AI-200 Practice Test 26 — 15 Questions

Question 1

You are troubleshooting a slow vector query performance issue in Azure Cosmos DB for NoSQL. Which THREE actions can you take to improve vector search execution times? (Choose three)

Accepted Answer

Use the diskANN indexing type instead of flat indexes for large collections. Improving vector search performance in Cosmos DB involves selecting efficient index types like diskANN, tuning query parameters, and ensuring proper logical partitioning.

Answer

Convert all vector embeddings to base64 string format

Answer

Disable all non-vector indexing policies on the container

Question 2

Your team is running an AI copilot application that uses Azure OpenAI. To comply with data residency regulations, you need to verify that prompts and completions are processed entirely within a specific Azure region and that no data is persisted outside that region. Where can you verify regional data processing and residency commitments?

Accepted Answer

Azure OpenAI service compliance terms and data residency documentation in the Azure Trust Center. Microsoft's Trust Center and Azure Compliance Documentation, along with service-level data residency specifications in the Azure OpenAI documentation, outline data processing and storage locations.

Answer

Azure Resource Graph queries filtering by region

Answer

Azure Monitor geographic latency metrics dashboard

Answer

Application Insights dependency maps

Question 3

You are implementing a RAG solution where user queries are matched against an Azure AI Search vector index. You want to ensure that search results only return documents where a specific metadata field 'securityLevel' matches the user's clearance. How should you apply this filter?

Accepted Answer

Pass an OData filter expression in the 'filter' parameter of the search query request. Vector queries in Azure AI Search support OData filtering via the 'filter' parameter, allowing you to combine semantic vector search with strict metadata filtering.

Answer

Create a separate Azure AI Search service for each security level

Answer

Encode the security level into the vector embedding values directly

Answer

Filter the results in application memory after receiving all 1000 documents

Question 4

You are designing a data layer architecture in Azure Cosmos DB for NoSQL for an AI application that performs high-frequency writes and real-time vector searches. You need to understand how vector indexing impacts write operations. What is the primary trade-off?

Accepted Answer

Writing documents consumes more Request Units (RUs) because vector indexes must be updated. Indexing vector embeddings requires maintaining complex index structures (like diskANN graphs), which increases RU (Request Unit) consumption during document inserts and updates.

Answer

Vector properties cannot be updated after initial document creation

Answer

Writes are completely blocked until the vector index reaches 100% synchronization

Answer

Writes require explicit distributed transactions across regions

Question 5

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.. Syncing Azure Cosmos DB with Azure AI Search can be achieved natively using Azure Cosmos DB change feed integration via Azure Functions, using built-in Azure AI Search indexers configured with an Azure Cosmos DB change feed data source, or via custom event-driven pipelines using Azure Event Grid.

Answer

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

Answer

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

Question 6

You are troubleshooting a performance degradation in an Azure Cosmos DB for NoSQL vector search application. Which THREE diagnostic steps or metrics should you inspect? (Choose three)

Accepted Answer

Normalized RU consumption across physical partitions to check for hot spotting. Troubleshooting Cosmos DB performance involves inspecting RU charge per vector query, monitoring partition-level throughput distribution, and analyzing indexing status.

Answer

Azure Blob Storage tier status (Hot vs Cool)

Answer

App Service SSL certificate expiration date

Question 7

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

Accepted Answer

TOP. Cosmos DB uses the standard SQL TOP clause to limit the number of results returned by a query.

Answer

FETCH FIRST

Answer

LIMIT

Answer

MAXROWS

Question 8

You need to secure access to an Azure AI Search service so that a Python web app running on Azure App Service can query indices without embedding API keys in configuration files. What is the most secure authentication mechanism to configure?

Accepted Answer

Enable a system-assigned managed identity on App Service and assign the Search Index Data Reader role on Azure AI Search. Azure RBAC using managed identities allows the App Service to authenticate to Azure AI Search securely via Azure Active Directory tokens without using admin API keys.

Answer

Configure an IP firewall on Azure AI Search to allow only the static outbound IP of App Service

Answer

Store the primary admin API key in Azure App Service configuration environment variables

Answer

Use shared access signatures (SAS) tokens generated by a client script

Question 9

You have a custom machine learning model deployed as a managed online endpoint in Azure Machine Learning. You need to ensure that scoring requests made to the endpoint are encrypted in transit and that client authentication is enforced using JSON Web Tokens (JWT). What authentication method does the managed online endpoint support by default?

Accepted Answer

Token-based authentication using Azure AD access tokens and key-based authentication. Managed online endpoints in Azure Machine Learning support key-based authentication and Azure Active Directory (Azure AD) token-based authentication out of the box.

Answer

Shared Access Signatures (SAS) tokens generated from Azure Blob Storage accounts

Answer

Basic HTTP authentication with plain-text username and password headers

Answer

Anonymous access with rate limiting enabled via network security groups

Question 10

You are configuring an Azure AI Search index with vector fields. You need to ensure that filtering operations (such as matching a category ID) are executed with 100% precision before or alongside the vector search. Which vector filter mode should you choose?

Accepted Answer

preFilter mode. Pre-filtering (preFilter) applies the OData filter before the vector search execution, ensuring that only documents matching the filter are considered in the vector similarity calculation.

Answer

approximateFilter mode

Answer

hybridFilter mode

Answer

postFilter mode

Question 11

You are designing an application that stores vector embeddings in Azure Cosmos DB for NoSQL. You want to ensure that vectors are stored securely and that all data at rest is encrypted using customer-managed keys (CMK). How is this achieved in Cosmos DB?

Accepted Answer

Configure customer-managed keys (CMK) at the Azure Cosmos DB account level referencing Azure Key Vault. Customer-managed keys in Azure Cosmos DB are configured at the Azure Cosmos DB account level using Azure Key Vault integration.

Answer

Enable encryption inside the vector embedding policy JSON definition

Answer

Encrypt each individual vector array in client application code before inserting

Answer

Store vectors in Azure Blob Storage with a CMK policy

Question 12

When configuring an Azure AI Search index, which TWO data types can be used for metadata fields that require exact filtering (e.g., category eq 'electronics')? (Choose two)

Accepted Answer

Edm.Int32 (with filterable: true). Edm.String and Edm.Int32 or Edm.Boolean are standard filterable field types in Azure AI Search for exact metadata filtering.

Answer

Binary stream objects

Answer

Edm.ComplexType without sub-properties

Answer

Edm.SingleCollection (vector fields)

Question 13

You are building an AI search solution in Azure AI Search. You need to ensure that when documents are deleted from the primary database, they are also automatically removed from the search index without requiring manual API calls. How should you configure the indexer?

Accepted Answer

Configure a soft-delete policy on the indexer referencing a deletion tracking column or flag in the data source. Azure AI Search indexers support change detection and soft-delete policies (such as Soft Delete Column) to automatically reflect deletions in the search index.

Answer

Write a custom Azure Function triggered by every delete event to issue an HTTP DELETE to the search index

Answer

Re-create the entire search index from scratch daily

Answer

Set the index retention policy to auto-purge every 24 hours

Question 14

You are configuring a vector index in Azure AI Search and want to apply scalar quantization to reduce the memory footprint. What is the primary effect of scalar quantization on float32 vector data?

Accepted Answer

It compresses float32 vectors to int8 integers, reducing memory usage by approximately 75%. Scalar quantization converts 32-bit floating-point numbers into 8-bit integers (int8), reducing memory consumption by roughly 4x while maintaining high search recall accuracy.

Answer

It increases vector dimensions from 1536 to 3072 to improve accuracy

Answer

It eliminates the need for an HNSW graph structure

Answer

It converts vectors into plain text strings for keyword indexing

Question 15

When designing an Azure AI Search index schema, which TWO properties must be defined for every field in the index? (Choose two)

Accepted Answer

name. Every field in an Azure AI Search index definition must have a name and a type (Edm data type).

Answer

analyzerName

Answer

scoringProfile

Answer

vectorDimensions