Exam AI-200 Topic 1 Question 128 Discussion

Actual exam question for Microsoft's AI-200 exam
Question #: 128
Topic #: 1
You are implementing semantic retrieval for a chatbot.
Embeddings are already stored in Redis. However, vector similarity queries do not return matches.
You need to resolve the vector similarity search issue.
What should you do?

Suggested Answer: C Vote an answer

Creating a FLAT vector index on your embedding field will resolve the issue and allow your Redis vector similarity queries to return matches.
In Redis (using the Redis Search and Query features), vector fields cannot be queried using Vector Similarity Search (VSS) syntax until a dedicated vector index is explicitly built over them.
No Automatic Indexing: Redis does not automatically index JSON or Hash fields containing raw binary or string embeddings.
Query Failure: Without an index, VSS queries (using the KNN operator) will fail with syntax errors or return zero results because the query engine cannot parse the unindexed field.
Reference:
https://www.louisbouchard.ai/indexing-methods/

by Renee at Oct 08, 2026, 05:39 AM

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