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| Section | Weight | Objectives |
|---|---|---|
| GPU and Cloud Computing | 16% | - GPU architecture and acceleration principles - Cloud GPU environments and deployment - CRISP-DM and data science methodology - Resource management and scaling strategies |
| MLOps | 19% | - Pipeline automation and orchestration - Monitoring, logging and maintenance - End-to-end workflow management - Model deployment and serving |
| Data Preparation | 17% | - Feature engineering and data type optimization - Data cleaning, preprocessing and transformation - Data validation and quality assurance - Workflow monitoring and bottleneck identification |
| Data Manipulation and Software Literacy | 19% | - GPU-accelerated ETL workflows - Dependency management and containerization - Performance profiling and optimization tools - Data processing libraries selection and usage |
| Data Analysis | 14% | - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection - Distributed and parallel data processing - Data visualization and graph analytics |
| Machine Learning | 15% | - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning - Distributed training strategies |
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NEW QUESTION # 281
You have developed a deep learning model using TensorFlow and trained it on an NVIDIA A100 GPU. The model is deployed in production and serves real-time inference requests. However, the inference latency is high, and you need to optimize performance without retraining the model.
Which of the following approaches is the most effective for optimizing inference performance using NVIDIA technologies?
Answer: D
NEW QUESTION # 282
Which of the following methods are commonly used to handle missing data in data analysis? (Select two)
Answer: D,E
NEW QUESTION # 283
A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
Which of the following approaches would best help identify the source of the bottleneck?
Answer: B
NEW QUESTION # 284
You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
- Ingest and preprocess data efficiently
- Perform similarity computations for user-item recommendations
- Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?
Answer: D
NEW QUESTION # 285
A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?
Answer: A
NEW QUESTION # 286
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