AI-200 Data Management Services And Vector Search Practice Test 10 — 15 Questions

Question 1

When designing an Azure AI Search solution, you need to store high-dimensional embeddings. Which field type must be used to store these vector representations correctly?

Accepted Answer

Collection(Edm.Single). Azure AI Search stores vector embeddings in fields of type Collection(Edm.Single), which is a collection of single-precision floats. This matches the float32 output of models like text-embedding-ada-002 and is the required type for vector fields. The field must also have a vector search profile and dimensions defined.

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Edm.Double

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Edm.String

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Collection(Edm.Int32)

Question 2

You are configuring a Vector Index in Azure AI Search. You need to ensure that the vector similarity search is performed using the Cosine distance metric. Where is this configuration specified?

Accepted Answer

In the 'vectorSearch' section of the index definition.. In Azure AI Search, the vector search configuration, including the similarity metric such as cosine, dotProduct, or euclidean, is specified in the 'vectorSearch' section of the index definition. This section defines algorithms (like HNSW) and profiles that bind the metric to vector fields. The metric is set at the algorithm level and referenced by vector fields.

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In the indexer definition.

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In the search query payload.

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In the field definitions within the index.

Question 3

You need to ensure that your vector data in Azure AI Search is protected at rest. Which setting should you verify?

Accepted Answer

Configure Customer-Managed Keys (CMK) for the search service.. Customer-Managed Keys (CMK) in Azure AI Search provide encryption at rest for the service's data, including vector indexes, by allowing you to use your own Azure Key Vault key to encrypt the service's internal storage. This ensures that vector data is protected at rest with a key you control, meeting compliance requirements. The other options address in-transit encryption (TLS), network access, or a different service (Cosmos DB), not at-rest encryption for Azure AI Search.

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Enable TLS 1.3 on the search service.

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Disable public network access.

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Enable Always Encrypted on the Cosmos DB container.

Question 4

You are evaluating Cosmos DB for NoSQL vs. Azure AI Search for a vector search application. Your application requires strict ACID compliance for metadata updates along with vector search. Which should you choose and why?

Accepted Answer

Cosmos DB because it supports document-level ACID transactions.. Cosmos DB for NoSQL supports document-level ACID transactions via the transactional batch API, ensuring metadata updates are atomic and consistent. When the requirement combines strict ACID compliance for metadata with vector search, Cosmos DB is the correct choice because Azure AI Search does not provide ACID transactional guarantees for document updates. This aligns the service's transactional model with the application's consistency requirement.

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Cosmos DB because it is the only service that supports vectors.

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Azure AI Search because it supports better hybrid search.

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Azure AI Search because it uses HNSW for faster searches.

Question 5

You are designing a vector search solution using Azure AI Search. Which THREE of the following are necessary steps to configure an index to support vector search?

Accepted Answer

Define a 'vectorSearch' configuration within the index schema.. Option A is correct because the index schema must include a 'vectorSearch' configuration block that declares the vector search algorithms (e.g., HNSW or exhaustive KNN) and the profiles used by the index. Option C is correct because each vector field must reference a 'vectorSearchProfile' that ties the field to a specific algorithm and vectorizer configuration defined in the 'vectorSearch' section. Option E is correct because Azure AI Search represents vector embeddings as 'Collection(Edm.Single)', a collection of single-precision floats, which is the required data type for vector fields. Option B is incorrect because setting the index to 'ReadOnly' mode is unrelated to vector search configuration and would only prevent updates to the index. Option D is incorrect because 'Edm.String' is used for text fields, not vector fields, and would not store the numeric embedding arrays required for vector search.

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Set the index to 'ReadOnly' mode.

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Ensure all vector fields are set to 'Edm.String'.

Question 6

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?

Accepted Answer

The vector dimensions in the source data exceed the index field's configured dimensions.. Option A is correct because Azure AI Search validates vector length at ingestion time: if the source vector has more (or fewer) dimensions than the index field's configured 'dimensions' value, the indexer fails with a dimension mismatch error. Option D is correct because the vector field must be defined as Collection(Edm.Single) (or another supported vector type) and the source data must supply a numeric array; an array of strings or other non-numeric type cannot be cast to the vector field and causes ingestion to fail. Option B is not a valid reason because a deprecated embedding model still produces numeric vectors that the indexer can ingest; deprecation affects the model's availability, not the indexer's type/dimension validation. Option C is not a valid reason because marking a Collection(Edm.Single) field as 'filterable' is a supported configuration (though it consumes extra storage) and does not by itself cause ingestion failure. Option E is not a valid reason because an 'AzureOpenAI' skill is only needed to generate embeddings at index time; if the source already contains vectors, no such skill is required, and its absence does not cause an ingestion failure.

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The embedding model used is deprecated.

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The 'Collection(Edm.Single)' field is marked as 'filterable'.

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The indexer is not configured with an 'AzureOpenAI' skill.

Question 7

Which THREE features are common to both Azure AI Search and Azure Cosmos DB for NoSQL when performing vector search?

Accepted Answer

Ability to filter results based on non-vector document properties.. Option A is correct because both Azure AI Search (via OData filter expressions in the query) and Azure Cosmos DB for NoSQL (via a WHERE clause on non-vector fields in the VectorDistance query) allow pre-filtering results on scalar/non-vector document properties. Option C is correct because both services support HNSW (Hierarchical Navigable Small World) as an approximate nearest neighbor indexing algorithm for vector fields. Option E is correct because both platforms perform similarity search by computing vector distance metrics such as cosine, dot product, or Euclidean distance. Option B is incorrect because Semantic Ranker is a feature exclusive to Azure AI Search and is not available in Cosmos DB for NoSQL. Option D is incorrect because neither service automatically translates SQL into vector queries; Cosmos DB uses its own SQL-like syntax with the VectorDistance function, and Azure AI Search uses its own query DSL, so no such automatic translation exists.

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Support for semantic re-ranking (Semantic Ranker).

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Automatic translation of SQL to Vector queries.

Question 8

Your team is choosing between using Azure AI Search or Cosmos DB for NoSQL for a new project. Which TWO factors are key considerations that favor Azure AI Search?

Accepted Answer

Need for deep integration with full-text search (BM25) and hybrid search.. Option A is correct because Azure AI Search is purpose-built as a search engine that natively supports full-text search using the BM25 ranking algorithm as well as hybrid search that combines keyword and vector queries, which Cosmos DB for NoSQL does not provide out of the box. Option D is correct because Azure AI Search includes built-in semantic re-ranking (semantic ranker/L2 reranking) that reorders results using deep-learning models to improve relevance and accuracy, a capability Cosmos DB for NoSQL lacks natively. Option B is not a factor favoring Azure AI Search, since petabyte-scale storage is a strength of Cosmos DB for NoSQL rather than a search service. Option C is not a factor favoring Azure AI Search, because high-frequency transactional ACID updates to metadata are handled by Cosmos DB for NoSQL's transactional capabilities, not by a search index. Option E is not a factor favoring Azure AI Search, as lowest possible database-level latency is a characteristic of Cosmos DB for NoSQL's globally distributed, low-latency data plane rather than the search service.

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Requirement to store petabytes of data.

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Requirement for high-frequency transactional ACID updates to metadata.

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Need for lowest possible latency at the database level.

Question 9

You are building a RAG solution using Azure AI Search. Documents are updated frequently, and you need each document's vector embedding to be regenerated automatically whenever its text changes, without writing custom code to detect changes. Which Azure AI Search feature should you configure?

Accepted Answer

An indexer with a skillset that includes the Azure OpenAI Embedding skill.. An indexer with the Azure OpenAI Embedding skill automatically chunks and vectorizes source content during indexing. When paired with a change detection policy (such as a high-water mark column or change tracking), only modified documents are reprocessed, keeping embeddings synchronized without custom code. This is the standard no-code approach for maintaining vector freshness in Azure AI Search.

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A synonym map that links text terms to their vector representations.

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A scoring profile that boosts documents with recently updated timestamps.

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A custom Web API skill that calls Azure OpenAI directly and writes vectors to the index.

Question 10

You manage an Azure Cosmos DB for NoSQL container that stores product documents with a vector embedding property. You need to query for products similar to a given embedding while also filtering by a category partition key. Which query approach should you use?

Accepted Answer

Use a SQL query with VectorDistance and a WHERE clause on the partition key.. Cosmos DB for NoSQL provides the VectorDistance function for vector similarity search directly in SQL queries. When you also filter on the partition key, the query is scoped to a single logical partition, which optimizes performance and RU consumption. This is the most efficient and supported method for combining vector search with attribute filters.

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Use a stored procedure that loads all documents and computes cosine similarity in JavaScript.

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Use a user-defined function that calls an external Azure AI Search service.

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Use the MongoDB API's $vectorSearch aggregation stage.

Question 11

You are building a RAG solution on Azure AI Search. Documents and their embeddings are updated frequently, and you need the vector index to reflect changes within seconds without rebuilding the entire index. Which indexer behavior should you configure?

Accepted Answer

Enable incremental enrichment and run the indexer on a schedule with change detection policies.. Incremental enrichment combined with change detection policies allows the indexer to identify and process only modified or deleted documents, updating the vector index efficiently. Scheduling the indexer ensures periodic synchronization without full rebuilds. This is the recommended approach for RAG pipelines that need fresh embeddings with minimal latency and resource consumption.

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Use a custom skill that writes embeddings directly to the index bypassing the indexer pipeline.

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Set the indexer to run once manually after each document update via the Azure portal.

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Recreate the vector index and re-upload all embeddings whenever a document changes.

Question 12

You are designing an Azure Cosmos DB for NoSQL container to store vector embeddings for a product recommendation engine. Queries will filter on category and then perform vector similarity search. You need to minimize latency and ensure the index supports both operations efficiently. Which indexing policy should you implement?

Accepted Answer

Include the category path in the range index and define a vector index on the embedding property.. For hybrid queries that filter on a scalar property and then perform vector search, the indexing policy must include the scalar property in the range index and define a vector index on the embedding property. This allows the query engine to apply the filter first, reducing the set of documents for vector comparison, which improves performance and lowers RU costs.

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Exclude all paths except the vector property and create a vector index on it.

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Use only the default indexing policy, which automatically indexes all properties including vectors.

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Create a composite index on category and the vector property.

Question 13

You are implementing vector search in Azure AI Search for a document retrieval system. You need to ensure that the vector search returns the most relevant results for a query embedding. Which similarity metric should you configure in the vector search profile?

Accepted Answer

Cosine similarity. Cosine similarity is the most suitable metric for text embeddings because it normalizes for vector magnitude and focuses on semantic orientation. Azure AI Search supports cosine as a vector similarity metric, and it aligns with common embedding models, ensuring that the most semantically similar documents are retrieved based on the query embedding.

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Euclidean distance

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Dot product

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Jaccard similarity

Question 14

You are managing an Azure Cosmos DB for NoSQL account that stores vector embeddings. You need to query the database using the VectorDistance function to find the top 10 most similar items to a given vector. The container has a vector index configured. Which query syntax should you use?

Accepted Answer

SELECT TOP 10 * FROM c ORDER BY VectorDistance(c.embedding, @queryVector). To retrieve the top N most similar items using vector search in Cosmos DB for NoSQL, you must use the ORDER BY clause with the VectorDistance function and limit the results with TOP. This allows the query engine to use the vector index to efficiently find the nearest neighbors. The other options either lack ordering, use invalid syntax, or do not limit results correctly.

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SELECT * FROM c WHERE VectorDistance(c.embedding, @queryVector) > 0.8

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SELECT TOP 10 * FROM c WHERE VectorDistance(c.embedding, @queryVector) ORDER BY c.id

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SELECT * FROM c ORDER BY VectorDistance(c.embedding, @queryVector) OFFSET 0 LIMIT 10

Question 15

You are configuring vector search in Azure AI Search for a knowledge mining solution. You need to ensure that the vector search queries return results efficiently and accurately. Which TWO components must be defined in the index schema? (Choose two.)

Accepted Answer

A vector search profile that specifies the algorithm and parameters.. To enable vector search in Azure AI Search, the index must include a vector field that stores the embeddings with the correct dimensions, and a vector search profile that defines the algorithm and parameters. These two components are essential for indexing vectors and executing similarity queries. Other features like semantic ranking or suggesters are optional and not required for basic vector search.

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A suggester for autocomplete functionality.

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A scoring profile to boost certain fields.

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A semantic configuration for ranking results.