AI-200 Practice Test 10 — 10 Questions

Question 1

You are writing a query in Azure Cosmos DB for NoSQL that sorts results by vector similarity and also filters results by a category property. How must you order the clauses in your SQL statement?

Accepted Answer

The WHERE clause must come before the ORDER BY VectorDistance() clause. In Azure Cosmos DB, when using VectorDistance in an ORDER BY clause along with a WHERE filter, the WHERE clause must precede the ORDER BY clause.

Answer

VectorDistance must be placed in the SELECT clause instead of ORDER BY

Answer

WHERE and ORDER BY clauses cannot be used in the same query when using vectors

Answer

The ORDER BY clause must come before the WHERE clause

Question 2

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 3

Which Azure portal blade allows you to view and manage access keys and connection strings for an Azure AI Search service?

Accepted Answer

Keys. The 'Keys' blade in the Azure Portal provides admin and query API keys for Azure AI Search.

Answer

Data source

Answer

Certificates

Answer

Scale

Question 4

When configuring vector search in Azure AI Search, which TWO components are required to successfully define and execute a vector search query? (Choose two)

Accepted Answer

A vector query payload specifying the query vector and target field. Executing a vector search query requires a vector-typed field in the index and a query request specifying the vector query vector values and target field.

Answer

A Cosmos DB container partition key

Answer

An Azure SQL Server database connection string

Answer

An HDInsight Hadoop cluster endpoint

Question 5

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 6

You are migrating a large vector dataset into an Azure AI Search index. To maximize ingestion speed and throughput, you temporarily adjust index settings. Which index configuration property should you modify during bulk ingestion?

Accepted Answer

Scale out search units (replicas and partitions) to distribute ingestion load. During heavy bulk ingestion, increasing the replica and partition count or adjusting merge/indexing batch sizes improves speed, but specifically, scaling out search units or temporarily disabling semantic ranker overhead optimizes throughput.

Answer

Set HNSW efConstruction to 10,000 to maximize graph build precision

Answer

Disable all scoring profiles and set vector dimensions to 1

Answer

Switch the index tier from Standard to Free

Question 7

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.

Answer

The 'vectorQueries' parameter

Answer

The 'top' parameter

Answer

The 'api-version' parameter

Question 8

You are designing an enterprise RAG application that stores vector embeddings in Azure AI Search. You want to ensure that if the primary Azure region goes down, your search service automatically fails over with minimal data loss. How should you design your geo-replication strategy?

Accepted Answer

Configure geo-replication with replica services in a paired Azure region, enabling read-only failover and re-indexing capabilities. Deploying Azure AI Search with geo-replication across paired Azure regions ensures read availability, while writes can be resubmitted to the primary region upon recovery.

Answer

Export search indexes to CSV files hourly and upload them manually

Answer

Rely on Azure Blob Storage geo-redundancy to automatically mirror search index memory

Answer

Configure synchronous multi-region write master locks across 10 regions simultaneously

Question 9

Which Azure AI Search field property indicates whether the contents of a field can be returned in search query result documents?

Accepted Answer

retrievable. The retrievable property in Azure AI Search field definitions determines whether field values appear in search response documents.

Answer

searchable

Answer

facetable

Answer

filterable

Question 10

You are optimizing an enterprise search architecture using Azure AI Search. Which THREE mechanisms help control latency and resource consumption during heavy vector query loads? (Choose three)

Accepted Answer

Tuning HNSW efSearch parameter to balance recall speed and latency. To control latency and resource consumption during heavy vector loads, you can scale out replicas, apply scalar quantization, and tune HNSW efSearch parameters.

Answer

Disabling caching and increasing index document count per partition beyond limits

Answer

Running exhaustive k-NN searches for every query by default