AI-200 Data Management Services And Vector Search Practice Test 8 — 15 Questions

Question 1

You are designing a data architecture on Azure where embeddings are generated offline by an Azure Databricks pipeline and stored in Azure AI Search. You want to minimize ingestion latency and optimize throughput when uploading millions of pre-computed vectors. Which client library API pattern should you use?

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

Accepted Answer

Use SearchClient batch upload APIs with up to 1000 documents per batch. When uploading large batches of documents containing pre-computed vectors to Azure AI Search, using the SearchClient.UploadDocumentsBatch (or mergeOrUpload) method with batched payloads of up to 1000 documents maximizes throughput.

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Stream vectors directly through the Azure AI Search integrated vectorizer skillset

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Send documents one-by-one using synchronous CreateOrUpdate API calls

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Execute direct SQL INSERT statements against the underlying search partition nodes

Question 2

You are implementing a multi-modal AI application that searches across both text descriptions and product images stored in Azure AI Search. Which feature allows you to query both modalities within a unified search index?

Accepted Answer

Multiple vector fields with distinct dimensions and vectorizers in a single index. Azure AI Search supports multiple vector fields within a single index schema, allowing different fields to use different vector dimensions and embedding models (e.g., text embeddings and image embeddings).

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Single-dimension global vector index restriction

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Mandatory unification of text and image vectors into a single 1536-dimensional field

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External fusion proxy service

Question 3

You are configuring an Azure AI Search vector index. Which TWO parameters are part of the HNSW algorithm configuration settings? Choose two.

Accepted Answer

efSearch. In Azure AI Search, the HNSW algorithm configuration accepts 'm' (number of bi-directional links per node) and 'efSearch' (size of the dynamic candidate list during search), along with 'efConstruction'.

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maxTokenLength

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clusterCount

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shingleSize

Question 4

You are designing an enterprise search architecture that combines Azure AI Search and Azure OpenAI. Which THREE strategies help optimize hybrid search relevance and retrieval quality? Choose three.

Accepted Answer

Tune HNSW parameters (m and efSearch) based on recall and latency trade-offs. To optimize hybrid search relevance, practitioners use Reciprocal Rank Fusion (RRF), semantic ranking (L2 re-ranking), and proper vector normalization/tuning.

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Disable all OData filters to maximize vector recall

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Store all embeddings as uncompressed plaintext strings in Blob Storage

Question 5

You are optimizing query performance for an Azure AI Search index experiencing high query volumes with vector search. You need to scale out query processing capabilities without increasing index build time. Which action should you perform?

Accepted Answer

Increase the number of search units by adding replica instances. To scale out query throughput (QPS) and handle higher query load for vector search in Azure AI Search, you should increase the number of replica units.

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Rebuild the index using exhaustiveKnn

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Increase the number of partition instances

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Upgrade the search service from Standard to Basic tier

Question 6

You are implementing vector search using Azure Cosmos DB for MongoDB (vCore). Which TWO distance metrics are valid options when creating a vector search index? Choose two.

Accepted Answer

euclidean (l2). Azure Cosmos DB for MongoDB (vCore) supports cosine distance (cos), inner product (ip), and Euclidean distance (l2).

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chi-squared (cs)

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pearson (pr)

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manhattan (m1)

Question 7

You are designing a data ingestion pipeline that generates embeddings using Azure OpenAI and stores them in Azure AI Search. Which THREE components are required in the Azure AI Search index definition to support integrated vectorization? Choose three.

Accepted Answer

Vectorizer definition referencing the Azure OpenAI connection. Integrated vectorization in Azure AI Search requires a vectorizer definition, an algorithm configuration, and vector fields configured in the index schema.

Answer

A dedicated Azure Blob Storage container data source

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A custom Lucene similarity scoring profile

Question 8

When configuring vector search in Azure Cosmos DB for NoSQL, which TWO properties must be defined within the container's vector embedding policy? Choose two.

Accepted Answer

Path (the JSON property containing the vector array). In Azure Cosmos DB for NoSQL, the vector embedding policy requires defining paths (properties containing vectors) and embedding details such as datatype, dimensions, and distance function.

Answer

Container partition key definition

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BM25 k1 and b tuning parameters

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Replication lag threshold

Question 9

You are troubleshooting slow query performance in an enterprise Azure AI Search vector deployment. Which THREE architectural or configuration adjustments can improve query throughput and latency? Choose three.

Accepted Answer

Scale out the number of search service replicas. To improve vector search performance in Azure AI Search, practitioners can scale out replicas, tune HNSW efSearch parameters, and leverage batching or appropriate tier sizing.

Answer

Replace all vector fields with standard ASCII folding tokenizers

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Configure the index to use exhaustive KNN exclusively for high-QPS production workloads

Question 10

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.

Accepted Answer

Azure SQL Database. Azure SQL Database and Azure Cosmos DB (both NoSQL and MongoDB vCore APIs) natively support vector data storage and vector search functions.

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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 11

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. The vectorSearch configuration block inside an Azure AI Search index definition requires a profiles array where algorithms and vectorizer settings are tied together. Each searchable vector field then references the vector search profile via the vectorSearchProfile property.

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vectorIndexKind

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compressionMethod

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similarityFunction

Question 12

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.

Answer

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 13

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 provides native vector search functions including VectorDistance, which computes the distance between two vectors using Cosine, DotProduct, or Euclidean distance metrics.

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EmbeddingDistance()

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GetVectorDistance()

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CosineSimilarity()

Question 14

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). Azure Cosmos DB for MongoDB (vCore) supports both DiskANN and HNSW (Hierarchical Navigable Small World) index types for vector search. HNSW and DiskANN provide ANN search capabilities, with DiskANN being optimized for large-scale datasets stored on SSDs.

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FULLTEXT index

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B-Tree index

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HASH index

Question 15

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) in Azure AI Search compresses 32-bit floating-point vector values down to 8-bit integers (int8), significantly reducing memory usage and accelerating vector search performance with minimal loss in recall accuracy.

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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