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

Question 1

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. Option B (name) is correct because every field in an Azure AI Search index schema requires a unique name that identifies the field within the fields collection, and this name is used in queries, filters, and result documents. Option E (type) is correct because each field must declare an Edm data type (such as Edm.String, Edm.Int32, Edm.Boolean, Edm.DateTimeOffset, or Collection(Edm.String)) so the search engine knows how to index, store, and query the field's values. Option A (analyzerName) is not required for every field; analyzers apply only to searchable string fields and default to the standard Lucene analyzer when omitted. Option C (scoringProfile) is not a field-level property at all; scoring profiles are defined at the index level to influence relevance ranking. Option D (vectorDimensions) is only required for fields of type Collection(Edm.Single) that participate in vector search, not for every field.

Answer

analyzerName

Answer

scoringProfile

Answer

vectorDimensions

Question 2

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 (CMK) for Azure Cosmos DB are configured at the account level, referencing an Azure Key Vault. This enables encryption of all data at rest, including vector embeddings, with keys managed by the customer. This is the correct and only way to achieve CMK encryption for Cosmos DB.

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 3

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. Configuring a soft-delete policy on the indexer referencing a deletion tracking column or flag in the data source allows Azure AI Search to automatically detect and remove documents from the index when they are deleted or marked as deleted in the source. This is the native, automated way to handle deletions without manual API calls.

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 4

You are configuring semantic ranking in Azure AI Search. You need to specify which fields in your index contain the title and content that the semantic ranker should analyze. Where do you configure these field mappings?

Accepted Answer

In the semantic configuration section of the index schema. Semantic ranking in Azure AI Search is configured in the semantic configuration section of the index schema. Within this configuration, you specify which fields contain the title, content, and keywords that the semantic ranker should analyze. This is done by defining a semantic configuration object in the index definition.

Answer

In the indexer skill set cognitive map

Answer

In the Azure OpenAI model deployment properties

Answer

In the vector profile definition

Question 5

Which distance metric in Azure Cosmos DB vector search calculates the inner product of two vectors, measuring both magnitude and angle?

Accepted Answer

dotproduct. The dotproduct distance metric in Cosmos DB vector search computes the inner product of two vectors, which incorporates both magnitude and angle. It is commonly used when vector magnitudes carry meaning, such as in recommendation systems where popularity or confidence scores matter.

Answer

cosine

Answer

euclidean

Answer

manhattan

Question 6

You are optimizing an enterprise Azure Cosmos DB for NoSQL vector database. Which THREE administrative or architectural practices help ensure high availability and predictable performance? (Choose three)

Accepted Answer

Choose a partition key that evenly distributes data and query load across physical partitions. Option A is correct because a well-chosen partition key that evenly distributes data and query load across physical partitions prevents hot partitions, which is essential for predictable performance and scalability in Cosmos DB for NoSQL. Option B is correct because enabling multi-region write replication (multi-master) provides global low-latency writes and improves availability by allowing the account to continue serving requests even if a region becomes unavailable. Option E is correct because provisioning adequate Request Units (RUs) or configuring autoscale throughput ensures the database has enough throughput capacity to handle workload demands, preventing throttling (429 errors) and maintaining predictable performance. Option C is incorrect because storing all documents in a single logical partition without a partition key creates a hot partition and limits scalability, harming both availability and performance. Option D is incorrect because disabling automatic indexing entirely removes the default indexing policy that supports efficient queries, which would degrade query performance rather than improve it.

Answer

Store all documents in a single partition without a partition key

Answer

Disable automatic indexing entirely across all properties

Question 7

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?

Accepted Answer

The indexer has not yet run its scheduled synchronization cycle to process changes from the data source. In Azure AI Search, data source changes are not automatically reflected in the index; an indexer must run to pull, transform, and load updated content. If the indexer is on a schedule (e.g., every 5 minutes or hourly), there is an inherent delay between the source update and the index refresh. Until that synchronization cycle completes, queries hit the old index data, returning outdated results. This is expected behavior, not a bug, and can be mitigated by triggering the indexer on-demand or using a change detection policy.

Answer

Semantic ranker caches search results for exactly 30 days

Answer

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

Answer

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

Question 8

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. Option A is correct because Azure Cosmos DB is designed as a low-latency operational database, offering single-digit millisecond point reads and writes (by key and partition key) for business records, whereas Azure AI Search is a search index optimized for query throughput rather than transactional point operations. Option B is correct because Cosmos DB natively stores schema-flexible JSON documents and, with vector indexing/vector search support, can hold operational documents and their embeddings together in the same container. Option D is correct because Cosmos DB supports multi-document ACID transactions within a single logical partition (transactional batch), which Azure AI Search cannot provide since it is not a transactional database. Option C is not correct because advanced BM25 keyword ranking and neural semantic re-ranking are core capabilities of Azure AI Search, not differentiators of Cosmos DB. Option E is not correct because automatic PDF text extraction, OCR, and document chunking via indexers are Azure AI Search (AI enrichment/indexer) features, not Cosmos DB features.

Answer

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

Answer

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

Question 9

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. In Azure AI Search, a pure vector query is executed by providing the 'vectorQueries' array and omitting or leaving the 'search' parameter empty (or setting it to null). When 'search' is omitted, the service performs only vector similarity search without keyword matching. This is documented behavior for vector-only queries.

Answer

The 'vectorQueries' parameter

Answer

The 'top' parameter

Answer

The 'api-version' parameter

Question 10

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 metrics and diagnostic logs are the native observability tools for Azure AI Search, exposing query volume (SearchQueriesPerSecond), latency (SearchLatency), and throttling (ThrottledSearchQueriesPercentage) broken down by search unit. Diagnostic logs can be routed to Log Analytics, Event Hubs, or Storage for deeper analysis of query and indexing operations. These are the only options that directly instrument the search service itself.

Answer

App Service stream logs

Answer

Azure Storage Explorer

Answer

Cosmos DB Data Explorer

Question 11

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. An asynchronous message queue (Azure Service Bus) decouples the ingestion producer from the Azure OpenAI embedding call, buffering requests during bursts and allowing retries with exponential backoff when HTTP 429 rate limits occur. This prevents Function timeouts because the Function can enqueue work quickly and a separate consumer processes at a controlled rate, absorbing peak load without dropping data.

Answer

Increase the Azure Function execution timeout limit to 24 hours

Answer

Disable SSL verification on the Azure OpenAI client connection

Answer

Switch from Azure OpenAI to local text file storage

Question 12

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. Option A is correct because Normalized RU consumption across physical partitions is the key Azure Cosmos DB metric for detecting hot spotting; if one physical partition consistently shows much higher normalized RU usage than others, vector search throughput is throttled by that partition. Option D is correct because the RU charge returned in each query response header (x-ms-request-charge) reveals the actual cost of each vector query, letting you identify expensive queries and compare them against provisioned throughput. Option E is correct because vector indexing in Cosmos DB for NoSQL is performed by an index transformation, and if that transformation is still in progress or stuck, vector queries may scan more data, return incomplete results, or run slower, so checking index transformation progress is essential. Option B is not relevant because Blob Storage access tiers (Hot vs Cool) affect blob storage cost and latency, not Cosmos DB vector query performance. Option C is not relevant because App Service SSL certificate expiration affects HTTPS connectivity to a web app, not the internal performance of Cosmos DB vector search.

Answer

Azure Blob Storage tier status (Hot vs Cool)

Answer

App Service SSL certificate expiration date

Question 13

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 ensures high query throughput for vector indexes by using HNSW approximate nearest neighbor indexing, which builds a navigable graph for fast similarity search, combined with multi-replica scale-out that distributes query load across replicas. This architecture supports sub-second queries over millions of documents.

Answer

By caching every possible query result indefinitely in memory

Answer

By executing sequential full table scans across all storage nodes simultaneously

Answer

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

Question 14

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 processor provides an ordered, continuous stream of inserts and updates from a source container, which a consumer can read and write into a target container in near real time. This enables online migration of vector embeddings without taking the application offline, since the source container keeps serving reads and writes while the change feed replicates data. It is the canonical pattern for live container-to-container migration in Cosmos DB for NoSQL.

Answer

Azure Data Factory tumbling window triggers over Blob Storage

Answer

SQL Server Integration Services (SSIS) file import

Answer

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

Question 15

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. Option A is correct because Azure AI Search scales along two independent dimensions: replicas provide query throughput and high availability, while partitions provide index storage and write parallelism, so right-sizing both to your QPS and index size is the core capacity-planning practice. Option C is correct because scalar quantization compresses float32 vectors to narrower types (for example int8), cutting memory usage and speeding up HNSW graph traversal with only modest recall loss, which improves cost-efficiency at high scale. Option D is correct because Azure Monitor exposes search unit metrics such as CPU, memory, and storage utilization, letting you detect saturation and resize replicas or partitions before performance degrades. Option B is wrong because vector fields hold numeric embeddings, not raw PDF binaries, which belong in blob storage or a separate content field. Option E is wrong because the Free tier has no SLA, tiny storage and index limits, and cannot support a high-scale production workload.

Answer

Store all raw PDF files inside the vector float32 array fields

Answer

Deploy all indexes on the Free tier to eliminate infrastructure costs