AI-200 Practice Test 13 — 15 Questions

Question 1

You are designing an enterprise search architecture that combines Azure AI Search and Azure OpenAI. Which THREE strategies help optimize hybrid search relevance and retrieval quality? Choose three.

Accepted Answer

Tune HNSW parameters (m and efSearch) based on recall and latency trade-offs. To optimize hybrid search relevance, practitioners use Reciprocal Rank Fusion (RRF), semantic ranking (L2 re-ranking), and proper vector normalization/tuning.

Answer

Disable all OData filters to maximize vector recall

Answer

Store all embeddings as uncompressed plaintext strings in Blob Storage

Question 2

You are configuring a hybrid search in Azure AI Search. You want to combine results from a vector search and a keyword-based search. Which feature is specifically designed to normalize scores from these different retrieval methods into a single ranked list?

Accepted Answer

Reciprocal Rank Fusion (RRF). Reciprocal Rank Fusion (RRF) is the standard algorithm used in Azure AI Search to combine scores from different search mechanisms into a single, cohesive ranking.

Answer

BM25 Scoring

Answer

Semantic Ranker

Answer

Vector Normalization

Question 3

You are optimizing storage costs and search latency for a massive Azure AI Search vector index containing 50 million records, each with 3,072-dimensional embeddings from text-embedding-3-large. Which Azure AI Search feature should you implement to reduce memory footprint while maintaining acceptable recall?

Accepted Answer

Scalar Quantization (SQ). Scalar Quantization (SQ) in Azure AI Search compresses 32-bit floating-point vector values down to 8-bit integers (int8), significantly reducing memory usage and accelerating vector search performance with minimal loss in recall accuracy.

Answer

Exhaustive K-nearest neighbor (exact KNN) search

Answer

Scoring profiles with magnitude boosting

Answer

Semantic enrichment with Azure AI Language

Question 4

Which THREE data types or formats are commonly used to represent embeddings when preparing data for storage in a vector-enabled Azure service?

Accepted Answer

JSON arrays.. Embeddings are typically represented as arrays of floats, stored as JSON lists, or exported from models like Azure OpenAI as numerical vectors.

Answer

Base64 encoded binary image data.

Answer

Vectorized text sequences (token IDs).

Question 5

You are implementing vector search using Azure Cosmos DB for MongoDB (vCore). Which TWO distance metrics are valid options when creating a vector search index? Choose two.

Accepted Answer

euclidean (l2). Azure Cosmos DB for MongoDB (vCore) supports cosine distance (cos), inner product (ip), and Euclidean distance (l2).

Answer

chi-squared (cs)

Answer

pearson (pr)

Answer

manhattan (m1)

Question 6

When designing a data layer architecture for an AI application using Azure AI Search, what is the primary role of an integrated vectorizer?

Accepted Answer

To automatically convert raw source text fields into vector embeddings during indexing and queries without requiring external orchestration code.. An integrated vectorizer in Azure AI Search automates the process of transforming raw text chunks into vector embeddings directly within the search service pipeline, calling an endpoint like Azure OpenAI automatically during document ingestion and queries.

Answer

To execute full-text BM25 keyword matching alongside vector calculations.

Answer

To compress vector indexes using lossy compression algorithms to save disk storage.

Answer

To generate cryptographic hashes for document deduplication.

Question 7

You have a large collection in Azure Cosmos DB for NoSQL and want to perform a vector search. The application is currently hitting 'Request Rate Too Large' (429) errors during vector search queries. What is the most effective way to mitigate this?

Accepted Answer

Scale up the throughput (RU/s) allocated to the container.. Increasing the Request Units (RUs) allocated to the container is the primary way to handle throughput bottlenecks in Cosmos DB.

Answer

Reduce the number of vectors stored in each document.

Answer

Implement client-side caching of vector search results.

Answer

Switch the index type to 'DiskANN'.

Question 8

You are integrating Azure Event Hubs with Apache Spark on Azure Databricks for real-time AI model scoring. You need to configure the Spark connector to read from all partitions of the Event Hub efficiently without missing records or creating duplicate processing bottlenecks. Which consumer property is critical to configure correctly?

Accepted Answer

A dedicated Consumer Group and correct starting offset configuration (e.g., startingPosition).. When using the Azure Event Hubs Spark connector, you must configure the Consumer Group and ensure offset management (starting position like `earliest` or `latest`) is correctly set.

Answer

Setting the Event Grid webhook handshake timeout to 120 seconds.

Answer

Enabling AMQP 1.0 web socket transport mode across all Spark worker nodes.

Answer

The Service Bus shared access policy connection string with Manage rights.

Question 9

Your organization requires that all data transmitted to an Azure Service Bus namespace is encrypted in transit using Transport Layer Security (TLS). What is the minimum required TLS version enforced by modern Azure Service Bus secure baselines?

Accepted Answer

TLS 1.2. Azure enforces TLS 1.2 as the minimum secure baseline for all modern Azure PaaS services, including Service Bus, Event Hubs, and Event Grid.

Answer

SSL 3.0

Answer

Plaintext HTTP with custom Base64 encoding

Answer

TLS 1.0

Question 10

You are designing a data ingestion pipeline that generates embeddings using Azure OpenAI and stores them in Azure AI Search. Which THREE components are required in the Azure AI Search index definition to support integrated vectorization? Choose three.

Accepted Answer

Vectorizer definition referencing the Azure OpenAI connection. Integrated vectorization in Azure AI Search requires a vectorizer definition, an algorithm configuration, and vector fields configured in the index schema.

Answer

A dedicated Azure Blob Storage container data source

Answer

A custom Lucene similarity scoring profile

Question 11

When configuring vector search algorithms in Azure AI Search, you can tune several parameters associated with the Hierarchical Navigable Small World (HNSW) graph. Which THREE parameters are configurable within an HNSW algorithm profile?

Accepted Answer

efConstruction. The HNSW algorithm profile configuration in Azure AI Search accepts parameters such as m (max bi-directional links per node), efConstruction (size of dynamic candidate list during construction), and efSearch (size of dynamic candidate list during search).

Answer

bm25K1

Answer

chunkOverlapSize

Question 12

You are troubleshooting high latency in an Azure AI Search vector indexing pipeline. The source data is stored in Azure Blob Storage. You notice that the 'skillset' execution is the bottleneck. Which configuration change is most likely to reduce ingestion time?

Accepted Answer

Increase the 'maxParallelism' setting in the indexer configuration.. Increasing the number of indexer concurrency (if the tier supports it) or optimizing the skillset to perform batch processing reduces the total overhead of document enrichment.

Answer

Change the Blob storage tier to Cool.

Answer

Switch from HNSW to Flat indexing.

Answer

Reduce the embedding vector dimensions.

Question 13

Which THREE features are common to both Azure AI Search and Azure Cosmos DB for NoSQL when performing vector search?

Accepted Answer

Ability to filter results based on non-vector document properties.. Both services support HNSW-based indexing, allow for filtering metadata alongside vector searches, and provide similarity search functions.

Answer

Support for semantic re-ranking (Semantic Ranker).

Answer

Automatic translation of SQL to Vector queries.

Question 14

You are configuring Azure Event Grid to send events to an Azure Logic App. Which built-in trigger should you use in your Logic App workflow to receive these events seamlessly?

Accepted Answer

When a resource event occurs (Azure Event Grid trigger). Azure Logic Apps provides a native 'When a resource event occurs' trigger (powered by Event Grid) that automatically provisions the webhook subscription behind the scenes.

Answer

When messages arrive in a Service Bus queue

Answer

When a blob is added or modified (V2)

Answer

When an HTTP request is received

Question 15

Your organization requires that all messages sent to an Azure Service Bus Premium namespace are encrypted using customer-managed keys (CMK) stored in Azure Key Vault. Where must you configure this encryption setting?

Accepted Answer

At the Service Bus Namespace level. Customer-managed keys for Azure Service Bus are configured at the Namespace level using Azure Key Vault integration.

Answer

Inside the Event Grid subscription endpoint URI

Answer

At the individual Queue or Topic level

Answer

Within the Azure Function consumer application configuration