AI-200 Data Management Services And Vector Search — Timed 40 Questions

Question 1

You are configuring a vector profile in Azure AI Search. Which three elements must be correctly linked together to define a complete vector search configuration?

Accepted Answer

Vectorizer, Algorithm configuration, and Field mapping. In Azure AI Search, a vector profile requires linking an algorithm configuration (such as HNSW parameters), a vectorizer (specifying how embeddings are created or stored), and a field mapping.

Answer

Storage account, Key Vault, and Managed Identity

Answer

Semantic configuration, Answer profile, and Caption settings

Answer

BM25 analyzer, Tokenizer, and Synonym map

Question 2

When configuring an Azure Cosmos DB for NoSQL container to store embeddings, what data type must the vector property be stored as within the JSON document?

Accepted Answer

An array of 32-bit floating-point numbers. In Azure Cosmos DB for NoSQL, vector embeddings must be stored as an array of 32-bit floating-point numbers (float32).

Answer

A base64 encoded string

Answer

An array of integers

Answer

A binary blob

Question 3

You are configuring an Azure AI Search index schema via the REST API. You need to define a field that will hold a 1536-dimensional vector. Which field type must you assign to this field?

Accepted Answer

Edm.SingleCollection. In Azure AI Search, fields intended to store embeddings must be explicitly typed as Edm.SingleCollection with dimensions defined.

Answer

Edm.Vector

Answer

Edm.String

Answer

Edm.DoubleCollection

Question 4

You are configuring semantic ranking in Azure AI Search to improve search relevance for your enterprise application. Which tier of Azure AI Search is required to enable semantic ranking?

Accepted Answer

Standard (S1 or higher). Semantic ranking in Azure AI Search is a premium feature available starting from the Standard tier (S1 and above), and is not supported on Free or Basic tiers.

Answer

Storage Optimized (L1)

Answer

Free

Answer

Basic

Question 5

You are building a hybrid search solution using Azure AI Search that combines BM25 full-text search with vector search. What is the name of the feature that intelligently combines and normalizes scores from different retrieval systems before presenting the final top results?

Accepted Answer

Reciprocal Rank Fusion (RRF). Azure AI Search uses Reciprocal Rank Fusion (RRF) as part of semantic ranking and hybrid search score combination to merge disparate result lists.

Answer

Min-Max Score Scaling

Answer

Cosine Distance Normalization

Answer

Cross-Encoder Re-ranking

Question 6

You are optimizing an Azure AI Search vector index with millions of documents. Users report high latency during vector queries. You decide to implement vector compression to reduce memory footprint and improve search throughput. Which compression method is natively supported in Azure AI Search?

Accepted Answer

Scalar Quantization (SQ). Azure AI Search supports scalar quantization (specifically SQ) to compress 32-bit floating-point vectors into 8-bit integers, significantly reducing memory usage with minimal loss in recall.

Answer

Autoencoders

Answer

Principal Component Analysis (PCA)

Answer

Product Quantization (PQ)

Question 7

Your company stores product catalog items in Azure Cosmos DB for NoSQL and needs to implement vector search for recommendation capabilities. Which indexing policy property must you add to enable vector indexing on a container?

Accepted Answer

vectorIndexes. Azure Cosmos DB for NoSQL requires a vectorIndexes array within the indexingPolicy to index vector properties for similarity search.

Answer

compositeIndexes

Answer

spatialIndexes

Answer

fullTextIndexes

Question 8

You are writing a query in Azure Cosmos DB for NoSQL to retrieve documents based on vector similarity against a target embedding. Which built-in SQL function should you use in the ORDER BY clause?

Accepted Answer

VectorDistance(). Azure Cosmos DB provides the VectorDistance() system function to compute similarity or distance between stored vectors and query vectors.

Answer

CosSimilarity()

Answer

EmbeddingDistance()

Answer

GetVectorDistance()

Question 9

You are developing a retrieval-augmented generation (RAG) solution using Azure AI Search. You need to configure a vector index to store 1536-dimensional embeddings generated by text-embedding-ada-002. Which parameter must you configure in the vector profile's algorithm configuration to use HNSW as the underlying approximate nearest neighbor algorithm?

Accepted Answer

algorithm.name set to hnsw. To configure HNSW in Azure AI Search, you must define a vectorizer and an algorithm configuration specifying algorithm parameters such as m, efConstruction, and metric under the vectorSearch property of the index.

Answer

algorithm.name set to exhaustiveKnn

Answer

vectorSearch.compression set to scalarQuantization

Answer

vectorSearch.algorithm.type set to faiss

Question 10

Which similarity metric measures the cosine of the angle between two vectors, focusing on orientation rather than magnitude, and is commonly used in Azure AI Search?

Accepted Answer

cosine. Cosine distance/similarity measures the angle between two vectors and is one of the standard supported metrics in Azure AI Search and Cosmos DB.

Answer

manhattan

Answer

euclidean

Answer

dotProduct

Question 11

You are configuring a vector search profile in Azure AI Search. Which TWO parameters are required when defining an algorithm configuration using the HNSW algorithm? (Choose two)

Accepted Answer

efConstruction. When configuring an HNSW algorithm profile in Azure AI Search, you must define parameters such as m (number of bi-directional links per node) and efConstruction (size of the dynamic candidate list during construction).

Answer

vectorDimensions

Answer

compressionType

Answer

shardCount

Question 12

You are using Azure OpenAI Service to generate embeddings for your documents before storing them in Azure Cosmos DB for NoSQL. The embedding model outputs vectors with a length of 1536. When defining the vector embedding policy in Cosmos DB, what value should you specify for the dimensions property?

Accepted Answer

1536. The dimensions property in the Azure Cosmos DB vector embedding policy must exactly match the output dimensions of the embedding model used (1536 for text-embedding-ada-002).

Answer

3072

Answer

512

Answer

768

Question 13

You are designing an enterprise search architecture on Azure that requires hybrid search capabilities. Which THREE components or features must be configured to successfully execute a hybrid query with semantic ranking in Azure AI Search? (Choose three)

Accepted Answer

A searchable Edm.String text field for keyword matching. To run hybrid search with semantic ranking, you need a vector field, a text field for BM25 keyword search, and semantic configuration enabled on the index.

Answer

Azure Blob Storage configured as an external data source

Answer

An Azure Functions application for manual score merging

Question 14

You are managing an Azure AI Search index that uses integrated vectorization with Azure OpenAI. When a user sends a query string, you want Azure AI Search to automatically vectorize the query text before performing the vector search. Which feature must you configure in the index schema?

Accepted Answer

A custom Web API skillset. Integrated vectorization in Azure AI Search uses a vectorizer configuration connected to Azure OpenAI to automatically convert plain text queries into vector embeddings.

Answer

A cognitive services attachment on the storage account

Answer

An Azure Logic App trigger

Question 15

You are designing a multi-tenant AI application using Azure Cosmos DB for NoSQL. Each tenant has their own set of embeddings, and you need to filter vector search results strictly by tenantId while maintaining high query performance. How should you structure your vector index definition?

Accepted Answer

Include tenantId in the container's standard indexing policy with a range index, and filter using a WHERE clause alongside ORDER BY VectorDistance(). When configuring vector embeddings in Azure Cosmos DB for NoSQL, you must specify the dataType, dimensions, distanceFunction, and similarity metric. To filter efficiently, you combine VectorDistance() with a traditional WHERE clause on tenantId, ensuring the property is indexed.

Answer

Store tenantId inside the vector array as the first element

Answer

Use spatial indexes on tenantId

Answer

Create a separate container for every tenant automatically

Question 16

Which distance metric in Azure Cosmos DB vector search calculates the shortest straight-line segment between two points in a multi-dimensional space?

Accepted Answer

euclidean. Euclidean distance measures the straight-line distance between two points in Euclidean space.

Answer

dotproduct

Answer

hamming

Answer

cosine

Question 17

You are designing the data architecture for an AI-powered document search application. You choose Azure AI Search because you need multi-lingual support, faceted navigation, and vector search. What is the maximum number of vector fields allowed per index in Azure AI Search?

Accepted Answer

Multiple vector fields are supported per index. Azure AI Search allows multiple vector fields per index to support multi-modal or multi-model embeddings (e.g., text and image embeddings in the same document).

Answer

Exactly one vector field per index

Answer

A maximum of two vector fields

Answer

Vector fields are not supported in Azure AI Search

Question 18

You are configuring vector search in Azure Cosmos DB for NoSQL. You want to ensure that vectors are indexed using the DiskANN algorithm for high recall and fast search performance. Which index type should you specify in the vector embedding policy?

Accepted Answer

diskANN. Azure Cosmos DB for NoSQL supports DiskANN as a high-performance vector indexing type alongside quantized flat indexes.

Answer

faissHNSW

Answer

hnsw

Answer

ivfFlat

Question 19

When querying an Azure AI Search vector index using the REST API or SDK, what parameter specifies the number of nearest neighbors to retrieve?

Accepted Answer

k. The nearest neighbors query parameter in Azure AI Search vector queries is k (or kNearestNeighbors).

Answer

radius

Answer

top

Answer

depth

Question 20

You are implementing a robust data layer architecture for an enterprise generative AI application. Which THREE factors should you evaluate when deciding between Azure AI Search and Azure Cosmos DB for vector search? (Choose three)

Accepted Answer

Need for automated multi-region replication with single-digit millisecond latency reads. When choosing between Azure AI Search and Cosmos DB for vector search, factors such as full-text search capabilities, transactional operational database needs, and scaling requirements are critical.

Answer

Requirement to run Hadoop MapReduce jobs directly over the vector indexes

Answer

Requirement to store vector embeddings as relational SQL tables with foreign key constraints