AI-200 Data Management Services And Vector Search Practice Test 5 — 15 Questions

Question 1

You are optimizing an Azure Cosmos DB for NoSQL vector search container. Which THREE properties must be correctly configured inside the vectorEmbeddingPolicy JSON object? (Choose three)

Accepted Answer

path. The vectorEmbeddingPolicy JSON object in an Azure Cosmos DB for NoSQL container requires three properties: path, dataType, and distanceFunction. Option A (path) is correct because it specifies the JSON path to the array property in each document that holds the vector embedding, such as "/embedding". Option C (dataType) is correct because it defines the data type of the vector elements, with supported values like float32, float16, int8, and uint8, which must match the stored vector data. Option D (distanceFunction) is correct because it specifies the similarity metric used for vector search, such as cosine, dotproduct, or euclidean, and is required for the index to compute nearest neighbors. Options B (httpPort) and E (sslEnabled) are not part of the vectorEmbeddingPolicy; these are connection or endpoint settings, not properties of the vector embedding policy JSON object.

Answer

httpPort

Answer

sslEnabled

Question 2

You are building a chat application with memory using Azure AI Search. You want to retrieve previous conversation turns that are semantically similar to the current user prompt. What search mode should you use if you want to execute both a vector search and a keyword search simultaneously and combine their results?

Accepted Answer

Hybrid search. Hybrid search in Azure AI Search runs a vector (k-NN) query and a keyword (BM25) query in parallel against the same index, then merges the two result sets using Reciprocal Rank Fusion (RRF) to produce a single ranked list. This is exactly what the scenario requires: semantic similarity from embeddings plus lexical precision from keyword matching. It is the recommended default for chat-with-memory and RAG patterns because it captures both conceptual and exact-term matches.

Answer

Exhaustive k-NN search

Answer

Wildcard search

Answer

Fuzzy search only

Question 3

Which property in an Azure Cosmos DB for NoSQL SQL query specifies the maximum number of vector search results to return?

Accepted Answer

TOP. In the Azure Cosmos DB for NoSQL SQL query syntax, TOP is the keyword that limits the number of results returned, and it is used with vector search queries to cap the number of nearest-neighbor results. For example, 'SELECT TOP 10 c.id FROM c ORDER BY VectorDistance(c.embedding, @queryVector)' returns the 10 closest matches. This mirrors T-SQL semantics rather than PostgreSQL or MySQL syntax.

Answer

FETCH FIRST

Answer

LIMIT

Answer

MAXROWS

Question 4

When configuring vector search in Azure Cosmos DB for NoSQL, which TWO components must be defined within the container's indexing policy? (Choose two)

Accepted Answer

vectorEmbeddingPolicy. Option B (vectorEmbeddingPolicy) is correct because the container's indexing policy must declare the vector embedding policy, which defines the vector paths, data type (e.g., float32), dimensions, and distance function (cosine, dotproduct, or euclidean) so Cosmos DB knows how to interpret and compare the stored embeddings. Option C (vectorIndexes) is also correct because the indexing policy must include a vectorIndexes section that specifies the vector path and the index type (flat, quantizedFlat, or diskANN) used to perform the similarity search. Together, vectorEmbeddingPolicy and vectorIndexes are the two required elements inside the indexing policy for vector search on Azure Cosmos DB for NoSQL. Option A (fullTextPolicy) is incorrect because it relates to full-text search (full-text indexing and scoring), not vector search. Option D (spatialIndexes) is incorrect because it configures geospatial indexing for GeoJSON data, which is unrelated to vector similarity. Option E (partitionKeyDefinition) is incorrect because the partition key is defined at the container level, not within the indexing policy.

Answer

fullTextPolicy

Answer

spatialIndexes

Answer

partitionKeyDefinition

Question 5

You are developing a RAG solution and need to evaluate the quality of your vector retrieval results from Azure AI Search. Which metric is commonly used to measure the proportion of relevant documents retrieved in the top-k results?

Accepted Answer

Recall@k. Recall@k is the metric that measures the proportion of relevant documents retrieved in the top-k results. It is defined as the number of relevant documents retrieved in the top-k divided by the total number of relevant documents available. This metric is widely used in information retrieval and RAG evaluation to assess retrieval quality.

Answer

HNSW efSearch latency

Answer

Request Units per second (RU/s)

Answer

BM25 term frequency

Question 6

You are designing a secure enterprise generative AI platform using Azure AI Search and Azure OpenAI. Which THREE security practices should you implement to protect sensitive data and access? (Choose three)

Accepted Answer

Implement Azure RBAC to restrict index management and data query permissions. Option A is correct because Azure RBAC provides fine-grained authorization over Azure AI Search, allowing you to separate roles such as Search Service Contributor (index management) from Search Index Data Reader/Contributor (query and data access), enforcing least privilege on sensitive indexes. Option B is correct because Microsoft Entra ID managed identities let Azure services authenticate to Azure OpenAI and Azure AI Search without embedding credentials, eliminating secret sprawl and enabling token-based access that can be scoped and audited. Option E is correct because Azure Private Endpoints assign private IP addresses from your VNet to Azure AI Search and Azure OpenAI, keeping traffic on the Microsoft backbone and blocking public internet exposure of these services. Option C is wrong because disabling TLS would expose embedding payloads and queries to interception; TLS 1.2+ must remain enforced. Option D is wrong because storing API keys in plain text on public web servers directly exposes credentials, violating secret-management best practices such as Azure Key Vault or managed identities.

Answer

Disable transport layer security (TLS) to speed up embedding transmission

Answer

Store all API keys in plain text configuration files on public web servers

Question 7

You are designing a high-throughput RAG pipeline where documents are continuously ingested and queried. You choose Azure Cosmos DB for NoSQL as your vector store. How does Cosmos DB handle vector indexing updates when new documents are inserted?

Accepted Answer

Vector indexes are updated asynchronously in the background by the database engine. Azure Cosmos DB for NoSQL automatically updates vector indexes asynchronously in the background when new documents are inserted. This means that after insertion, there may be a slight delay before the new vectors are indexed and available for vector search queries. The database engine handles the indexing without manual intervention.

Answer

You must manually trigger an index rebuild stored procedure every hour

Answer

Vector search is completely unavailable until the entire collection is re-indexed from scratch

Answer

New documents cannot be queried using VectorDistance until the container is restarted

Question 8

Which Azure AI Search API parameter allows you to specify a vector query alongside traditional keyword search terms when executing a hybrid search?

Accepted Answer

vectorQueries. In the Azure AI Search REST API and SDKs, the 'vectorQueries' array is the parameter that carries one or more vector query definitions alongside the traditional 'search' text parameter, enabling hybrid search. Each entry in vectorQueries specifies the vector value (or a reference to a vector field), the k-nearest neighbors count, and optional filters. When both 'search' and 'vectorQueries' are present in the same request, Azure AI Search performs hybrid ranking that fuses keyword BM25 scores with vector similarity scores.

Answer

embeddingQuery

Answer

knnQuery

Answer

vectorSearchParam

Question 9

When configuring an Azure AI Search vectorizer for integrated vectorization, which TWO parameters or connections must be specified to link to an Azure OpenAI deployment? (Choose two)

Accepted Answer

Model name (e.g., text-embedding-ada-002). Option A is correct because an Azure AI Search vectorizer that targets Azure OpenAI must specify the embedding model name (for example, text-embedding-ada-002 or text-embedding-3-large) so the service knows which deployed model to invoke for generating vectors. Option E is correct because the vectorizer also requires the Azure OpenAI resource endpoint URL plus authentication details — either an API key or a managed identity — to authorize calls to that deployment. Options B, C, and D are incorrect because SQL Server connection strings, Cosmos DB container database names, and Blob storage SAS token expiration times are unrelated to linking a vectorizer to an Azure OpenAI embedding deployment; they pertain to other data sources or storage access scenarios.

Answer

SQL Server connection string

Answer

Cosmos DB container database name

Answer

Blob storage SAS token expiration time

Question 10

You are designing a data layer architecture for an enterprise AI assistant that requires high-performance vector search, structured filtering, and multi-tenant isolation. Which THREE design patterns should you consider? (Choose three)

Accepted Answer

Combining vector similarity with metadata filters to narrow search scope. Option A is correct because combining vector similarity with metadata filters (pre-filtering or post-filtering on structured attributes) narrows the candidate set, improving both relevance and latency for an AI assistant that needs structured filtering alongside semantic search. Option C is correct because tenant-specific partition keys or metadata filter tags enforce logical isolation in a shared vector index, ensuring queries only return a tenant's own data and satisfying multi-tenant isolation requirements. Option E is correct because hybrid search fuses dense vector similarity with sparse keyword matching (for example BM25 plus embeddings, often combined via reciprocal rank fusion), capturing both exact term matches and conceptual similarity for higher recall and precision. Option B is wrong because an unindexed JSON blob with no partition keys destroys search performance and provides no tenant isolation. Option D is wrong because hardcoding user prompts into the index schema is brittle, non-scalable, and violates separation of data from schema design.

Answer

Storing all tenant data in a single unindexed JSON blob without partition keys

Answer

Hardcoding all user prompts directly into the search index schema

Question 11

You are writing a query in Azure Cosmos DB for NoSQL that sorts results by vector similarity and also filters results by a category property. How must you order the clauses in your SQL statement?

Accepted Answer

The WHERE clause must come before the ORDER BY VectorDistance() clause. In Azure Cosmos DB for NoSQL, SQL clause order is fixed: SELECT, FROM, WHERE, ORDER BY. When using VectorDistance() in ORDER BY, the WHERE clause must still appear before ORDER BY, so filtering by category happens first and then results are sorted by vector similarity. This is standard SQL clause ordering, not a vector-specific exception.

Answer

VectorDistance must be placed in the SELECT clause instead of ORDER BY

Answer

WHERE and ORDER BY clauses cannot be used in the same query when using vectors

Answer

The ORDER BY clause must come before the WHERE clause

Question 12

Which REST API HTTP method is used to create or update an index definition in Azure AI Search?

Accepted Answer

PUT. Azure AI Search exposes index definitions through its REST API at /indexes/{indexName}, and creating or replacing an index definition uses HTTP PUT. PUT is idempotent and replaces the entire index definition, which is the documented method for creating or updating an index. GET retrieves, PATCH is not supported for index definitions, and DELETE removes the index.

Answer

GET

Answer

PATCH

Answer

DELETE

Question 13

Which tool in Azure Portal allows you to test search queries, inspect index schemas, and view JSON responses for Azure AI Search?

Accepted Answer

Search explorer. Search explorer is the built-in query console in the Azure portal for Azure AI Search. It lets you submit simple, full, or Lucene queries against a selected index, inspect the index schema (fields, attributes, analyzers), and view the raw JSON response documents returned by the service. This makes it the correct tool for testing search queries and validating index behavior directly from the portal.

Answer

Metrics Advisor

Answer

Data Explorer

Answer

Log Analytics

Question 14

When configuring vector search in Azure AI Search, which TWO components are defined within the vectorSearch object of an index definition? (Choose two)

Accepted Answer

algorithms. Option C (algorithms) is correct because the vectorSearch object in an Azure AI Search index definition contains an algorithms array where you configure the approximate nearest neighbor (ANN) algorithm used for vector indexing, such as HNSW (with parameters like m, efConstruction, efSearch) or exhaustive KNN. Option D (profiles) is correct because the same vectorSearch object contains a profiles array that defines named vector search profiles, each combining an algorithm configuration with a vectorizer, which fields then reference via their vectorSearchProfile property. Options A (sqlPartitionKey), B (storageAccountConnectionString), and E (cosmosContainerName) are incorrect because they are not properties of the vectorSearch object; they relate to data source or indexer configuration for external data stores (e.g., Azure SQL or Cosmos DB), not to vector search configuration within an index definition.

Answer

sqlPartitionKey

Answer

storageAccountConnectionString

Answer

cosmosContainerName

Question 15

You are troubleshooting a scenario where hybrid queries in Azure AI Search are returning unexpected relevance rankings. Which THREE factors could influence the final score when using hybrid search with semantic ranking? (Choose three)

Accepted Answer

Semantic ranker deep learning re-scoring. Option B is correct because in hybrid search with semantic ranking, the semantic ranker applies deep learning models to re-score the top results returned by the initial retrieval, directly influencing the final relevance ranking. Option C is correct because hybrid search combines keyword-based BM25 scoring with vector similarity, so the BM25 keyword match relevance scores contribute to the fused ranking. Option D is correct because Reciprocal Rank Fusion (RRF) is the algorithm Azure AI Search uses to merge keyword and vector result sets, and its rank-based combination determines the fused score that feeds into semantic re-ranking. Option A is incorrect because the CPU core count of the client application has no bearing on how Azure AI Search computes relevance scores server-side. Option E is incorrect because Blob Storage access tiers affect storage cost and latency, not the relevance scoring pipeline of a search index.

Answer

Virtual machine CPU core count assigned to the client application

Answer

Azure Blob Storage container access tier (Hot vs Cool)