AI-200 Practice Test 25 — 15 Questions

Question 1

When querying an Azure AI Search vector index using the REST API or SDK, what parameter specifies the number of nearest neighbors to retrieve?

Accepted Answer

k. The nearest neighbors query parameter in Azure AI Search vector queries is k (or kNearestNeighbors).

Answer

radius

Answer

top

Answer

depth

Question 2

You are building a hybrid search solution using Azure AI Search that combines BM25 full-text search with vector search. What is the name of the feature that intelligently combines and normalizes scores from different retrieval systems before presenting the final top results?

Accepted Answer

Reciprocal Rank Fusion (RRF). Azure AI Search uses Reciprocal Rank Fusion (RRF) as part of semantic ranking and hybrid search score combination to merge disparate result lists.

Answer

Min-Max Score Scaling

Answer

Cosine Distance Normalization

Answer

Cross-Encoder Re-ranking

Question 3

You are designing an enterprise search architecture on Azure that requires hybrid search capabilities. Which THREE components or features must be configured to successfully execute a hybrid query with semantic ranking in Azure AI Search? (Choose three)

Accepted Answer

A searchable Edm.String text field for keyword matching. To run hybrid search with semantic ranking, you need a vector field, a text field for BM25 keyword search, and semantic configuration enabled on the index.

Answer

Azure Blob Storage configured as an external data source

Answer

An Azure Functions application for manual score merging

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 troubleshooting a Python application that uses Azure SDK libraries to interact with Azure AI services. The application is flooding Log Analytics with excessive telemetry, causing unexpected cost increases. You want to dynamically adjust the sampling rate of Application Insights without redeploying application code. How can you achieve this?

Accepted Answer

Configure adaptive sampling in the Application Insights SDK configuration or connection string parameters to automatically reduce telemetry volume. Application Insights supports adaptive sampling and fixed-rate sampling configured via application settings or through the Application Insights portal settings (for certain platforms) or instrumentation key configuration, but adaptive sampling can also be tuned via code or connection strings.

Answer

Modify Azure subscription billing policies to cap Log Analytics data ingestion at zero cost

Answer

Delete the Log Analytics workspace and recreate it with a lower pricing tier

Answer

Use Azure Policy to deny all incoming Application Insights trace requests

Question 6

When configuring an Azure AI Search vectorizer for integrated vectorization, which TWO parameters or connections must be specified to link to an Azure OpenAI deployment? (Choose two)

Accepted Answer

Model name (e.g., text-embedding-ada-002). Integrated vectorization requires configuring the model name (e.g., text-embedding-ada-002) and the resource URL/authentication details of the Azure OpenAI service.

Answer

SQL Server connection string

Answer

Cosmos DB container database name

Answer

Blob storage SAS token expiration time

Question 7

You are troubleshooting a scenario where hybrid queries in Azure AI Search are returning unexpected relevance rankings. Which THREE factors could influence the final score when using hybrid search with semantic ranking? (Choose three)

Accepted Answer

Semantic ranker deep learning re-scoring. Hybrid search scores are influenced by BM25 full-text scores, vector similarity scores, RRF normalization, and semantic ranker re-scoring.

Answer

Virtual machine CPU core count assigned to the client application

Answer

Azure Blob Storage container access tier (Hot vs Cool)

Question 8

You are implementing a robust data layer architecture for an enterprise generative AI application. Which THREE factors should you evaluate when deciding between Azure AI Search and Azure Cosmos DB for vector search? (Choose three)

Accepted Answer

Need for automated multi-region replication with single-digit millisecond latency reads. When choosing between Azure AI Search and Cosmos DB for vector search, factors such as full-text search capabilities, transactional operational database needs, and scaling requirements are critical.

Answer

Requirement to run Hadoop MapReduce jobs directly over the vector indexes

Answer

Requirement to store vector embeddings as relational SQL tables with foreign key constraints

Question 9

You are configuring data ingestion into an Azure AI Search index using an Azure AI Search indexer. The source data resides in Azure Blob Storage and consists of PDF documents. You want to chunk the documents and generate embeddings during ingestion without writing custom orchestration code. Which feature should you configure?

Accepted Answer

Integrated vectorization with built-in document splitting and vectorizer skillset configuration. Azure AI Search supports built-in chunking and vectorization capabilities within skillsets or integrated vectorization to parse, chunk, and embed documents automatically via indexers.

Answer

Azure Data Factory mapping data flows with vector transformations

Answer

A custom WebJob running LangChain

Answer

Cosmos DB change feed triggers

Question 10

You are building a multi-modal AI application that searches across both product images and product descriptions. You store your data in Azure AI Search. How should you define your index schema to support searching with both image embeddings and text embeddings?

Accepted Answer

Define two separate Edm.SingleCollection vector fields with distinct dimensions and vector profiles. To support multi-modal search, you define two separate vector fields in the index schema, each with its own dimensions and vector profile mapped to the respective embedding model.

Answer

Concatenate the image and text embeddings into a single 3072-dimensional vector field

Answer

Use a single Edm.String field containing both embeddings as JSON strings

Answer

Create two completely separate search services

Question 11

You are configuring an Azure AI Search index schema via the REST API. You need to define a field that will hold a 1536-dimensional vector. Which field type must you assign to this field?

Accepted Answer

Edm.SingleCollection. In Azure AI Search, fields intended to store embeddings must be explicitly typed as Edm.SingleCollection with dimensions defined.

Answer

Edm.Vector

Answer

Edm.String

Answer

Edm.DoubleCollection

Question 12

You are configuring a vector search profile in Azure AI Search. Which TWO parameters are required when defining an algorithm configuration using the HNSW algorithm? (Choose two)

Accepted Answer

efConstruction. When configuring an HNSW algorithm profile in Azure AI Search, you must define parameters such as m (number of bi-directional links per node) and efConstruction (size of the dynamic candidate list during construction).

Answer

vectorDimensions

Answer

compressionType

Answer

shardCount

Question 13

You are optimizing an Azure AI Search vector index with millions of documents. Users report high latency during vector queries. You decide to implement vector compression to reduce memory footprint and improve search throughput. Which compression method is natively supported in Azure AI Search?

Accepted Answer

Scalar Quantization (SQ). Azure AI Search supports scalar quantization (specifically SQ) to compress 32-bit floating-point vectors into 8-bit integers, significantly reducing memory usage with minimal loss in recall.

Answer

Autoencoders

Answer

Principal Component Analysis (PCA)

Answer

Product Quantization (PQ)

Question 14

You are configuring semantic ranking in Azure AI Search to improve search relevance for your enterprise application. Which tier of Azure AI Search is required to enable semantic ranking?

Accepted Answer

Standard (S1 or higher). Semantic ranking in Azure AI Search is a premium feature available starting from the Standard tier (S1 and above), and is not supported on Free or Basic tiers.

Answer

Storage Optimized (L1)

Answer

Free

Answer

Basic

Question 15

You are developing a retrieval-augmented generation (RAG) solution using Azure AI Search. You need to configure a vector index to store 1536-dimensional embeddings generated by text-embedding-ada-002. Which parameter must you configure in the vector profile's algorithm configuration to use HNSW as the underlying approximate nearest neighbor algorithm?

Accepted Answer

algorithm.name set to hnsw. To configure HNSW in Azure AI Search, you must define a vectorizer and an algorithm configuration specifying algorithm parameters such as m, efConstruction, and metric under the vectorSearch property of the index.

Answer

algorithm.name set to exhaustiveKnn

Answer

vectorSearch.compression set to scalarQuantization

Answer

vectorSearch.algorithm.type set to faiss