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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| MLOps | 19% | - Pipeline automation and orchestration - Model deployment and serving - Monitoring, logging and maintenance - End-to-end workflow management |
| Machine Learning | 15% | - Model evaluation and validation - Model training and hyperparameter tuning - Distributed training strategies - GPU-accelerated ML frameworks and algorithms |
| 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 |
| Data Analysis | 14% | - Time-series analysis and anomaly detection - Data visualization and graph analytics - Distributed and parallel data processing - Exploratory Data Analysis (EDA) |
| Data Preparation | 17% | - Data cleaning, preprocessing and transformation - Data validation and quality assurance - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification |
| GPU and Cloud Computing | 16% | - Cloud GPU environments and deployment - CRISP-DM and data science methodology - Resource management and scaling strategies - GPU architecture and acceleration principles |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?
- A. Using only virtual environments (venv) without managing GPU dependencies separately
- B. Manually installing each package and its dependencies using pip
- C. Using a single system-wide installation of CUDA and forcing all projects to use the same version
- D. Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies
Correct Answer: D 🗳️
In the CRISP-DM process, which NVIDIA technology is commonly used during the "Data Preparation" phase to handle large datasets efficiently?
- A. NVIDIA CUDA Toolkit
- B. NVIDIA Jetson
- C. NVIDIA RAPIDS
- D. NVIDIA Deep Learning AI
Correct Answer: C 🗳️
You are implementing a Dask-based solution for distributed data parallelism across a multi-GPU system.
Which configuration steps would ensure effective use of GPUs for parallel computation? (Select two)
- A. Use dask_cudf to convert DataFrame computations into GPU-accelerated operations using cuDF
- B. Use dask_cuda's LocalCUDACluster with proper GPU memory management to handle multiple GPUs
- C. Create a LocalCUDACluster and manually specify the GPUs you want to use for each Dask worker
- D. Use Dask's Cluster class with the distributed scheduler and specify CPU cores only for GPU workloads
- E. Use dask_cuda's LocalCUDACluster and let Dask automatically allocate GPUs without any configuration
Correct Answer: A,B 🗳️
You have a multi-GPU cluster running RAPIDS with Dask to process a large dataset stored in Apache Parquet format. During execution, you notice some GPUs are underutilized, while others are overloaded, leading to uneven processing times.
What is the most effective way to balance the workload across GPUs?
- A. Split the dataset into smaller chunks manually and assign them to GPUs
- B. Use Dask's adaptive scaling to dynamically adjust the number of GPU workers
- C. Switch to a CPU-based framework like Spark to distribute the load evenly
- D. Increase the number of worker threads per GPU manually
Correct Answer: B 🗳️
You are considering using a multi-GPU setup to accelerate training a large deep learning model.
Which of the following are important factors to consider when deciding whether to use single-GPU or multi-GPU training? (Select two)
- A. The success of multi-GPU training depends heavily on the ability to increase the batch size without exceeding memory limits.
- B. Multi-GPU setups require proper load balancing and efficient gradient synchronization, as uneven distribution of work can lead to suboptimal performance.
- C. The performance of multi-GPU training scales linearly with the number of GPUs, meaning adding more GPUs will always result in a proportional reduction in training time.
- D. The increase in training time from using a multi-GPU setup is negligible, as the overhead from communication and synchronization is minimal.
Correct Answer: A,B 🗳️
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