AI-200 Data Management Services And Vector Search Practice Test 9 — 15 Questions

Question 1

You are planning a data layer architecture for an AI application on Azure that requires both relational transactional storage and vector search capabilities. Which TWO Azure database services natively support vector data types and similarity search functions? Choose two.

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Azure SQL Database. Azure SQL Database (A) is correct because it natively supports a VECTOR data type and vector similarity functions such as VECTOR_DISTANCE, enabling cosine and Euclidean similarity search directly in T-SQL alongside relational transactional workloads. Azure Cosmos DB for NoSQL (C) is correct because it provides native vector indexing and vector search (including DiskANN-based indexing) with similarity functions like VectorDistance, so it can store and query embeddings alongside JSON documents. Azure Queue Storage (B) is a simple message-queue service with no data types or query engine, so it cannot store vectors or perform similarity search. Azure Table Storage (D) is a key-value/NoSQL table store without vector data types or similarity functions. Azure Cache for Redis Basic tier (E) lacks the RediSearch/vector modules, so it cannot perform vector similarity search in that tier.

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Azure Queue Storage

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Azure Table Storage

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Azure Cache for Redis (Basic tier without modules)

Question 2

You are developing an enterprise retrieval-augmented generation (RAG) application using Azure AI Search. You need to configure a vector index to store dense vector embeddings alongside traditional text fields. Which property within the Azure AI Search index definition must be explicitly configured with a vector search profile to enable HNSW (Hierarchical Navigable Small World) algorithm indexing?

Accepted Answer

vectorSearchProfile. In an Azure AI Search index, the vectorSearchProfile property on a vector field binds that field to a named profile defined in the index's vectorSearch configuration. The profile in turn references an algorithm configuration (for example, an HNSW parameters object) and optionally a compression method, so setting vectorSearchProfile is what actually activates HNSW indexing for the field.

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vectorIndexKind

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compressionMethod

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similarityFunction

Question 3

Your team is implementing hybrid search in Azure AI Search combining full-text keyword search and vector search. Users report that certain exact product serial numbers are missing from the semantic ranking results. Which query parameter should you adjust to balance the influence of BM25 text search scores against vector cosine similarity scores?

Accepted Answer

Enabling semantic ranking and providing a semanticConfiguration with targeted weight overrides.. When performing hybrid queries in Azure AI Search, the search engine uses Reciprocal Rank Fusion (RRF) to combine results from text and vector searches. You can influence the weighting or use standard RRF parameters, but adjusting semantic ranking configurations or ensuring proper semantic configuration boosts can address relevance blending. Specifically, adjusting query parameters like semantic configuration or custom scoring profiles allows fine-tuning.

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Increasing the efSearch parameter in the index definition.

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Adjusting the top parameter to retrieve a larger candidate pool before RRF combination.

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Switching the vector similarity metric from cosine to euclidean distance.

Question 4

You are storing vector embeddings generated by Azure OpenAI in Azure Cosmos DB for NoSQL. Which built-in SQL function must you use within your query to compute vector distance when executing a similarity search?

Accepted Answer

VectorDistance(). Azure Cosmos DB for NoSQL exposes the built-in VectorDistance() function, which computes the distance between a stored vector and a query vector using the configured distance function (cosine, dot product, or Euclidean). It is used in the ORDER BY clause of a query to return the nearest neighbors. This is the only correct built-in function name for vector similarity in Cosmos DB for NoSQL.

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EmbeddingDistance()

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GetVectorDistance()

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CosineSimilarity()

Question 5

You are configuring vector indexing in Azure Cosmos DB for MongoDB (vCore). You need to choose an indexing type for your vector property to balance query performance and recall accuracy for high-dimensional data. Which vector index type should you select for approximate nearest neighbor (ANN) search?

Accepted Answer

HNSW (Hierarchical Navigable Small World). HNSW (Hierarchical Navigable Small World) is a graph-based approximate nearest neighbor algorithm that builds a multi-layer navigable graph, delivering high recall with low latency for high-dimensional vectors. In Azure Cosmos DB for MongoDB vCore, HNSW is the supported vector index type for ANN search, and it is the recommended choice when you need to balance query performance and recall accuracy. FULLTEXT, B-Tree, and HASH indexes are not designed for vector similarity search and cannot perform ANN queries.

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FULLTEXT index

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B-Tree index

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HASH index

Question 6

You are optimizing storage costs and search latency for a massive Azure AI Search vector index containing 50 million records, each with 3,072-dimensional embeddings from text-embedding-3-large. Which Azure AI Search feature should you implement to reduce memory footprint while maintaining acceptable recall?

Accepted Answer

Scalar Quantization (SQ). Scalar Quantization (SQ) compresses each float32 vector component into a smaller integer (typically 8-bit), reducing the in-memory and on-disk footprint of the vector index by roughly 4x while preserving enough precision to maintain acceptable recall. For a 50-million-record index with 3,072-dimension embeddings, this is the Azure AI Search feature explicitly designed to trade a small amount of recall for substantial memory and cost savings.

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Exhaustive K-nearest neighbor (exact KNN) search

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Scoring profiles with magnitude boosting

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Semantic enrichment with Azure AI Language

Question 7

When designing a data layer architecture for an AI application using Azure AI Search, what is the primary role of an integrated vectorizer?

Accepted Answer

To automatically convert raw source text fields into vector embeddings during indexing and queries without requiring external orchestration code.. An integrated vectorizer in Azure AI Search is a configuration that binds an embedding model (e.g., Azure OpenAI text-embedding-ada-002) to an index so that raw text fields are automatically converted into vector embeddings at both indexing time and query time. This eliminates the need for external orchestration code to call the embedding API, chunk text, and push vectors into the index.

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To execute full-text BM25 keyword matching alongside vector calculations.

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To compress vector indexes using lossy compression algorithms to save disk storage.

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To generate cryptographic hashes for document deduplication.

Question 8

You are designing the data storage and indexing strategy for a multi-tenant AI search solution on Azure. Which TWO architectural practices should you implement to optimize vector search performance and tenant isolation in Azure AI Search?

Accepted Answer

Implement security trimming by appending OData filter expressions (e.g., tenantId eq 'tenant-1') to every vector search query.. Option C is correct because Azure AI Search supports security trimming via OData filter expressions such as tenantId eq 'tenant-1' applied to vector queries, which enforces tenant isolation by restricting results to documents matching the caller's tenant identifier while still allowing shared indexes. Option E is correct because provisioning dedicated indexes or service tiers for enterprise tenants provides physical/logical isolation and dedicated compute, ensuring predictable vector search latency and strict data separation for high-compliance customers. Option A is wrong because disabling HNSW and relying on exhaustive brute-force (exhaustive KNN) search sacrifices scalability and latency, and brute-force across all tenant partitions does not provide tenant isolation. Option B is wrong because storing all tenants' embeddings in a single unpartitioned vector field without metadata tags prevents filtering and security trimming, breaking tenant isolation. Option D is wrong because moving embeddings to Blob Storage and computing cosine similarity client-side eliminates Azure AI Search's optimized vector indexing and integrated filtering, harming performance and isolation.

Answer

Disable the HNSW algorithm and rely exclusively on exhaustive brute-force search across all tenant partitions.

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Store all tenants' embeddings in a single unpartitioned vector field without metadata tags to maximize index density.

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Store raw embeddings exclusively in Azure Blob Storage and perform client-side cosine similarity calculations for all queries.

Question 9

Your enterprise application architecture incorporates Azure Cosmos DB for NoSQL as the primary operational data store and Azure AI Search as the vector index for semantic search. Which THREE mechanisms can you use to keep the Azure AI Search vector index synchronized with changes in Azure Cosmos DB?

Accepted Answer

Configure an Azure AI Search indexer connected to an Azure Cosmos DB change feed data source.. Option A is correct because Azure AI Search natively supports an Azure Cosmos DB data source for indexers, which reads the container's change feed (including inserts, updates, and deletes when soft-delete/change-feed policies are configured) and incrementally pushes documents into the vector index. Option B is correct because Azure Cosmos DB emits container mutation events to Azure Event Grid, which can trigger downstream orchestration (for example, Functions, Logic Apps, or custom handlers) that updates Azure AI Search documents via its REST/SDK APIs. Option C is correct because the Azure Cosmos DB change feed can be consumed by an Azure Function trigger, and the function can call the Azure AI Search REST API (for example, the index or merge-or-upload actions) to upsert vectors and fields in near real time. Option D is not appropriate because a full scan plus drop-and-rebuild every 5 minutes is a costly, high-latency batch pattern rather than an incremental synchronization mechanism, and Azure Data Factory is not the recommended vector-index sync path. Option E is not a real capability: Azure Cosmos DB does not provide native bidirectional memory-mapped replication to Azure AI Search replicas; synchronization must be implemented through indexers, change feed consumers, or event-driven pipelines.

Answer

Execute a full database table scan and drop-rebuild of the search index every 5 minutes using Azure Data Factory batch pipelines.

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Enable native real-time bidirectional memory-mapped replication between Azure Cosmos DB database partitions and search service replicas.

Question 10

You are designing an application that requires low-latency vector search for a high-traffic AI chatbot. You need to ensure the vector index remains performant as the dataset grows into millions of documents. Which index configuration in Azure AI Search should you implement to optimize for speed over absolute recall accuracy?

Accepted Answer

HNSW. HNSW (Hierarchical Navigable Small World) is a graph-based approximate nearest neighbor (ANN) algorithm that builds a multi-layer navigable graph, delivering sub-linear query latency with high recall. In Azure AI Search, HNSW is the recommended index configuration for high-traffic, low-latency vector search over millions of documents when you can trade a small amount of recall for speed.

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Flat Indexing

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Exhaustive KNN

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Partitioned Indexing

Question 11

When configuring vector search algorithms in Azure AI Search, you can tune several parameters associated with the Hierarchical Navigable Small World (HNSW) graph. Which THREE parameters are configurable within an HNSW algorithm profile?

Accepted Answer

efConstruction. In Azure AI Search, an HNSW algorithm profile exposes three tunable graph parameters: efConstruction (option A), which controls the size of the dynamic candidate list during index construction and thus the quality of the built graph; efSearch (option D), which controls the size of the candidate list at query time and trades recall against latency; and m (option E), which sets the number of bidirectional links created per node and governs graph connectivity and memory usage. These are exactly the parameters documented for the HNSW configuration object in Azure AI Search. Option B (bm25K1) is a parameter of the BM25 ranking function used for keyword relevance scoring, not vector search, and option C (chunkOverlapSize) relates to how documents are split during chunking in a skillset or integrated vectorization pipeline, not to the HNSW graph itself.

Answer

bm25K1

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chunkOverlapSize

Question 12

Your application uses Cosmos DB for NoSQL to store product metadata and vectors. You need to perform a vector search that combines similarity scores with specific filter criteria on the 'category' field. How should you define your index policy to support this?

Accepted Answer

Define a vector index policy and include the 'category' property in the indexing policy's included paths.. For vector search with pre-filtering in Cosmos DB for NoSQL, the index policy must include both a vector index policy for the embedding path and a regular indexing policy that includes the filter property (e.g., 'category'). This allows the query engine to apply the filter efficiently before or alongside the vector similarity search, reducing the candidate set and improving performance.

Answer

Create a vector index policy containing only the vector embedding path.

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Enable auto-indexing for all properties to ensure vector search works.

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Use a stored procedure to perform the filter after the vector search result set is returned.

Question 13

You are configuring a hybrid search in Azure AI Search. You want to combine results from a vector search and a keyword-based search. Which feature is specifically designed to normalize scores from these different retrieval methods into a single ranked list?

Accepted Answer

Reciprocal Rank Fusion (RRF). Reciprocal Rank Fusion (RRF) is the specific algorithm Azure AI Search uses to merge results from parallel vector (kNN) and keyword (BM25) queries in a hybrid search. It converts each document's rank in each result set into a reciprocal score (1/(k + rank)) and sums them, producing a single unified ranked list without needing to normalize the raw, incomparable scores from BM25 and cosine similarity.

Answer

BM25 Scoring

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Semantic Ranker

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Vector Normalization

Question 14

You are building a RAG application using Azure AI Search. You want to ensure that the search results provided to the LLM are as relevant as possible by leveraging a re-ranking model. Which service must be enabled to utilize the 'Semantic Ranker'?

Accepted Answer

Standard Tier or higher. Semantic Ranker in Azure AI Search requires the Standard tier or higher; it is not available on the Free or Basic tiers. Enabling Semantic Ranker allows the service to re-rank search results using a deep learning model, improving relevance for RAG applications by promoting semantically relevant documents.

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Basic Tier

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Free Tier

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Storage Optimized Tier

Question 15

You have a large collection in Azure Cosmos DB for NoSQL and want to perform a vector search. The application is currently hitting 'Request Rate Too Large' (429) errors during vector search queries. What is the most effective way to mitigate this?

Accepted Answer

Scale up the throughput (RU/s) allocated to the container.. 'Request Rate Too Large' (HTTP 429) in Cosmos DB means the container's provisioned or autoscale RU/s has been exhausted by the vector search workload. The most direct and effective mitigation is to increase the throughput allocated to the container so that the RU cost of vector queries is covered without throttling.

Answer

Reduce the number of vectors stored in each document.

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Implement client-side caching of vector search results.

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Switch the index type to 'DiskANN'.