AI-102 Practice Question: Implement natural language processing solutions
You are designing a solution that must extract personally identifiable information (PII) from medical records stored in Azure Blob Storage. The solution must redact the PII before storing the results. Which combination of Azure services should you use?
⚠ Common exam trap
Many exam-takers confuse Text Analytics for Health (which is for medical entity extraction) with Azure AI Language's PII detection (which is for privacy compliance), leading them to choose Option D despite its lack of redaction capabilities.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Azure AI Language's PII detection feature and a custom Azure Function to redact.
Azure AI Language's PII detection feature is specifically designed to identify and categorize PII entities in text, and combining it with a custom Azure Function allows you to programmatically redact those entities before storing the results in Blob Storage. This provides a serverless, scalable pipeline that meets the requirement of extracting and redacting PII from medical records without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Azure AI Language's PII detection feature and a custom Azure Function to redact.
Why this is correct
Azure AI Language's PII detection returns entity offsets and categories, letting a custom Azure Function mask or replace each span before writing results back to Blob Storage. This satisfies the stem's redaction-before-storage constraint, since the Function performs the redaction step rather than relying on a service that only detects.
- ✗
Use Azure AI Search with cognitive skills for PII detection.
Why it's wrong here
Azure AI Search indexes and queries content; its cognitive skills enrich documents during indexing rather than redacting source records before storage. It is tempting because the built-in PII detection skill exists, but that skill calls Azure AI Language during an indexer run, so it suits searchable enrichment, not a pre-storage redaction stage.
- ✗
Use Azure OpenAI to detect and redact PII.
Why it's wrong here
Azure OpenAI generates text and cannot reliably enumerate or redact every PII entity across large record sets; it lacks the prebuilt PII entity categories and confidence scoring of Azure AI Language. It is tempting because GPT models can spot names in prose, but that suits summarisation or conversational extraction, not deterministic redaction pipelines.
- ✗
Use Text Analytics for Health and then manually redact.
Why it's wrong here
Text Analytics for Health returns entities and relations but performs no redaction, so manual post-processing is required and cannot guarantee complete PII removal. It suits clinical entity extraction where downstream code handles masking, not an automated redaction pipeline.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.