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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis | 14% | - Data visualization and graph analytics - Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Time-series analysis and anomaly detection |
| Topic 2: Machine Learning | 15% | - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Model training and hyperparameter tuning - Distributed training strategies |
| Topic 3: MLOps | 19% | - End-to-end workflow management - Model deployment and serving - Pipeline automation and orchestration - Monitoring, logging and maintenance |
| Topic 4: GPU and Cloud Computing | 16% | - Cloud GPU environments and deployment - CRISP-DM and data science methodology - GPU architecture and acceleration principles - Resource management and scaling strategies |
| Topic 5: Data Preparation | 17% | - Data validation and quality assurance - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization |
| Topic 6: Data Manipulation and Software Literacy | 19% | - Dependency management and containerization - Performance profiling and optimization tools - Data processing libraries selection and usage - GPU-accelerated ETL workflows |
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NEW QUESTION # 139
You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?
Answer: A
NEW QUESTION # 140
You are processing a dataset of high-resolution images for deep learning training and want to optimize image loading, augmentation, and transformation on an NVIDIA GPU.
Which of the following statements correctly describes the role of NVIDIA DALI (Data Loading Library) in accelerating data preparation?
Answer: B
NEW QUESTION # 141
You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?
Answer: A
NEW QUESTION # 142
You are working with a dataset containing 2 billion rows of financial transactions, and you need to perform exploratory data analysis (EDA) before building a predictive model.
Which of the following approaches is the most appropriate for handling this data efficiently?
Answer: A
NEW QUESTION # 143
When using cuDF from NVIDIA RAPIDS for GPU-accelerated data manipulation, which of the following operations will not efficiently utilize the GPU?
Answer: D
NEW QUESTION # 144
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