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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 |
| Topic 2: Data Preparation | 17% | - Feature engineering and data type optimization - Data validation and quality assurance - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation |
| Topic 3: MLOps | 19% | - Pipeline automation and orchestration - End-to-end workflow management - Model deployment and serving - Monitoring, logging and maintenance |
| Topic 4: Machine Learning | 15% | - Model evaluation and validation - Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning |
| Topic 5: Data Analysis | 14% | - Time-series analysis and anomaly detection - Distributed and parallel data processing - Exploratory Data Analysis (EDA) - Data visualization and graph analytics |
| Topic 6: GPU and Cloud Computing | 16% | - Resource management and scaling strategies - CRISP-DM and data science methodology - GPU architecture and acceleration principles - Cloud GPU environments and deployment |
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NEW QUESTION # 134
A machine learning team needs to process terabytes of image metadata stored in a distributed storage system. They want to leverage GPU acceleration to speed up preprocessing and transformation while ensuring efficient parallel access.
Which of the following approaches best aligns with NVIDIA's accelerated data science ecosystem?
Answer: A
NEW QUESTION # 135
A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.
To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?
Answer: A
NEW QUESTION # 136
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: D
NEW QUESTION # 137
A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?
Answer: C
NEW QUESTION # 138
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 # 139
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