4.1Standard
Use customer data for summarization, search, and logs.
Find exposure points along the flow.
Choose two analysis steps.
× Incorrect
Map collection, transfer, inference, retrieval, logs, cache, output, and deletion, then assess reidentification, memory, sharing, misdelivery, and over-retention.
Detailed explanation
IncorrectAnonymization removes the need to check logs and output
Other stages can expose data.
IncorrectInternal use has no privacy threats
Insider error and compromise remain possible.
CorrectAssess reidentification, memory, oversharing, misdelivery, and excessive retention
AI-specific privacy risks are covered.
CorrectMap collection, transfer, inference, retrieval, logs, cache, output, and deletion boundaries
Data flow and exposure points are visible.
Check official documentation
Compare the following points with the official documentation.
AWS公式AIF-C01 Domain 4.1のプライバシー、データフロー、リスクを確認する。
Official references
What to confirm
AIデータの全処理段階にあるプライバシー露出点を分析できる。
Key points
- Threat model
- Reidentification
- Data flow
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Compare the definition with the AIF-C01 objective and the service documentation before applying it.
- Keep the concept separate from a particular answer choice or implementation step.
Foundation review
Privacy threat model
A structured analysis of personal-data flows, exposure paths, misuse, and possible mitigations.
Re-identification
Linking anonymized or pseudonymous data with other information to identify a person.
Question IDAWS-AIF-382
EnvironmentAWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
Last technical review2026-08-20
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