AI-102 Practice Question: Implement knowledge mining and information extraction solutions
Which TWO configurations are required to enable Azure AI Search to index content from an Azure SQL database?
⚠ Common exam trap
A common mix-up: candidates confuse optional enrichment features (like custom skillsets or semantic ranking) with mandatory infrastructure requirements for data ingestion in Azure AI Search, leading them to select those options instead of the core connectivity and change tracking configurations.
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 change tracking on the Azure SQL table
Option C is correct because Azure AI Search's SQL indexer relies on change tracking (or a rowversion/timestamp column) to detect which rows have been inserted, updated, or deleted since the last indexing run, so enabling change tracking on the Azure SQL table is required for incremental indexing. Option D is correct because the indexer must be given a data source object that specifies the connection string, table or view, and change-tracking policy for the Azure SQL database, which is the mandatory link between the search service and the SQL data. Option A is not required because a skillset is only needed for AI enrichment (for example OCR, key phrase extraction, or embedding generation), not for basic SQL-to-index ingestion. Option B is not required because semantic ranking is an optional query-time feature that improves relevance; it does not affect whether content can be indexed. Option E is not required because high availability is a resilience/uptime configuration for the SQL database and is unrelated to the indexer's ability to read and index data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a custom skillset for data enrichment
Why it's wrong here
A custom skillset enriches already-retrieved documents with AI-generated fields; it does not establish the SQL data source or index schema, so indexing never starts. It is tempting because enrichment adds value, and would be correct when extracted content needs translation, OCR or entity recognition during indexing.
- ✗
Configure semantic ranking on the index
Why it's wrong here
Semantic ranking reorders results using language understanding; it neither connects to Azure SQL nor defines the data source, so indexing cannot begin. It is tempting because it improves relevance scoring, and would be the right choice when query result quality on an existing index needs lifting.
- ✓
Enable change tracking on the Azure SQL table
Why this is correct
Change tracking lets the indexer detect which rows were inserted, updated, or deleted since the last run, so incremental re-indexing stays accurate without full reloads. Without it, the SQL indexer cannot identify changed rows, and the required high-water mark column for incremental indexing is unavailable.
- ✓
Define a data source connection to the Azure SQL database
Why this is correct
The indexer needs a data source object holding the Azure SQL connection string and credentials, plus the table or view to read. This satisfies the requirement that Azure AI Search can reach and query the source database before any indexing run can begin.
- ✗
Enable high availability on the Azure SQL database
Why it's wrong here
High availability replicates the database for resilience; it has no bearing on whether Azure AI Search can read and index its content. It is tempting because HA is a common Azure SQL configuration, and would be correct when the requirement is fault tolerance rather than search indexing.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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