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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 15% | - Model Development and Optimization
|
| MLOps | 19% | - Deployment and Monitoring
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?
- A. Increasing the batch size beyond the GPU's memory limit
- B. Disabling cuDNN optimizations to allow more flexibility in kernel execution
- C. Using mixed precision training with NVIDIA Tensor Cores
- D. Using only CPU-based data augmentation to keep the GPU dedicated to training
You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?
- A. Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.
- B. Split the dataset into multiple files based on a logical partitioning key, such as a date column.
- C. Store the entire dataset in a single large file to minimize the number of files in the directory.
- D. Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?
- A. Compiling all dependencies into the host machine and using system-wide installations
- B. Running the model in a local Python virtual environment and copying dependencies manually
- C. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
- D. Using Docker without GPU support and relying on CPU fallback when running TensorFlow
You have a massive time-series dataset containing millions of records per day, and you need to perform forecasting at scale.
Which of the following techniques best utilizes NVIDIA technologies to optimize time-series forecasting?
- A. Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.
- B. Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
- C. Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.
- D. Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.
You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?
- A. RAPIDS + Dask for distributed GPU-accelerated ETL
- B. Apache Spark with its default CPU-based execution
- C. Hadoop MapReduce
- D. Pandas with multiprocessing
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