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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation | 17% | - Data validation and quality assurance - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation |
| Topic 2: Data Manipulation and Software Literacy | 19% | - Dependency management and containerization - Performance profiling and optimization tools - GPU-accelerated ETL workflows - Data processing libraries selection and usage |
| Topic 3: MLOps | 19% | - Monitoring, logging and maintenance - Model deployment and serving - Pipeline automation and orchestration - End-to-end workflow management |
| Topic 4: Data Analysis | 14% | - Data visualization and graph analytics - Distributed and parallel data processing - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) |
| Topic 5: GPU and Cloud Computing | 16% | - Cloud GPU environments and deployment - Resource management and scaling strategies - CRISP-DM and data science methodology - GPU architecture and acceleration principles |
| Topic 6: Machine Learning | 15% | - Distributed training strategies - Model evaluation and validation - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms |
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NEW QUESTION # 222
You are working with cloud-based GPUs to process a large dataset (terabytes in size) stored in Parquet format. One column represents a unique identifier (e.g., product ID), and it contains only positive integers ranging from 1 to 100,000.
Which of the following data types provides the best balance of memory efficiency and performance?
Answer: B
NEW QUESTION # 223
Your data science team is performing exploratory data analysis (EDA) on a large GPU-accelerated environment using cuDF and Dask-cuDF. During analysis, queries on categorical columns are performing poorly.
Which approach will most effectively improve query performance for categorical data in GPU-accelerated DataFrames?
Answer: B
NEW QUESTION # 224
You are processing a large-scale transportation network graph using NVIDIA cuGraph. The graph is extremely large, consuming almost all available GPU memory. Performance is deteriorating, and some computations fail due to memory exhaustion.
What is the best approach to efficiently handle this large graph while keeping computations on the GPU?
Answer: B
NEW QUESTION # 225
You are working with cuGraph to analyze a large social network dataset where users are represented as nodes, and their connections (friendships) are represented as edges. You need to determine the most efficient way to store and process the graph in cuGraph for high-performance analytics.
Which of the following graph representations is best suited for efficient processing in cuGraph?
Answer: C
NEW QUESTION # 226
You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.
Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?
Answer: C
NEW QUESTION # 227
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