Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| 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% | - Data processing libraries selection and usage - GPU-accelerated ETL workflows - Dependency management and containerization - Performance profiling and optimization tools |
| GPU and Cloud Computing | 16% | - Resource management and scaling strategies - GPU architecture and acceleration principles - Cloud GPU environments and deployment - CRISP-DM and data science methodology |
| Data Preparation | 17% | - Data cleaning, preprocessing and transformation - Data validation and quality assurance - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization |
| MLOps | 19% | - Pipeline automation and orchestration - End-to-end workflow management - Monitoring, logging and maintenance - Model deployment and serving |
| Data Analysis | 14% | - Data visualization and graph analytics - Time-series analysis and anomaly detection - Distributed and parallel data processing - Exploratory Data Analysis (EDA) |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
When deciding whether to use GPU acceleration or a traditional CPU approach for a machine learning task, which of the following factors should be considered to determine if the data qualifies as "big data" and whether GPU acceleration is beneficial? (Select two)
- A. CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.
- B. GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.
- C. The complexity of the algorithm being used plays a crucial role in deciding whether to use GPU acceleration, with more complex algorithms benefiting from parallel computation.
- D. The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.
- E. The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.
Correct Answer: B,C 🗳️
You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?
- A. Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.
- B. Install NVIDIA drivers manually inside a Docker container every time it runs.
- C. Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.
- D. Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.
Correct Answer: D 🗳️
You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA- accelerated library should you use to achieve optimal performance?
- A. cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.
- B. NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.
- C. cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.
- D. Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.
Correct Answer: C 🗳️
You are working on a machine learning pipeline using NVIDIA RAPIDS cuML and need to standardize the dataset to ensure that all features have a mean of 0 and a standard deviation of 1.
Which of the following methods should you use to achieve this in cuML?
- A. cuml.preprocessing.OneHotEncoder()
- B. cuml.preprocessing.LabelEncoder()
- C. cuml.preprocessing.MinMaxScaler()
- D. cuml.preprocessing.StandardScaler()
Correct Answer: D 🗳️
You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?
- A. Use DLProf's Tensor Core Analysis to check if the model is leveraging Tensor Cores effectively.
- B. Increase the batch size to fully utilize available GPU memory and reduce per-sample processing overhead.
- C. Enable mixed precision training to improve computational efficiency.
- D. Reduce the number of layers in the model to decrease computation time.
Correct Answer: A 🗳️
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