-threading overhead
- C. Transfer data between GPUs using NumPy's .copy() function
- D. Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster
Correct Answer: D 🗳️
You are working on a financial dataset that tracks stock prices over time, and you need to detect anomalies such as sudden spikes or drops using NVIDIA technologies.
Which of the following approaches would be the most effective for anomaly detection in a time-series dataset using NVIDIA's RAPIDS AI and TensorRT?
- A. Apply a traditional rule-based thresholding method using pandas and NumPy for detecting sudden spikes in stock prices.
- B. Use RAPIDS cuML's Isolation Forest for anomaly detection and deploy it with NVIDIA Triton Inference Server.
- C. Perform anomaly detection by applying DBSCAN clustering with RAPIDS cuML without any feature engineering.
- D. Use traditional ARIMA modeling with RAPIDS cuML to classify anomalies based on residual analysis.
Correct Answer: B 🗳️
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