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Last Updated: Sep 13, 2026
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning | 15% | - Model Development and Optimization
|
| Topic 2: Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Topic 3: Data Preparation | 17% | - Data Cleaning and Transformation
|
| Topic 4: GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Topic 5: Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Topic 6: MLOps | 19% | - Deployment and Monitoring
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You have developed a deep learning model using TensorFlow and trained it on an NVIDIA A100 GPU. The model is deployed in production and serves real-time inference requests. However, the inference latency is high, and you need to optimize performance without retraining the model.
Which of the following approaches is the most effective for optimizing inference performance using NVIDIA technologies?
- A. Enable mixed precision training and retrain the model to improve inference speed.
- B. Implement data augmentation techniques to improve inference efficiency.
- C. Convert the model to ONNX format and use TensorRT for inference optimization.
- D. Reduce the batch size to decrease computational overhead and improve latency.
You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?
- A. Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.
- B. Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.
- C. Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
- D. Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
You are setting up a GPU-accelerated data science environment on a cloud-based instance that utilizes NVIDIA GPUs.
To ensure compatibility between CUDA, RAPIDS, and Python libraries, which of the following is the most effective approach for managing dependencies and avoiding version conflicts?
- A. Install RAPIDS, PyTorch, TensorFlow, and other libraries in the same Conda base environment for simplified access
- B. Use a virtual machine with all dependencies pre-installed to avoid any software conflicts
- C. Use Conda environments with version-pinned dependencies to create an isolated environment for RAPIDS and CUDA
- D. Manually install required libraries in a system-wide Python environment using pip install without version specifications
You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.
Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?
- A. RAPIDS
- B. TensorRT
- C. CUDA
- D. TensorFlow
You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?
- A. Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.
- B. Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.
- C. Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.
- D. Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
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