Foundation Model Integration, Data Management, and Compliance
Data Validation and Multimodal Processing
Build validated ingestion and transformation pipelines that normalize text, image, audio, and structured inputs into model-specific formats while preserving quality, lineage, and compliance controls.
Concepts
- Validate completeness, schema, encoding, duplication, and quality before data reaches an embedding or inference workflow.
- Use specialized extraction and normalization stages for documents, speech, images, and tabular sources.
- Preserve source identifiers and transformation metadata so generated answers can be traced to governed inputs.
- Quarantine failed records and publish quality metrics instead of silently passing malformed content downstream.
Exam tips
- AWS Glue Data Quality is suited to managed data-quality rules and results.
- SageMaker Data Wrangler supports visual data preparation; Lambda is useful for focused custom normalization.
- Model request payloads must follow each provider model’s required schema.