Domain 4. Data for AI

Task 1: Managing Data Fundamentals and Big Data Concepts

  • Explain how data fuels AI intelligence initiatives
  • Define Big Data and its relationship to AI systems
  • Extract value from unstructured data resources
  • Apply lessons learned from Big Data implementations
  • Evaluate the role of data science in analytics processes
  • Apply Big Data approaches to enhance AI capabilities

Task 2: Implementing Data Governance and Management

  • Design comprehensive data lifecycles for AI applications
  • Establish data stewardship roles and responsibilities
  • Establish data management plans for AI initiatives
  • Document data lineage throughout the AI pipeline
  • Implement master data management practices

Task 3: Engineering Data Pipelines for AI

  • Design data feed mechanisms for continuous data flow
  • Construct data pipelines optimized for AI workloads
  • Apply data engineering principles to AI infrastructure
  • Create separate training and inference data pipelines
  • Design scalable data architectures for growing datasets
  • Create automated documentation of data pipeline components

Task 4: Executing Data Preparation and Transformation

  • Validate the “garbage in, garbage out” principle in AI contexts
  • Apply methods to improve data quality and accuracy
  • Address AI-specific needs in data preparation
  • Clean and enhance data for optimal AI performance
  • Apply data augmentation to increase dataset robustness
  • Balance datasets to prevent bias in model training

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