AI-200 Data Management Services And Vector Search Practice Question
Which THREE data types or formats are commonly used to represent embeddings when preparing data for storage in a vector-enabled Azure service?
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
✓
JSON arrays.
Embeddings are typically represented as arrays of floats, stored as JSON lists, or exported from models like Azure OpenAI as numerical vectors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
JSON arrays.
Why this is correct
JSON arrays are the standard way to transport vector data to Azure services.
- ✓
Arrays of floats (single precision).
Why this is correct
Single-precision floating-point arrays are the standard format for vector embeddings.
- ✓
List of floating-point numbers (floats).
Why this is correct
Vector databases store embeddings as arrays of floating-point numbers.
- ✗
Base64 encoded binary image data.
Why it's wrong here
Binary image data is not a vector representation for similarity search.
- ✗
Vectorized text sequences (token IDs).
Why it's wrong here
Token IDs are not the same as vector embeddings used for semantic search.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
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