AI-102 Practice Question: Implement knowledge mining and document intelligence solutions
A logistics company uses an Azure AI Search indexer to process bills of lading stored in Azure Blob Storage. The indexer uses a skillset with a ShaperSkill that builds a complex object named 'shipment' containing nested fields for carrier, origin, and destination. After a full index run, queries for the carrier field return no results even though the source documents contain the data. You need to make the carrier value searchable. What should you do?
⚠ Common exam trap
The trap here is believing that shaping an object automatically indexes its members, when shaped nodes must be explicitly mapped to index fields.
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
✓
Add an outputFieldMapping that maps the nested carrier value to a top-level index field.
The ShaperSkill constructs a complex object inside the enrichment tree, but that tree is transient. Persisting any part of it requires an outputFieldMapping from the enrichment node to a field in the index. Mapping the nested carrier node to a searchable top-level field is what actually makes the value appear in query results.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the ShaperSkill to output a string instead of an object.
Why it's wrong here
The ShaperSkill is designed to build complex types from multiple inputs, and changing its output type would not automatically persist the carrier value. The real issue is that shaped outputs are not written to the index unless an output field mapping targets an index field. Altering the shape alone would still leave the value unsearchable.
- ✗
Increase the indexer batch size so that all documents are processed in a single run.
Why it's wrong here
Batch size controls how many documents are read per invocation, not whether enrichment outputs are persisted. The carrier value is missing from the index because no field mapping writes it there, so changing batch size leaves the query results empty. Larger batches may even increase memory pressure without fixing the mapping gap.
- ✗
Enable the indexer's cache and rerun the indexer.
Why it's wrong here
Enrichment caching stores intermediate skill outputs so that unchanged documents are not reprocessed, reducing cost. It does not create index fields or map enrichment nodes to them. Rerunning with caching enabled would reuse the same enrichment tree and still produce no carrier values in the index.
- ✓
Add an outputFieldMapping that maps the nested carrier value to a top-level index field.
Why this is correct
Values produced inside a ShaperSkill object exist only in the enrichment tree. To persist them in the index, the indexer needs an outputFieldMapping that targets a field defined in the index. Mapping the nested carrier node to a searchable top-level field makes the value retrievable and queryable, resolving the empty query results.
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 |
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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.