AI-200 Data Management Services And Vector Search Practice Test 5 — 15 Questions

Question 1

You are designing a RAG solution where documents are dynamically updated in Azure AI Search. You notice that search results return outdated information immediately after document updates. What is the cause of this behavior?

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

Accepted Answer

The indexer has not yet run its scheduled synchronization cycle to process changes from the data source. Azure AI Search indexers run on schedules or require explicit change detection, meaning changes in the source data are not reflected in the search index until the indexer runs or push updates are submitted.

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Semantic ranker caches search results for exactly 30 days

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Azure OpenAI embedding models require a 24-hour cool-down period before new embeddings become valid

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Vector indexes in Azure AI Search are read-only after creation and cannot accept updates

Question 2

When configuring vector search in Azure Cosmos DB for NoSQL, which TWO data types are valid for storing vector embeddings within the vector embedding policy? (Choose two)

Accepted Answer

float32. Cosmos DB vector embedding policy supports float32 and float16 (or int8 depending on quantization) for vector properties.

Answer

boolean

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guid

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string

Question 3

You are evaluating data layer architectures for an enterprise AI application that requires both vector search and structured transactional data management. Which THREE criteria favor choosing Azure Cosmos DB over Azure AI Search as the primary database? (Choose three)

Accepted Answer

Need for sub-10 millisecond point reads and writes on operational business records. Azure Cosmos DB is preferred when you need ACID transactions, low-latency point reads/writes on transactional records, and native NoSQL document storage alongside vector search.

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Requirement for advanced BM25 keyword search and neural semantic re-ranking

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Requirement to generate automatic PDF text extraction and document chunking via indexers

Question 4

You are implementing vector search in Azure AI Search using the REST API. You want to execute a pure vector query without any keyword matching. What parameter must be omitted or left empty in your query request payload?

Accepted Answer

The 'search' parameter. To execute a pure vector query in Azure AI Search without keyword search, you omit or leave the 'search' parameter empty while providing the 'vectorQueries' parameter.

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The 'vectorQueries' parameter

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The 'top' parameter

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The 'api-version' parameter

Question 5

Which Azure AI Search metric or tool helps administrators monitor query volume, latency, and throttling rates across search units?

Accepted Answer

Azure Monitor metrics and diagnostic logs. Azure Monitor and Log Analytics provide comprehensive telemetry, metrics, and logs for monitoring Azure AI Search performance.

Answer

App Service stream logs

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Azure Storage Explorer

Answer

Cosmos DB Data Explorer

Question 6

You are designing an AI application architecture where embeddings are generated on-the-fly by an Azure Function and stored in Azure Cosmos DB for NoSQL. During peak load, the Azure Function times out due to rate limits (HTTP 429) from Azure OpenAI Service. What design pattern should you implement to make the ingestion pipeline resilient?

Accepted Answer

Implement an asynchronous message queue using Azure Service Bus to buffer ingestion requests and handle rate-limit retries. Implementing an asynchronous messaging pattern using Azure Service Bus or Event Hubs decouples the client ingestion request from embedding generation, allowing rate limiting and retries.

Answer

Increase the Azure Function execution timeout limit to 24 hours

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Disable SSL verification on the Azure OpenAI client connection

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Switch from Azure OpenAI to local text file storage

Question 7

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

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Edm.ComplexType without sub-properties

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Edm.SingleCollection (vector fields)

Question 8

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 9

You are designing an AI search architecture that requires handling millions of documents with sub-second vector similarity queries. You choose Azure AI Search. How does Azure AI Search ensure high query throughput for vector indexes?

Accepted Answer

By utilizing HNSW approximate nearest neighbor indexing combined with multi-replica scale-out. Azure AI Search uses HNSW approximate nearest neighbor graphs combined with multi-replica scale-out architecture to deliver high throughput and low latency.

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By caching every possible query result indefinitely in memory

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By executing sequential full table scans across all storage nodes simultaneously

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By converting all vectors into relational database tables with SQL foreign keys

Question 10

You are managing an Azure Cosmos DB for NoSQL database containing vector embeddings. You need to perform a migration of embeddings from one container to another without taking the application offline. Which Azure feature should you use?

Accepted Answer

Azure Cosmos DB Change Feed processor. The Azure Cosmos DB change feed streams changes to containers in real time, enabling zero-downtime data migration and synchronization.

Answer

Azure Data Factory tumbling window triggers over Blob Storage

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SQL Server Integration Services (SSIS) file import

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Taking a manual offline portal backup and restoring to a new account

Question 11

When configuring an Azure AI Search index, which TWO field attributes are required if a field is going to be used for full-text keyword search and filtering? (Choose two)

Accepted Answer

searchable. To search and filter text fields, the field must be marked as searchable (for BM25 search) and filterable (for OData filters).

Answer

vectorSearchProfile

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facetable

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sortable

Question 12

You are designing a high-scale vector search architecture using Azure AI Search. Which THREE operational and architectural best practices should you follow to ensure optimal performance and cost-efficiency? (Choose three)

Accepted Answer

Balance replica and partition counts based on your query throughput and index size requirements. Best practices for Azure AI Search include monitoring resource utilization, right-sizing replicas and partitions, and leveraging vector compression (scalar quantization).

Answer

Store all raw PDF files inside the vector float32 array fields

Answer

Deploy all indexes on the Free tier to eliminate infrastructure costs

Question 13

Which Azure AI Search capability allows you to extract text from images and PDF documents during indexer execution using AI cognitive skills?

Accepted Answer

Skillsets. Skillsets in Azure AI Search enable cognitive enrichment, allowing tasks like OCR and entity extraction during ingestion.

Answer

Semantic configurations

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Scoring profiles

Answer

Vector profiles

Question 14

You are writing a Python script to query an Azure AI Search index using the azure-search-documents SDK. You want to retrieve both vector matches and keyword matches using hybrid search. Which class should you instantiate to represent the vector query?

Accepted Answer

VectorizedQuery. The VectorizedQuery class in the Azure AI Search Python SDK is used to construct vector queries for hybrid or vector-only search requests.

Answer

EmbeddingQueryClient

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HnswSearchRequest

Answer

CosmosVectorSearch

Question 15

You are designing a high-availability RAG architecture where search queries must succeed even if an entire Azure region experiences an outage. You deploy Azure AI Search in a primary region and a secondary region. How should client applications handle failover?

Accepted Answer

Use Azure Traffic Manager or Azure Front Door in front of regional search service endpoints with health probes. Applications implement traffic routing via Azure Traffic Manager or Azure Front Door pointing to both search service regional endpoints, combined with geo-replication of search indexes.

Answer

Hardcode the secondary search IP address in client application configuration files

Answer

Rely on Cosmos DB automatic failover to replicate search indices

Answer

Configure SQL replication between search indexes