AI-200 Practice Test 5 — 25 Questions

Question 1

You are troubleshooting a high memory consumption issue on an Azure AI Search service hosting large vector indexes. Which index configuration choice contributes most directly to high memory pressure when using the HNSW algorithm?

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

Accepted Answer

Storing raw uncompressed vectors in memory for HNSW graph traversal. HNSW indexes must load the entire vector graph structure into RAM to execute fast approximate nearest neighbor searches, making memory sizing critical.

Answer

Configuring high-frequency indexers

Answer

Enabling semantic ranking on text fields

Answer

Using exhaustiveKnn instead of HNSW

Question 2

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

Answer

Azure Storage Explorer

Answer

Cosmos DB Data Explorer

Question 3

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

Answer

SQL Server Integration Services (SSIS) file import

Answer

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

Question 4

You are implementing a multi-modal AI application that searches across both text descriptions and product images stored in Azure AI Search. Which feature allows you to query both modalities within a unified search index?

Accepted Answer

Multiple vector fields with distinct dimensions and vectorizers in a single index. Azure AI Search supports multiple vector fields within a single index schema, allowing different fields to use different vector dimensions and embedding models (e.g., text embeddings and image embeddings).

Answer

Single-dimension global vector index restriction

Answer

Mandatory unification of text and image vectors into a single 1536-dimensional field

Answer

External fusion proxy service

Question 5

Your team is implementing hybrid search in Azure AI Search combining full-text keyword search and vector search. Users report that certain exact product serial numbers are missing from the semantic ranking results. Which query parameter should you adjust to balance the influence of BM25 text search scores against vector cosine similarity scores?

Accepted Answer

Enabling semantic ranking and providing a semanticConfiguration with targeted weight overrides.. When performing hybrid queries in Azure AI Search, the search engine uses Reciprocal Rank Fusion (RRF) to combine results from text and vector searches. You can influence the weighting or use standard RRF parameters, but adjusting semantic ranking configurations or ensuring proper semantic configuration boosts can address relevance blending. Specifically, adjusting query parameters like semantic configuration or custom scoring profiles allows fine-tuning.

Answer

Increasing the efSearch parameter in the index definition.

Answer

Adjusting the top parameter to retrieve a larger candidate pool before RRF combination.

Answer

Switching the vector similarity metric from cosine to euclidean distance.

Question 6

When migrating data into Azure AI Search for vector search, you encounter issues with ingestion. Which TWO of the following are valid reasons why an indexer might fail to ingest vector data?

Accepted Answer

The vector dimensions in the source data exceed the index field's configured dimensions.. Indexer failures often stem from schema mismatches (e.g., incorrect field type) or capacity issues (e.g., exceeding the maximum allowable dimensions for a vector).

Answer

The embedding model used is deprecated.

Answer

The 'Collection(Edm.Single)' field is marked as 'filterable'.

Answer

The indexer is not configured with an 'AzureOpenAI' skill.

Question 7

You are designing an AI application that needs to route real-time telemetry events from thousands of IoT devices to multiple downstream serverless functions simultaneously. Which Azure service should you use as the event broker?

Accepted Answer

Azure Event Grid. Azure Event Grid is designed for reactive programming and reliable event routing at scale, making it ideal for routing telemetry metadata or state changes to serverless functions.

Answer

Azure Service Bus Queues

Answer

Azure Event Hubs

Answer

Azure Blob Storage

Question 8

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 9

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 10

Your team is choosing between using Azure AI Search or Cosmos DB for NoSQL for a new project. Which TWO factors are key considerations that favor Azure AI Search?

Accepted Answer

Need for deep integration with full-text search (BM25) and hybrid search.. Azure AI Search offers built-in support for full-text search (BM25) and advanced re-ranking, which are not native features of Cosmos DB's vector implementation.

Answer

Requirement to store petabytes of data.

Answer

Requirement for high-frequency transactional ACID updates to metadata.

Answer

Need for lowest possible latency at the database level.

Question 11

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 12

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 13

When storing vector embeddings in Azure Cosmos DB for MongoDB (vCore), which distance metric is natively supported when creating a vector search index?

Accepted Answer

Cosine distance (cos). Azure Cosmos DB for MongoDB (vCore) supports cosine distance, inner product, and Euclidean distance for vector search indexing.

Answer

Levenshtein distance

Answer

Jaccard distance

Answer

Hamming distance

Question 14

You are evaluating Cosmos DB for NoSQL vs. Azure AI Search for a vector search application. Your application requires strict ACID compliance for metadata updates along with vector search. Which should you choose and why?

Accepted Answer

Cosmos DB because it supports document-level ACID transactions.. Cosmos DB for NoSQL provides strong consistency and ACID transactions, which is critical for metadata consistency, while offering built-in vector search capabilities.

Answer

Cosmos DB because it is the only service that supports vectors.

Answer

Azure AI Search because it supports better hybrid search.

Answer

Azure AI Search because it uses HNSW for faster searches.

Question 15

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 16

You are storing embeddings generated by text-embedding-3-small in Azure Cosmos DB for NoSQL. Which indexing policy property must be explicitly configured to enable vector search queries on your container?

Accepted Answer

Vector embedding policy and vector indexes. To run vector search queries in Azure Cosmos DB for NoSQL, the container's indexing policy must include a dedicated vector embedding path with a datatype of 'vector'.

Answer

Full-text search indexing policy

Answer

Composite indexes containing string fields

Answer

Included paths with geospatial indexes

Question 17

You are troubleshooting slow query performance in an enterprise Azure AI Search vector deployment. Which THREE architectural or configuration adjustments can improve query throughput and latency? Choose three.

Accepted Answer

Scale out the number of search service replicas. To improve vector search performance in Azure AI Search, practitioners can scale out replicas, tune HNSW efSearch parameters, and leverage batching or appropriate tier sizing.

Answer

Replace all vector fields with standard ASCII folding tokenizers

Answer

Configure the index to use exhaustive KNN exclusively for high-QPS production workloads

Question 18

Your application publishes events to an Azure Event Grid topic. You want to ensure that event publishers must present a valid shared access signature or key in the HTTP request header. Which header name does Event Grid expect for key-based authentication?

Accepted Answer

aeg-sas-key. Event Grid accepts keys via the 'aeg-sas-key' header for SAS keys or 'aeg-sas-token' for SAS tokens.

Answer

Authorization

Answer

x-functions-key

Answer

sb-connection-string

Question 19

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 20

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