AI-200 Data Management Services And Vector Search Practice Question
When migrating data into Azure AI Search for vector search, you encounter issues with ingestion. Which TWO of the following are valid reasons why an indexer might fail to ingest vector data?
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
✓
The vector dimensions in the source data exceed the index field's configured dimensions.
Indexer failures often stem from schema mismatches (e.g., incorrect field type) or capacity issues (e.g., exceeding the maximum allowable dimensions for a vector).
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The vector dimensions in the source data exceed the index field's configured dimensions.
Why this is correct
Mismatching dimensions will cause the indexing process to fail.
- ✗
The embedding model used is deprecated.
Why it's wrong here
While this affects application output, the indexer itself just stores the resulting array, so it wouldn't fail the ingestion.
- ✗
The 'Collection(Edm.Single)' field is marked as 'filterable'.
Why it's wrong here
Vector fields can be filterable; this is not a cause for failure.
- ✓
The source document contains a vector array with an incorrect field type (e.g., array of strings).
Why this is correct
The index expects a specific array of numbers; incompatible types cause ingestion errors.
- ✗
The indexer is not configured with an 'AzureOpenAI' skill.
Why it's wrong here
An indexer can ingest pre-computed vectors without needing an enrichment skill.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-200 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-200 exam.