AIP-C01 practice question: Processing images, audio, documents and tables for FMs (Bedrock Data Automation, Transcribe)
Foundation Model Integration, Data Management, and Compliance A free question from our original AIP-C01 practice exam. Try it, then open the answer to see why each option is right or wrong.
A property insurer receives three kinds of claim material: scanned PDF claim forms with tables, photos of storm damage, and recorded phone calls with policyholders.
The claims system needs the same core fields from all three, such as policy number, incident date, amount claimed and damage category, in one JSON format that downstream systems can read. The team has only two developers and wants to write as little code as possible.
New file types are rare, but the fields must be extracted from each file with a consistent schema.
Which solution meets these requirements with the LEAST custom code?
Choose one answer.
AA. Use Amazon Bedrock Data Automation with a blueprint for each material type, so that documents, images and audio each return structured JSON with the same core fields in S3.
BB. Run Amazon Textract on the PDF forms, Amazon Transcribe on the call recordings, and a custom computer vision model on SageMaker AI for the photos, then join the three outputs with an AWS Glue job.
CC. Send each file as raw bytes in a single prompt to a foundation model, and ask it to return the policy number, date, amount and damage category as JSON for every claim.
DD. Convert the PDFs to plain text with a script, transcribe the calls with Amazon Transcribe, and skip the photos, because the core claim fields can be filled from the forms and the calls.
Show the answer and explanation
Answer: A
A. Right: Bedrock Data Automation extracts structured output from documents, images, audio and video using blueprints, so the team writes configuration rather than extraction code. The consistent JSON output meets the schema and effort requirements. Learn more: Amazon Bedrock Data Automation (opens in a new tab)
B. Wrong: This approach can work, but it needs three extraction pipelines, a trained image model and a join job to maintain. That is far more code than the two developers can support, so it misses the effort requirement.
C. Wrong: Large audio files and multi-page scans may exceed what one request can carry, and output consistency would depend on the prompt. The team would have no reliable schema check, so the approach misses the consistency requirement.
D. Wrong: This option is simpler, but photos often show the damage category that the claims system needs. Dropping them means the required fields are incomplete for many claims, which breaks the schema requirement.
Explanation
Bedrock Data Automation handles documents, images and audio with blueprints that output consistent JSON, so the team configures rather than codes the extraction. The multi-service pipeline and the raw prompt approach each add code or risk, and skipping photos leaves required fields empty.
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