AI-103 Practice Question 9: Conversation PII Redaction
Microsoft Azure AI Apps and Agents Developer Associate (AI-103) · Test 1 · question 9 of 10
A contact centre stores the previous day's chat and voice transcripts in an overnight job so analysts can study them. Compliance requires that customer names, phone numbers, and payment card numbers be masked before the transcripts land in the analytics store. The transcripts arrive as turn-based exchanges with speaker attribution, and masking decisions must take the turn boundaries and conversational context into account. Nobody has labelled any training data.
Which capability should the ingestion job call?
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Answer:
PII detection is grouped into feature types by the shape of the input. Conversation PII processes multi-turn exchanges and transcript-oriented payloads where turn boundaries and conversation context affect detection and masking behaviour, and it is the feature intended for asynchronous workloads over chat and transcript structures: contact centres, meetings, and voice transcripts. It uses predefined entity categories, so no labelled data is needed.
- A is wrong because custom NER requires exactly the labelled training data the team does not have, and it rebuilds recognizers for names, phone numbers, and card numbers that the predefined PII categories already cover. - C is wrong because Text PII is optimized for synchronous, string-based input such as messages, prompts, and log fields. Flattening a conversation into one string discards the turn structure that the stem says must inform masking. - D is wrong because Document-based PII targets native files (.pdf, .docx, .txt), and preserves document fidelity. Converting chat logs to PDF adds a lossy step purely to reach the wrong feature type.
Reference: What is the Personally Identifiable Information (PII) detection feature in Azure Language?