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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: MLOps | 19% | - Pipeline automation and orchestration - Model deployment and serving - End-to-end workflow management - Monitoring, logging and maintenance |
| Topic 2: Data Preparation | 17% | - Data cleaning, preprocessing and transformation - Data validation and quality assurance - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization |
| Topic 3: Machine Learning | 15% | - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Distributed training strategies |
| 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% | - CRISP-DM and data science methodology - Cloud GPU environments and deployment - GPU architecture and acceleration principles - Resource management and scaling strategies |
| Topic 6: Data Manipulation and Software Literacy | 19% | - Performance profiling and optimization tools - Data processing libraries selection and usage - GPU-accelerated ETL workflows - Dependency management and containerization |
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NEW QUESTION # 127
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 # 128
You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)
Answer: C,D
NEW QUESTION # 129
You are working with a time-series dataset containing network traffic logs. You suspect the presence of anomalies, such as distributed denial-of-service (DDoS) attacks or sudden traffic surges.
Which machine learning approach is best suited for detecting these anomalies?
Answer: C
NEW QUESTION # 130
A data scientist is working with a dataset of sensor readings (temperature, pressure, vibration) in different scales and units. To ensure all features contribute equally to a machine learning model, the data needs to be standardized.
Which approach is best for standardizing numerical features?
Answer: C
NEW QUESTION # 131
You need to determine the optimal data processing library for a small dataset of 500,000 records that will be processed on a multi-core CPU machine with no GPU access.
Which of the following libraries would be the most efficient for this task?
Answer: A
NEW QUESTION # 132
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