AI-200 Practice Test 30 — 15 Questions

Question 1

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 natively supports vector search with HNSW and DiskANN algorithms integrated into its managed, globally distributed NoSQL database. It combines the vector index with the document store, so embeddings and metadata live in the same container and are queried with SQL-like syntax.

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Azure Logic Apps

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Azure Functions

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Azure Virtual Machines

Question 2

You are configuring vector search options in Azure Cosmos DB for NoSQL. Which TWO distance metrics are supported when defining a vector embedding policy? (Choose two)

Accepted Answer

euclidean. In Azure Cosmos DB for NoSQL, the vector embedding policy's "distanceFunction" property accepts exactly three values: cosine, dotproduct, and euclidean. Option D (euclidean) is correct because Euclidean distance is one of the natively supported metrics, measuring straight-line distance between vectors and typically used with normalized or unnormalized embeddings. Option E (cosine) is correct because cosine similarity is the default and most commonly used metric, measuring the angle between vectors and ideal for text embeddings where magnitude is irrelevant. Option A (hamming) is not supported; it applies to binary/categorical string comparison, not the float32 vector indexes Cosmos DB builds. Option B (manhattan) is not supported; only cosine, dotproduct, and euclidean are valid distanceFunction values. Option C (jaccard) is not supported; it is a set-similarity measure used in data mining, not a Cosmos DB vector distance metric.

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hamming

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manhattan

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jaccard

Question 3

You are troubleshooting an AI inference container in Azure Container Apps that fails to start because it cannot pull the base image from Azure Container Registry. Authentication fails with an unauthorized error. What is the recommended way to grant the Container App access to ACR?

Accepted Answer

Configure a system-assigned managed identity on the Container App and assign the AcrPull role on ACR. The recommended approach is to enable a system-assigned (or user-assigned) managed identity on the Container App and grant it the AcrPull role on the Azure Container Registry. This lets the Container App authenticate to ACR without storing any credentials, using Entra ID token exchange under the hood. It is the Azure-native, secretless pattern for image pulls.

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Hardcode the admin username and password into environment variables

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Expose the ACR endpoint publicly without authentication

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Attach an Azure Storage Account SAS token to the container environment

Question 4

You are deploying a distributed PyTorch training job across multiple nodes in an Azure Kubernetes Service (AKS) cluster. Which mechanism should you use to facilitate high-speed, low-latency inter-node communication required for gradient synchronization?

Accepted Answer

Node placement with GPU-optimized VM series (such as NDm A100 v4) and InfiniBand support. Distributed PyTorch training requires high-bandwidth, low-latency inter-node communication for AllReduce gradient synchronization, and InfiniBand (via RDMA) on GPU-optimized VM series like NDm A100 v4 provides exactly that. These SKUs include dedicated InfiniBand fabric that bypasses the TCP/IP stack, delivering the microsecond latency and hundreds of Gbps throughput needed to keep GPUs from starving during gradient exchange.

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Kubenet basic networking with dynamic NAT

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Azure Application Gateway Ingress Controller (AGIC)

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Standard Azure CNI with Network Policies enabled

Question 5

Your application publishes events to an Azure Event Grid custom topic. You need to restrict publishing access so that only clients with a specific User-Assigned Managed Identity can send events. How should you secure the topic?

Accepted Answer

Assign the 'Event Grid Data Sender' role to the Managed Identity on the Event Grid topic resource scope.. Azure Event Grid integrates with Azure RBAC, and the built-in role 'Event Grid Data Sender' grants permission to publish events to a topic or namespace. Assigning this role to the user-assigned managed identity at the Event Grid topic resource scope allows only that identity to publish, satisfying the least-privilege requirement. This is the standard Entra ID-based authorization model for Event Grid publishing.

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Embed the primary SAS access key into the client's application environment variables.

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Enable AMQP 1.0 token authentication on the event subscription.

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Configure an Azure Storage firewall rule on the topic endpoint.

Question 6

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. In Azure AI Search, a pure vector query is executed by providing the 'vectorQueries' array and omitting or leaving the 'search' parameter empty (or setting it to null). When 'search' is omitted, the service performs only vector similarity search without keyword matching. This is documented behavior for vector-only queries.

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The 'vectorQueries' parameter

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The 'top' parameter

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The 'api-version' parameter

Question 7

You are configuring an Azure AI Search vector index. Which TWO parameters are part of the HNSW algorithm configuration settings? Choose two.

Accepted Answer

efSearch. Option A, efSearch, is correct because it is the HNSW parameter that controls the size of the dynamic candidate list used during query time, trading off recall against latency in Azure AI Search vector search. Option B, m, is correct because it is the HNSW parameter specifying the number of bi-directional links created for each node in the graph, which affects index build time, memory, and search accuracy. The other options do not belong: maxTokenLength and shingleSize are text-analyzer settings used in custom analyzers for lexical indexing, not vector HNSW configuration, and clusterCount is not an HNSW parameter in Azure AI Search (it relates to partitioning or clustering concepts, not the HNSW graph settings).

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maxTokenLength

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clusterCount

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shingleSize

Question 8

You need to analyze log queries across multiple AI applications and infrastructure components to identify custom performance bottlenecks. Which query language is used in Azure Monitor Log Analytics to write queries against collected log data?

Accepted Answer

Kusto Query Language (KQL). Kusto Query Language (KQL) is the native query language used by Azure Monitor Log Analytics to query data stored in Log Analytics workspaces. It is designed for read-only, high-performance analysis of large volumes of structured, semi-structured, and unstructured log data, making it ideal for identifying custom performance bottlenecks across AI applications and infrastructure. KQL supports advanced operators like `summarize`, `join`, `render`, and `make-series` that enable deep diagnostics and visualization.

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GraphQL

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SQL Server Transact-SQL (T-SQL)

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Azure Resource Graph Query (ARGQL)

Question 9

You are implementing a multi-modal AI application that searches across both text descriptions and product images stored in Azure AI Search. Which feature allows you to query both modalities within a unified search index?

Accepted Answer

Multiple vector fields with distinct dimensions and vectorizers in a single index. Azure AI Search supports defining multiple vector fields in a single index, each with its own dimensions and vectorizer, so text embeddings (e.g., 1536-dim from text-embedding-ada-002) and image embeddings (e.g., 512-dim from a CLIP-style model) can coexist and be queried together. This enables unified multi-modal search where a query can target both text and image vector fields, with results merged via hybrid or RRF ranking.

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Single-dimension global vector index restriction

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Mandatory unification of text and image vectors into a single 1536-dimensional field

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External fusion proxy service

Question 10

Which command-line interface tool is used to deploy and manage container images directly to Azure Container Registry?

Accepted Answer

az acr.. The Azure CLI command group 'az acr' is specifically designed to manage Azure Container Registry, including building, pushing, and pulling container images directly to/from ACR. It provides native integration with ACR and supports tasks like creating registries, managing credentials, and running ACR Tasks. This makes it the correct tool for deploying and managing container images directly to ACR.

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helm.

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docker build.

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kubectl.

Question 11

You are configuring semantic ranking in Azure AI Search. You need to specify which fields in your index contain the title and content that the semantic ranker should analyze. Where do you configure these field mappings?

Accepted Answer

In the semantic configuration section of the index schema. Semantic ranking in Azure AI Search is configured in the semantic configuration section of the index schema. Within this configuration, you specify which fields contain the title, content, and keywords that the semantic ranker should analyze. This is done by defining a semantic configuration object in the index definition.

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In the indexer skill set cognitive map

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In the Azure OpenAI model deployment properties

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In the vector profile definition

Question 12

You are building an AI document processing pipeline that uses Azure Event Grid to notify downstream services when a new document is uploaded to an Azure Blob Storage container. You need to ensure that the event subscription delivers events to an Azure Function only when the document has a .pdf extension. What should you do?

Accepted Answer

Use event filtering on the event subscription to filter on the subject ending with .pdf.. Azure Event Grid event subscriptions support filtering based on the event subject. For Blob Storage events, the subject is the full path to the blob. By configuring a subject suffix filter with '.pdf', you ensure that only events for .pdf files are delivered to the Azure Function. This reduces unnecessary invocations and aligns with the requirement.

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Use a dead-letter destination to store non-.pdf events and process only .pdf events.

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Create a custom topic and publish events only for .pdf files.

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Configure the Azure Function to check the file extension and ignore non-.pdf files.

Question 13

You are building a serverless AI workflow that triggers an Azure Function whenever a new file is uploaded to an Azure Blob Storage container. The Function must start processing within seconds of the upload and should not require polling. Which Azure service should you use to connect Blob Storage to the Function?

Accepted Answer

Azure Event Grid. Azure Event Grid natively integrates with Blob Storage to publish events like BlobCreated, and it can push these events directly to an Azure Function. This provides a serverless, push-based trigger that processes files within seconds without polling, making it the correct choice for the scenario.

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Azure Queue Storage

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Azure Service Bus

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Azure Event Hubs

Question 14

You manage an Azure Cosmos DB for NoSQL container that stores product documents with a vector embedding property. You need to query for products similar to a given embedding while also filtering by a category partition key. Which query approach should you use?

Accepted Answer

Use a SQL query with VectorDistance and a WHERE clause on the partition key.. Cosmos DB for NoSQL provides the VectorDistance function for vector similarity search directly in SQL queries. When you also filter on the partition key, the query is scoped to a single logical partition, which optimizes performance and RU consumption. This is the most efficient and supported method for combining vector search with attribute filters.

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Use a stored procedure that loads all documents and computes cosine similarity in JavaScript.

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Use a user-defined function that calls an external Azure AI Search service.

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Use the MongoDB API's $vectorSearch aggregation stage.

Question 15

You are designing an AI inference pipeline that publishes prediction results to an Azure Event Hubs namespace with four partitions. A downstream Azure Function must process events in the exact order they were produced for each customer ID, and the pipeline must scale to handle increasing load. The customer ID is included in the event body as a property. What should you do to guarantee per-customer ordering while maximizing throughput?

Accepted Answer

Set the partition key to the customer ID when publishing events, and have each Function instance process a single partition.. Ordering in Event Hubs is guaranteed only within a partition. Setting the partition key to the customer ID ensures all events for that customer go to the same partition, and having each Function instance read from a single partition preserves sequential processing. This design also allows the four partitions to be processed concurrently, meeting the scaling requirement.

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Publish events without a partition key and enable the Event Hubs capture feature to archive events in order.

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Enable the Event Hubs deduplication feature and set a sequence number on each event before publishing.

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Use a single partition for the Event Hubs namespace and scale the Function with multiple instances reading from that partition.