AI-200 Data Management Services And Vector Search Practice Test 3 — 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

When configuring an Azure AI Search vector index, which TWO parameters can be adjusted within the HNSW algorithm configuration to trade off between search speed, memory usage, and recall accuracy? (Choose two)

Accepted Answer

efSearch. In HNSW, parameters like m (links per node) and efSearch (size of candidate list during search) allow tuning between recall accuracy and query performance.

Answer

replicaCount

Answer

partitionCount

Answer

maxTextLength

Question 3

You are planning the disaster recovery and high availability strategy for an Azure AI Search service storing critical vector indexes for an AI assistant. Which THREE practices should you implement? (Choose three)

Accepted Answer

Configure at least two replicas for high availability during service updates. Ensuring high availability and DR for Azure AI Search involves configuring multiple replicas, deploying across Availability Zones, and establishing geo-replication architectures.

Answer

Store all vector embeddings as flat text files in local VM temp storage

Answer

Rely solely on the Free tier with automated backups

Question 4

You are writing a Python application using the Azure Cosmos DB Python SDK to perform a vector search. You execute a parameterized SQL query containing VectorDistance(). What must be passed as a query parameter for the VectorDistance function to evaluate correctly against the container's vector property?

Accepted Answer

A Python list of float values representing the query embedding. When using VectorDistance() in Cosmos DB SQL queries, the target vector must be passed as a parameter array of floats corresponding to the model dimensions.

Answer

A base64 encoded string of the text

Answer

A connection string to Azure OpenAI Service

Answer

A file path pointing to a local .npy numpy file

Question 5

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 6

Which Azure service provides a fully managed NoSQL database with native vector search capabilities and turnkey multi-region replication?

Accepted Answer

Azure Cosmos DB. Azure Cosmos DB provides managed NoSQL capabilities with built-in vector search and global distribution.

Answer

Azure Table Storage

Answer

Azure Blob Storage

Answer

Azure SQL Database

Question 7

When designing an AI architecture that uses Azure AI Search, which TWO methods can be used to ingest documents and their vector embeddings into the search index? (Choose two)

Accepted Answer

Push API using HTTP POST requests to upload documents directly. Azure AI Search supports pushing data via the Push API (REST/SDK) or pulling data using indexers connected to data sources like Blob Storage or Cosmos DB.

Answer

Direct mounting of an NFS network share as the search index

Answer

FTP file transfer into the search engine storage directory

Answer

Direct SQL Server backup restore (.bak file import)

Question 8

You are optimizing an Azure Cosmos DB for NoSQL vector search container. Which THREE properties must be correctly configured inside the vectorEmbeddingPolicy JSON object? (Choose three)

Accepted Answer

path. The vectorEmbeddingPolicy in Cosmos DB requires defining paths, dataTypes, distanceFunctions, and dimensions for each vector property.

Answer

httpPort

Answer

sslEnabled

Question 9

You are building a chat application with memory using Azure AI Search. You want to retrieve previous conversation turns that are semantically similar to the current user prompt. What search mode should you use if you want to execute both a vector search and a keyword search simultaneously and combine their results?

Accepted Answer

Hybrid search. Executing vector and keyword search simultaneously in Azure AI Search is known as hybrid search.

Answer

Exhaustive k-NN search

Answer

Wildcard search

Answer

Fuzzy search only

Question 10

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 11

You are managing an Azure AI Search index with millions of high-dimensional vectors. Users experience intermittent timeout errors during peak query hours. Upon investigation, you find that the search service CPU utilization is at 100%. What is the most effective architectural mitigation?

Accepted Answer

Scale out the search service by adding search units (replicas). Scaling out search units (adding replicas) increases query throughput and distributes CPU load across multiple nodes.

Answer

Increase the embedding dimension size from 1536 to 3072

Answer

Switch the index from HNSW to exhaustive k-NN search

Answer

Decrease the number of replicas to reduce network chatter

Question 12

When configuring vector search in Azure Cosmos DB for NoSQL, which TWO components must be defined within the container's indexing policy? (Choose two)

Accepted Answer

vectorEmbeddingPolicy. Cosmos DB vector indexing requires defining both the vector embedding policy and the vector indexes array in the indexing policy.

Answer

fullTextPolicy

Answer

spatialIndexes

Answer

partitionKeyDefinition

Question 13

You are developing a RAG solution and need to evaluate the quality of your vector retrieval results from Azure AI Search. Which metric is commonly used to measure the proportion of relevant documents retrieved in the top-k results?

Accepted Answer

Recall@k. Recall@k measures the proportion of relevant items found in the top-k retrieved results relative to all relevant items.

Answer

HNSW efSearch latency

Answer

Request Units per second (RU/s)

Answer

BM25 term frequency

Question 14

You are designing a secure enterprise generative AI platform using Azure AI Search and Azure OpenAI. Which THREE security practices should you implement to protect sensitive data and access? (Choose three)

Accepted Answer

Implement Azure RBAC to restrict index management and data query permissions. Securing AI search and embedding stores involves using managed identities, restricting network access via private endpoints, and enforcing role-based access control.

Answer

Disable transport layer security (TLS) to speed up embedding transmission

Answer

Store all API keys in plain text configuration files on public web servers

Question 15

You are designing a high-throughput RAG pipeline where documents are continuously ingested and queried. You choose Azure Cosmos DB for NoSQL as your vector store. How does Cosmos DB handle vector indexing updates when new documents are inserted?

Accepted Answer

Vector indexes are updated asynchronously in the background by the database engine. Azure Cosmos DB indexes vector embeddings asynchronously in the background as documents are written, balancing write performance with index availability.

Answer

You must manually trigger an index rebuild stored procedure every hour

Answer

Vector search is completely unavailable until the entire collection is re-indexed from scratch

Answer

New documents cannot be queried using VectorDistance until the container is restarted