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| Section | Weight | Objectives |
|---|---|---|
| Data Analysis | 14% | - Data visualization and graph analytics - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) - Distributed and parallel data processing |
| Machine Learning | 15% | - Model training and hyperparameter tuning - Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation |
| Data Preparation | 17% | - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Data validation and quality assurance |
| GPU and Cloud Computing | 16% | - CRISP-DM and data science methodology - Resource management and scaling strategies - Cloud GPU environments and deployment - GPU architecture and acceleration principles |
| Data Manipulation and Software Literacy | 19% | - Dependency management and containerization - Data processing libraries selection and usage - GPU-accelerated ETL workflows - Performance profiling and optimization tools |
| MLOps | 19% | - Model deployment and serving - End-to-end workflow management - Pipeline automation and orchestration - Monitoring, logging and maintenance |
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NEW QUESTION # 71
You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?
Answer: A
NEW QUESTION # 72
A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?
Answer: C
NEW QUESTION # 73
Which of the following Nvidia technologies is commonly used for deploying machine learning models in production environments, enabling scalable deployment and monitoring?
Answer: A
NEW QUESTION # 74
Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?
Answer: D
NEW QUESTION # 75
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: D
NEW QUESTION # 76
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