AI-200 Practice Test 3 — 10 Questions

Question 1

You are designing an operational dashboard in Azure Monitor. Which TWO visualization components can you include in an Azure Workbook? (Choose two)

Accepted Answer

KQL query result grids and charts. Option B is correct because Azure Workbooks natively render KQL query results as grids, charts, and other visualizations, which is the core mechanism for displaying Log Analytics data in a workbook. Option E is correct because Workbooks can also visualize Azure Monitor platform metrics directly through metric chart components, letting you combine metrics and logs in one dashboard. Options A, C, and D are incorrect because DDL scripts, Key Vault plain-text secrets, and Docker image binary layers are not visualization components and cannot be embedded as workbook tiles; Workbooks only display query/metric-based visualizations, text, links, and parameters.

Answer

Azure SQL database table creation scripts (DDL)

Answer

Azure Key Vault plain-text master password files

Answer

Docker container image binary layers

Question 2

You are designing a data layer architecture in Azure Cosmos DB for NoSQL for an AI application that performs high-frequency writes and real-time vector searches. You need to understand how vector indexing impacts write operations. What is the primary trade-off?

Accepted Answer

Writing documents consumes more Request Units (RUs) because vector indexes must be updated. Writing documents consumes more Request Units (RUs) because vector indexes must be updated on every write. In Azure Cosmos DB for NoSQL, vector indexing adds overhead to write operations since the index must be maintained as documents are inserted or updated. This is the primary trade-off: faster vector search at the cost of higher write RU consumption.

Answer

Vector properties cannot be updated after initial document creation

Answer

Writes are completely blocked until the vector index reaches 100% synchronization

Answer

Writes require explicit distributed transactions across regions

Question 3

You are configuring an Azure AI Search vector index for a retrieval-augmented generation (RAG) application. You need to select an algorithm for vector similarity search that balances recall speed with accuracy while allowing approximate nearest neighbor (ANN) search. Which vector search algorithm configuration should you select?

Accepted Answer

Hierarchical Navigable Small World (hnsw). Hierarchical Navigable Small World (hnsw) is the correct algorithm because it provides approximate nearest neighbor search with a tunable balance between recall and latency, using a multi-layer graph that enables fast traversal. Azure AI Search supports hnsw as a vector search algorithm configuration, and it is the recommended choice for RAG workloads that need low-latency, high-recall retrieval over large vector indexes. It directly satisfies the requirement for ANN with balanced speed and accuracy.

Answer

Exhaustive K-Nearest Neighbor (exhaustiveKnn)

Answer

Locality-Sensitive Hashing (lsh)

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Inverted File with Product Quantization (ivf-pq)

Question 4

You want to limit the CPU resources that a specific AI inference pod can consume. Which Kubernetes field do you configure?

Accepted Answer

resources.limits.. The `resources.limits` field in a Kubernetes pod spec sets the maximum CPU and memory a container can consume. When a container exceeds its CPU limit, it is throttled; when it exceeds its memory limit, it is OOM-killed. This is the correct field to cap CPU for an inference pod.

Answer

resources.requests.

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securityContext.

Answer

livenessProbe.

Question 5

You are deploying an AI workload to AKS and want to ensure that pods belonging to your application are distributed across different fault domains (availability zones) for high availability. Which Kubernetes feature should you configure?

Accepted Answer

Topology spread constraints. Topology spread constraints let you control how pods are distributed across topology domains such as zones or nodes, using label selectors and maxSkew to enforce even spreading. By setting topologyKey to topology.kubernetes.io/zone and a maxSkew of 1, you ensure pods are spread across availability zones, providing fault tolerance if a zone fails.

Answer

Resource quotas

Answer

Node taints and tolerations

Answer

Horizontal Pod Autoscaler

Question 6

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?

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 7

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 8

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 9

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 10

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