AI-200 Practice Test 12 — 15 Questions

Question 1

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?

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

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 2

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 3

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 4

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 5

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

Question 6

You are integrating Azure OpenAI Service's text-embedding-ada-002 model with Azure AI Search. You want to automate the generation of embeddings directly inside the search service during data ingestion. Which component of Azure AI Search should you configure?

Accepted Answer

Azure AI Search Indexer with integrated vectorization. Indexers in Azure AI Search support integrated vectorization, allowing you to connect to Azure OpenAI to automatically chunk text and generate embeddings during the indexing pipeline.

Answer

Semantic ranker configuration profile

Answer

Azure Function scheduled trigger for batch updates

Answer

Custom Web API skill in Azure AI Services enrichment pipeline

Question 7

You are storing vector embeddings generated by Azure OpenAI in Azure Cosmos DB for NoSQL. Which built-in SQL function must you use within your query to compute vector distance when executing a similarity search?

Accepted Answer

VectorDistance(). Azure Cosmos DB for NoSQL provides native vector search functions including VectorDistance, which computes the distance between two vectors using Cosine, DotProduct, or Euclidean distance metrics.

Answer

EmbeddingDistance()

Answer

GetVectorDistance()

Answer

CosineSimilarity()

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

When designing an Azure AI Search solution, you need to store high-dimensional embeddings. Which field type must be used to store these vector representations correctly?

Accepted Answer

Collection(Edm.Single). In Azure AI Search, vector embeddings must be defined using the 'Collection(Edm.Single)' field type.

Answer

Edm.Double

Answer

Edm.String

Answer

Collection(Edm.Int32)

Question 10

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 11

You are implementing a hybrid search solution in Azure AI Search that combines BM25 keyword search scores and HNSW vector search scores. By default, how does Azure AI Search normalize and combine these disparate score spaces before applying the RRF algorithm?

Accepted Answer

Reciprocal Rank Fusion (RRF) ranking combination. Azure AI Search uses Reciprocal Rank Fusion (RRF) to combine scores from different query types (such as text and vector) by normalizing ranks rather than raw scores, mitigating the scale discrepancy between BM25 and cosine distance.

Answer

Min-max feature scaling on raw scores

Answer

Softmax probability conversion

Answer

Z-score standardization on vector distances

Question 12

You are configuring a Vector Index in Azure AI Search. You need to ensure that the vector similarity search is performed using the Cosine distance metric. Where is this configuration specified?

Accepted Answer

In the 'vectorSearch' section of the index definition.. The distance metric (e.g., cosine, euclidean, dotProduct) is specified within the 'vectorSearch' profile configuration in the index definition.

Answer

In the indexer definition.

Answer

In the search query payload.

Answer

In the field definitions within the index.

Question 13

You are querying an Azure Cosmos DB for NoSQL container using vector search. You execute a query using the VectorDistance() system function. Which query structure is required to properly execute this vector search?

Accepted Answer

SELECT TOP 10 c.id, VectorDistance(c.vectorProp, [0.1, 0.2]) AS score FROM c ORDER BY VectorDistance(c.vectorProp, [0.1, 0.2]). Vector search queries in Azure Cosmos DB for NoSQL require using the VectorDistance system function in the ORDER BY clause to sort by similarity and selecting the top results using TOP.

Answer

SELECT TOP 10 c.id FROM c WHERE VectorDistance(c.vectorProp, [0.1, 0.2]) > 0.9

Answer

JOIN vectorIndex ON c.id = vectorIndex.id

Answer

GROUP BY VectorDistance(c.vectorProp, [0.1, 0.2])

Question 14

When configuring vector search in Azure Cosmos DB for NoSQL, which TWO properties must be defined within the container's vector embedding policy? Choose two.

Accepted Answer

Path (the JSON property containing the vector array). In Azure Cosmos DB for NoSQL, the vector embedding policy requires defining paths (properties containing vectors) and embedding details such as datatype, dimensions, and distance function.

Answer

Container partition key definition

Answer

BM25 k1 and b tuning parameters

Answer

Replication lag threshold

Question 15

You are planning a data layer architecture for an AI application on Azure that requires both relational transactional storage and vector search capabilities. Which TWO Azure database services natively support vector data types and similarity search functions? Choose two.

Accepted Answer

Azure SQL Database. Azure SQL Database and Azure Cosmos DB (both NoSQL and MongoDB vCore APIs) natively support vector data storage and vector search functions.

Answer

Azure Queue Storage

Answer

Azure Table Storage

Answer

Azure Cache for Redis (Basic tier without modules)