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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: GPU and Cloud Computing | 16% | - Resource management and scaling strategies - Cloud GPU environments and deployment - GPU architecture and acceleration principles - CRISP-DM and data science methodology |
| Topic 2: Data Manipulation and Software Literacy | 19% | - Data processing libraries selection and usage - Performance profiling and optimization tools - GPU-accelerated ETL workflows - Dependency management and containerization |
| Topic 3: Machine Learning | 15% | - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Distributed training strategies - Model training and hyperparameter tuning |
| Topic 4: Data Preparation | 17% | - Data validation and quality assurance - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization |
| Topic 5: Data Analysis | 14% | - Data visualization and graph analytics - Distributed and parallel data processing - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection |
| Topic 6: MLOps | 19% | - Model deployment and serving - Pipeline automation and orchestration - End-to-end workflow management - Monitoring, logging and maintenance |
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NEW QUESTION # 67
A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?
Answer: A
NEW QUESTION # 68
A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?
Answer: B
NEW QUESTION # 69
Which of the following Nvidia technologies is commonly used for deploying machine learning models in production environments, enabling scalable deployment and monitoring?
Answer: D
NEW QUESTION # 70
Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?
Answer: D
NEW QUESTION # 71
You are optimizing a deep learning model that runs on an NVIDIA GPU and notice that inference latency is unexpectedly high. You decide to use DLProf to analyze the model's execution profile. After running the profiler, you find that a significant portion of execution time is spent on a single GPU kernel.
Which of the following actions would best help you identify and optimize this performance bottleneck?
Answer: B
NEW QUESTION # 72
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