Implement knowledge mining and information extraction solutions →hardMultiple SelectObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Which THREE components are required to build a knowledge mining solution using Azure AI Search that extracts and enriches content from PDF files?
⚠ Common exam trap
The trap here is that candidates often include the index as a required component or overlook the indexer. However, the three core pipeline components are data source, skillset, and indexer. The index is a separate entity that is created as part of the solution but is not one of the three pipeline components asked for.
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
✓
Skillset
To build a knowledge mining solution that extracts and enriches content from PDF files using Azure AI Search, three components are required: a data source (to connect to the PDFs), a skillset (to apply AI enrichment like OCR and entity extraction), and an indexer (to orchestrate the pipeline and ingest the enriched content into the index). While an index is also necessary to store the results, it is not considered a pipeline component in the same context, and the question asks for the three components of the extraction and enrichment pipeline.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Index
Why it's wrong here
An index is necessary to store the searchable results, but it is not one of the three pipeline components required for extraction and enrichment. The question asks for the components that extract and enrich, which are data source, skillset, and indexer.
- ✓
Skillset
Why this is correct
Correct. A skillset defines the AI enrichment steps such as OCR, entity recognition, or language detection that transform the raw PDF content into enriched data.
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Semantic configuration
Why it's wrong here
Incorrect. Semantic configuration is an optional feature for improving search relevance, not a required component for extraction and enrichment.
- ✓
Data source
Why this is correct
Correct. A data source provides the connection information and credentials to access the PDF files stored in Azure Blob Storage or other locations.
- ✓
Indexer
Why this is correct
Correct. An indexer orchestrates the pipeline by connecting the data source, applying the skillset, and loading the enriched content into the index.
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