AI-200 Data Management Services And Vector Search Practice Test 6 — 15 Questions

Question 1

Which Azure portal blade allows you to view and manage access keys and connection strings for an Azure AI Search service?

Accepted Answer

Keys. The 'Keys' blade in the Azure Portal provides admin and query API keys for Azure AI Search.

Answer

Data source

Answer

Certificates

Answer

Scale

Question 2

When configuring vector search in Azure AI Search, which TWO compression options or algorithm settings can be defined to optimize resource usage? (Choose two)

Accepted Answer

Scalar quantization configuration. Scalar quantization and HNSW algorithm parameters are key configurations used to optimize vector search performance and resource usage.

Answer

Blob storage container access level

Answer

SQL table partitioning scheme

Answer

Cosmos DB throughput auto-scale limits

Question 3

You are configuring an Azure AI Search index where text fields are automatically analyzed using language-specific rules. Which property on an Edm.String field specifies the language analyzer (e.g., 'en.microsoft')?

Accepted Answer

analyzer. The analyzer property on an Edm.String field specifies the language-specific analyzer used for full-text tokenization.

Answer

tokenizerType

Answer

languageMapping

Answer

vectorProfile

Question 4

You are designing a secure data layer architecture for financial documents using Azure Cosmos DB for NoSQL and Azure AI Search. Which THREE compliance and security controls should you implement? (Choose three)

Accepted Answer

Enable diagnostic logging and audit log streaming to Azure Monitor / Log Analytics. Enterprise compliance and security controls include configuring customer-managed keys, enabling diagnostic logging for auditing, and enforcing private endpoint networking.

Answer

Store all database backup files in unencrypted public blob storage containers

Answer

Disable authentication keys and allow anonymous public access for easy integration

Question 5

Which Azure service provides cloud-scale vector search backed by either HNSW or DiskANN algorithms alongside managed NoSQL capabilities?

Accepted Answer

Azure Cosmos DB for NoSQL. Azure Cosmos DB for NoSQL provides managed NoSQL storage with native vector search powered by DiskANN.

Answer

Azure Logic Apps

Answer

Azure Functions

Answer

Azure Virtual Machines

Question 6

You are troubleshooting a performance issue where an Azure AI Search indexer fails to complete within the scheduled execution window when processing large PDF documents. What is the most effective configuration change to resolve the timeout issue?

Accepted Answer

Configure the indexer maxRunTime property and scale out search units or adjust batch size. Increasing the indexer maxRunTime or increasing search unit capacity helps handle large workloads during indexing.

Answer

Convert all vector fields to Edm.String

Answer

Switch the search service to the Free tier

Answer

Delete all semantic configurations from the index

Question 7

When configuring vector search in Azure AI Search, which TWO parameters must be defined when creating a vector profile? (Choose two)

Accepted Answer

algorithm. A vector profile requires a profile name, an algorithm configuration name, and optionally a vectorizer name.

Answer

sqlConnectionString

Answer

vmSize

Answer

cosmosDatabaseName

Question 8

You are designing a high-performance RAG data layer architecture using Azure AI Search. Which THREE indexing and retrieval strategies should you implement to maximize recall and precision? (Choose three)

Accepted Answer

Enable semantic ranker to re-order top results using deep learning models. Maximizing recall and precision involves using HNSW indexing with tuned parameters, implementing hybrid search (BM25 + vector), and applying semantic re-ranking.

Answer

Disable all scoring and ranking algorithms to save CPU cycles

Answer

Store embeddings as plain text strings in unindexed fields

Question 9

You are writing a query in Azure Cosmos DB for NoSQL to calculate the vector distance between a stored embedding and a query vector. You want to retrieve only the top 5 most similar documents. What is the correct structure of the LIMIT or TOP clause?

Accepted Answer

SELECT TOP 5 c.id, VectorDistance(c.vector, [0.1, 0.2, ...]) AS score FROM c ORDER BY VectorDistance(c.vector, [0.1, 0.2, ...]). Cosmos DB uses SELECT TOP 5 ... ORDER BY VectorDistance(...) to retrieve the top 5 nearest neighbors.

Answer

SELECT LIMIT 5 c.id FROM c ORDER BY c.vector

Answer

SELECT c.id FROM c SORT BY VectorDistance LIMIT 5

Answer

SELECT c.id FROM c WHERE VectorDistance(c.vector, [...]) < 5

Question 10

Which Azure AI Search field property indicates whether the contents of a field can be returned in search query result documents?

Accepted Answer

retrievable. The retrievable property in Azure AI Search field definitions determines whether field values appear in search response documents.

Answer

searchable

Answer

facetable

Answer

filterable

Question 11

You are designing an AI architecture that stores vector embeddings in Azure Cosmos DB for NoSQL. You need to implement role-based access control (RBAC) so that specific application microservices can only execute read-only queries against the database without having permission to write or delete documents. How should you configure this?

Accepted Answer

Create a custom SQL role definition with read-only data actions and assign it to the microservice's managed identity. Azure Cosmos DB supports custom SQL role definitions with specific data actions (such as read vs write) assigned via role assignments to managed identities or service principals.

Answer

Configure Azure Blob Storage RBAC permissions on the Cosmos DB container

Answer

Share the primary master master-key with all microservices

Answer

Disable authentication on the Cosmos DB account during query execution

Question 12

When configuring vector search in Azure AI Search, which TWO components are required to successfully define and execute a vector search query? (Choose two)

Accepted Answer

A vector query payload specifying the query vector and target field. Executing a vector search query requires a vector-typed field in the index and a query request specifying the vector query vector values and target field.

Answer

A Cosmos DB container partition key

Answer

An Azure SQL Server database connection string

Answer

An HDInsight Hadoop cluster endpoint

Question 13

You are designing a disaster recovery and backup strategy for Azure Cosmos DB for NoSQL storing vector embeddings. Which THREE backup options or capabilities are available? (Choose three)

Accepted Answer

Restoring backed-up data to a new Azure Cosmos DB account. Azure Cosmos DB provides continuous backups (point-in-time restore) and periodic backups, allowing restoration to a new account.

Answer

Direct SQL Server .bak file restoration

Answer

Manual file export via FTP into local VM storage

Question 14

You are building an AI search solution using Azure AI Search. You want to ensure that users searching for acronyms or synonyms receive relevant results even if the exact keyword does not appear in the document. Which feature should you configure?

Accepted Answer

Synonym maps. Synonym maps in Azure AI Search allow you to map terms (e.g., AI -> Artificial Intelligence) so queries automatically expand to include synonyms.

Answer

Scoring profiles

Answer

Semantic ranker

Answer

Vector profiles

Question 15

Which property in an Azure Cosmos DB for NoSQL vector embedding policy defines whether the similarity calculation measures cosine, dotproduct, or euclidean distance?

Accepted Answer

distanceFunction. The distanceFunction property in the vector embedding policy defines the metric used for vector distance calculation.

Answer

metricType

Answer

algorithmFunction

Answer

similarityMetric