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Last Updated: Sep 20, 2026
No. of Questions: 303 Questions & Answers with Testing Engine
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 15% | - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms - Distributed training strategies - Model evaluation and validation |
| Data Manipulation and Software Literacy | 19% | - GPU-accelerated ETL workflows - Performance profiling and optimization tools - Data processing libraries selection and usage - Dependency management and containerization |
| GPU and Cloud Computing | 16% | - Resource management and scaling strategies - Cloud GPU environments and deployment - CRISP-DM and data science methodology - GPU architecture and acceleration principles |
| MLOps | 19% | - Monitoring, logging and maintenance - Pipeline automation and orchestration - End-to-end workflow management - Model deployment and serving |
| Data Analysis | 14% | - Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Time-series analysis and anomaly detection - Data visualization and graph analytics |
| Data Preparation | 17% | - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Data validation and quality assurance |
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. Increase the number of CPU cores and distribute training across multiple CPU threads.
- B. Use a cloud instance with multiple GPUs and enable mixed-precision training.
- C. Disable CUDA and use only OpenMP to parallelize computations across CPU cores.
- D. Store all training data in RAM and load it directly to the CPU for processing.
Correct Answer: B 🗳️
A machine learning engineer is tasked with deploying a real-time image classification model as part of an MLOps pipeline. The model requires low-latency inference and must handle high-throughput requests efficiently.
Which of the following deployment strategies is the most suitable for leveraging GPU acceleration?
- A. Using an Apache Spark cluster with distributed CPU-based inference.
- B. Using a REST API wrapper to load the model dynamically into memory for each request.
- C. Deploying the model using NVIDIA Triton Inference Server with TensorRT optimizations.
- D. Running the model inference on a multi-core CPU server with batch processing enabled.
Correct Answer: C 🗳️
You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?
- A. Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.
- B. Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.
- C. Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.
- D. Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
Correct Answer: B 🗳️
A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?
- A. Deploy separate full-GPU instances for each workload, even if they have variable compute demands.
- B. Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.
- C. Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.
- D. Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
Correct Answer: C 🗳️
You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?
- A. df['sales'].convert_dtypes('int32')
- B. df['sales'] = df['sales'].to_numeric('int32')
- C. df['sales'] = df['sales'].astype('int32')
- D. df['sales'].apply(lambda x: int(x))
Correct Answer: C 🗳️
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