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| Section | Weight | Objectives |
|---|---|---|
| Data Analysis | 14% | - Time-series analysis
|
| Data Manipulation and Software Literacy | 19% | - GPU-accelerated data manipulation using cuDF
|
| GPU and Cloud Computing | 16% | - Cloud GPU environments
|
| MLOps | 19% | - Experiment tracking
|
| Data Preparation | 17% | - GPU-accelerated ETL workflows
|
| Machine Learning | 15% | - Model training with GPU acceleration
|
You are working on a large dataset (several terabytes in size) and need to perform data preprocessing, filtering, and transformations before training a machine learning model.
Given the dataset size and the requirement to optimize for GPU acceleration using NVIDIA technologies, which of the following is the most appropriate data processing library to use?
Correct Answer: B 🗳️
You are deploying a deep learning model on an edge device with 8GB of available RAM. The model's estimated peak memory usage, including model weights, intermediate tensors, and batch data, is 9.5GB.
What is the best course of action to ensure successful deployment while maintaining performance?
Correct Answer: A 🗳️
You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)
Correct Answer: C,E 🗳️
Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?
Correct Answer: B 🗳️
A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?
Correct Answer: D 🗳️
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