Implement knowledge mining and information extraction solutions →hardMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
You are designing a knowledge mining solution using Azure AI Search. The solution must process large volumes of PDFs daily. You need to minimize the cost of cognitive skills execution while ensuring the pipeline can handle transient failures. Which approach should you recommend?
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
✓
Enable incremental enrichment on the indexer
Enabling incremental enrichment caches intermediate results and recovers from failures without re-processing unchanged documents, reducing cost. Option B is incorrect because disabling field mappings would break the pipeline. Option C is incorrect because increasing the number of replicas improves query performance, not indexing. Option D is incorrect because using a free tier is not feasible for large volumes.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable incremental enrichment on the indexer
Why this is correct
Incremental enrichment caches skill outputs, so on failure only changed documents are reprocessed, saving cost.
- ✗
Disable field mappings
Why it's wrong here
Disabling field mappings would break the data flow.
- ✗
Increase the number of replicas
Why it's wrong here
Replicas improve query throughput, not indexing resilience.
- ✗
Use the free tier for the indexer
Why it's wrong here
Free tier has very limited capacity and is not suitable for large volumes.
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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.