AI-200 Practice Test 2 — 25 Questions

Question 1

You are debugging a Python application integrated with Application Insights. You notice that custom telemetry events generated in a background worker thread are not appearing in Application Insights. What is the most likely cause of this issue?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

The application terminated or the worker thread exited before the telemetry client buffer was flushed. The Application Insights Python SDK buffers telemetry in memory and sends it asynchronously in batches. If the background worker thread exits or the application terminates before the buffer is flushed, the queued telemetry events are lost and never reach Application Insights. This is the most common cause of missing custom events from short-lived threads.

Answer

Log Analytics ingestion rate limiting always drops multi-threaded telemetry

Answer

Application Insights automatically blocks all telemetry generated outside of HTTP request threads

Answer

The instrumentation key was incorrectly formatted as a GUID

Question 2

You are managing an Azure Cosmos DB for NoSQL database containing vector embeddings. You need to perform a migration of embeddings from one container to another without taking the application offline. Which Azure feature should you use?

Accepted Answer

Azure Cosmos DB Change Feed processor. The Azure Cosmos DB Change Feed processor provides an ordered, continuous stream of inserts and updates from a source container, which a consumer can read and write into a target container in near real time. This enables online migration of vector embeddings without taking the application offline, since the source container keeps serving reads and writes while the change feed replicates data. It is the canonical pattern for live container-to-container migration in Cosmos DB for NoSQL.

Answer

Azure Data Factory tumbling window triggers over Blob Storage

Answer

SQL Server Integration Services (SSIS) file import

Answer

Taking a manual offline portal backup and restoring to a new account

Question 3

You are optimizing an enterprise Azure Cosmos DB for NoSQL vector database. Which THREE administrative or architectural practices help ensure high availability and predictable performance? (Choose three)

Accepted Answer

Choose a partition key that evenly distributes data and query load across physical partitions. Option A is correct because a well-chosen partition key that evenly distributes data and query load across physical partitions prevents hot partitions, which is essential for predictable performance and scalability in Cosmos DB for NoSQL. Option B is correct because enabling multi-region write replication (multi-master) provides global low-latency writes and improves availability by allowing the account to continue serving requests even if a region becomes unavailable. Option E is correct because provisioning adequate Request Units (RUs) or configuring autoscale throughput ensures the database has enough throughput capacity to handle workload demands, preventing throttling (429 errors) and maintaining predictable performance. Option C is incorrect because storing all documents in a single logical partition without a partition key creates a hot partition and limits scalability, harming both availability and performance. Option D is incorrect because disabling automatic indexing entirely removes the default indexing policy that supports efficient queries, which would degrade query performance rather than improve it.

Answer

Store all documents in a single partition without a partition key

Answer

Disable automatic indexing entirely across all properties

Question 4

When configuring an Azure AI Search vector profile, which TWO elements must be successfully referenced together to make the profile functional? (Choose two)

Accepted Answer

An algorithm configuration name. Option B is correct because a vector profile in Azure AI Search must reference a valid algorithm configuration name (the HNSW or exhaustive KNN configuration defined in the index's vectorSearch.algorithms collection) that determines how vector similarity is computed during queries. Option C is correct because when integrated vectorization is used, the vector profile must also reference a vectorizer name (defined in the vectorSearch.vectorizers collection) so the search service can convert text queries into embeddings at query time. These two references — algorithm configuration and vectorizer — are the required components that make a vector profile functional. Option A is incorrect because a Cognitive Services storage blob container is not part of a vector profile definition; blob containers are used as data sources for indexers, not for vector profiles. Option D is incorrect because an Azure SQL connection string belongs to a data source definition for indexers, not to a vector profile. Option E is incorrect because Cosmos DB RU/s throughput is a database provisioning setting and has no role in defining a vector profile.

Answer

A Cognitive Services storage blob container

Answer

An Azure SQL connection string

Answer

A Cosmos DB throughput RU/s setting

Question 5

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 requires the Standard tier (S1) or higher — it is not available on Free or Basic tiers. Semantic ranking uses Microsoft's language models to rerank the top results from BM25 or vector search, improving relevance for natural-language queries. Enabling it on a supported tier is done by adding a semantic configuration to the index and setting queryType to 'semantic'.

Answer

Storage Optimized (L1)

Answer

Free

Answer

Basic

Question 6

You are troubleshooting a latency issue with an AI model deployed in a Virtual Machine. You suspect the issue is related to storage IOPS limits. Which tool should you use to check the storage performance metrics?

Accepted Answer

Azure Monitor Metrics for the Virtual Machine.. Azure Monitor 'Metrics' for the Virtual Machine resource provides 'Disk Read Operations/sec' and 'Disk Write Operations/sec', which are key for diagnosing IOPS bottlenecks.

Answer

Azure Network Watcher.

Answer

Azure Resource Graph Explorer.

Answer

Application Insights Dependency logs.

Question 7

You are implementing an event-driven architecture where an Azure Function acts as an Event Grid subscriber. Occasionally, the downstream cognitive service is throttled, causing failures. You need to ensure that unprocessable events are not lost and can be inspected later. Where should you configure a dead-letter destination?

Accepted Answer

On the Event Grid event subscription, specify an Azure Blob Storage container as the dead-letter endpoint.. Event Grid event subscriptions support a native dead-letter destination where events that cannot be delivered (after retries are exhausted) are stored as blobs in an Azure Storage container. This is configured on the event subscription itself, not on the subscriber, because Event Grid owns the delivery and retry lifecycle. The dead-lettered blob retains the original event payload plus failure metadata, enabling later inspection and reprocessing.

Answer

On the Azure Function trigger binding definition using the deadLetterQueueName property.

Answer

On the Azure Storage account firewall settings as an IP filter exception.

Answer

Inside the Azure Function application configuration settings as an Application Insights error sink.

Question 8

You need to monitor the monthly cost of an Azure AI Document Intelligence resource to ensure it does not exceed a specified budget. Where should you configure an automated notification when the cost reaches 80% of the budget?

Accepted Answer

Create a Budget in Azure Cost Management + Billing with an alert threshold set to 80%.. Azure Cost Management + Billing Budgets are the native mechanism for setting spending thresholds and triggering automated notifications (email or action groups) when actual or forecasted cost reaches a defined percentage of the budget. Setting an 80% threshold on a budget scoped to the Document Intelligence resource is the correct approach.

Answer

Configure an alert rule in Azure Monitor on the Document Intelligence resource metric 'Transactions'.

Answer

Set up an action group in Azure Advisor to monitor billing anomalies.

Answer

Create an expenditure alert inside the Azure AI Foundry portal.

Question 9

Which THREE logs or telemetry types should you collect in Azure Monitor to comprehensively troubleshoot an AI-powered web application?

Accepted Answer

Application requests and response status codes.. Application Insights collects request telemetry, dependency tracking for API calls, and trace logs for debugging logic.

Answer

Azure Bastion audit logs.

Answer

Azure Firewall packet capture PCAP files.

Question 10

Your enterprise AI application is deployed across multiple Azure regions for high availability. You need to ensure that logs from Application Insights instances in all regions are aggregated into a single central Log Analytics workspace for unified querying. How should you configure this?

Accepted Answer

Configure each Application Insights resource to use the same shared centralized Log Analytics workspace during creation. Application Insights resources can be configured to send their telemetry to a Log Analytics workspace, and pointing multiple regional Application Insights instances at the same workspace aggregates all logs for unified KQL querying. This is done at creation time (or via migration) by selecting the shared workspace as the ingestion target. The result is a single pane of glass across regions without custom ETL.

Answer

Deploy a separate Log Analytics workspace in every region and use Azure Backup to copy tables

Answer

Write a custom Python script that downloads logs from each region every hour and uploads them to Azure Blob Storage

Answer

Use Azure Traffic Manager to merge log data streams at the DNS level

Question 11

Your application publishes messages to an Azure Service Bus queue. The messages contain sensitive personal data. Enterprise compliance mandates that message payloads are encrypted end-to-end at the application layer before being sent to Service Bus. How should you implement this?

Accepted Answer

Encrypt the message payload in your application code using a key from Azure Key Vault before sending, and decrypt upon receipt.. End-to-end application-layer encryption means the payload must be encrypted before it leaves the producer and decrypted only by the consumer, so the broker never sees plaintext. Using a key from Azure Key Vault to encrypt in code and decrypt on receipt satisfies this because Service Bus only transports ciphertext, and key custody stays in Key Vault.

Answer

Configure Event Grid advanced filters to redact sensitive keys.

Answer

Enable Service Bus Premium Geo-Disaster Recovery with encryption enabled.

Answer

Use Shared Access Signature tokens with 5-minute expirations.

Question 12

Your team is developing an AI solution that uses Azure Cognitive Services. You need to ensure that the API keys cannot be extracted from the client-side mobile application. What is the recommended architectural pattern to achieve this?

Accepted Answer

Implement a backend API proxy (e.g., Azure Functions or API Management) that authenticates mobile clients and calls Cognitive Services using secure server-side keys. A backend API proxy (Azure Functions or API Management) is the recommended pattern because the Cognitive Services subscription key stays server-side and is never shipped in the mobile binary, where it could be decompiled or intercepted. The mobile client authenticates to the proxy (e.g., via OAuth 2.0/Entra ID token), and the proxy validates that identity before calling Cognitive Services with the secret key. This also enables rate limiting, key rotation, and per-user quota enforcement without redeploying the app.

Answer

Use Azure Front Door to strip API keys from incoming mobile requests

Answer

Store the Cognitive Services API key in an encrypted local SQLite database on the mobile device

Answer

Configure Cognitive Services for anonymous public access and rely on mobile app version checks

Question 13

When storing vector embeddings in Azure Cosmos DB for MongoDB (vCore), which distance metric is natively supported when creating a vector search index?

Accepted Answer

Cosine distance (cos). Azure Cosmos DB for MongoDB (vCore) natively supports cosine distance when creating a vector index, specified as 'cos' in the index definition. Cosine distance measures the angle between vectors and is the standard metric for text embeddings, which are typically normalized. This makes it the correct native metric for vector search indexes in that service.

Answer

Levenshtein distance

Answer

Jaccard distance

Answer

Hamming distance

Question 14

Your containerized AI application requires a specific version of a kernel driver that is not installed on the AKS nodes. What is the most efficient way to manage this?

Accepted Answer

Use a privileged InitContainer to load the driver.. A privileged InitContainer runs before the main application container and, with privileged security context, can load kernel modules (e.g., via modprobe or insmod) onto the node. This is the standard Kubernetes pattern for node-level setup that must complete before the workload starts. It is more efficient than rebuilding the cluster and avoids granting the application container itself elevated privileges.

Answer

Mount the /proc filesystem as read-write.

Answer

Run the application as root.

Answer

Rebuild the AKS cluster.

Question 15

You are setting up auto-scaling for a containerized AI web app in Azure Container Apps. You want to scale based on the number of concurrent HTTP requests. Which scaler do you use?

Accepted Answer

HTTP scaler.. The HTTP scaler in Azure Container Apps scales replicas based on the number of concurrent HTTP requests, which directly matches the requirement to scale on concurrent HTTP request count. It is a KEDA-based scaler configured in the container app's scale rules with a `concurrentRequests` threshold.

Answer

Azure Service Bus scaler.

Answer

CPU usage scaler.

Answer

Memory usage scaler.

Question 16

You are designing an AI architecture that stores vector embeddings in Azure Cosmos DB for NoSQL. You need to implement role-based access control (RBAC) so that specific application microservices can only execute read-only queries against the database without having permission to write or delete documents. How should you configure this?

Accepted Answer

Create a custom SQL role definition with read-only data actions and assign it to the microservice's managed identity. Azure Cosmos DB for NoSQL has a native RBAC system built on SQL role definitions and role assignments. A custom role definition can be scoped to specific data-plane actions such as 'Microsoft.DocumentDB/databaseAccounts/readMetadata' and 'Microsoft.DocumentDB/databaseAccounts/sqlDatabases/containers/items/read', granting read-only access. Assigning that role to the microservice's managed identity enforces least privilege without exposing keys.

Answer

Configure Azure Blob Storage RBAC permissions on the Cosmos DB container

Answer

Share the primary master master-key with all microservices

Answer

Disable authentication on the Cosmos DB account during query execution

Question 17

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. Modern Azure Service Bus secure baselines require a minimum of TLS 1.2 for all data in transit. TLS 1.2 is the enforced floor because older protocols (SSL 3.0, TLS 1.0, TLS 1.1) have known cryptographic weaknesses and are deprecated across Azure services.

Answer

SSL 3.0

Answer

Plaintext HTTP with custom Base64 encoding

Answer

TLS 1.0

Question 18

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

Accepted Answer

retrievable. In Azure AI Search, the 'retrievable' attribute controls whether a field's contents are returned in search results. If retrievable is false, the field can still be searched, filtered, or sorted but its value is omitted from the returned document. This is the exact property that governs result payload inclusion.

Answer

searchable

Answer

facetable

Answer

filterable

Question 19

Your team needs to process high-volume clickstream data from a web application using Apache Kafka protocols while leveraging Azure-managed infrastructure. Which Azure service should you deploy?

Accepted Answer

Azure Event Hubs. Azure Event Hubs is a fully managed, real-time data ingestion service that supports the Apache Kafka protocol via its Kafka endpoint. It is designed for high-throughput event streaming and can handle clickstream data at scale. By deploying Event Hubs, you can use Kafka producers and consumers without managing Kafka clusters.

Answer

Azure Event Grid

Answer

Azure Notification Hubs

Answer

Azure Service Bus

Question 20

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). Reciprocal Rank Fusion (RRF) is the algorithm Azure AI Search uses to merge BM25 and vector result sets in hybrid queries. It converts each result's rank into a reciprocal score (1/(k+rank)) and sums them, producing a normalized combined ranking without needing to align raw score scales. This is why hybrid search in Azure AI Search 'just works' without manual score normalization.

Answer

Min-Max Score Scaling

Answer

Cosine Distance Normalization

Answer

Cross-Encoder Re-ranking