AI-200 Practice Test 10 — 10 Questions

Question 1

You are developing an enterprise retrieval-augmented generation (RAG) application using Azure AI Search. You need to configure a vector index to store dense vector embeddings alongside traditional text fields. Which property within the Azure AI Search index definition must be explicitly configured with a vector search profile to enable HNSW (Hierarchical Navigable Small World) algorithm indexing?

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

Accepted Answer

vectorSearchProfile. In an Azure AI Search index, the vectorSearchProfile property on a vector field binds that field to a named profile defined in the index's vectorSearch configuration. The profile in turn references an algorithm configuration (for example, an HNSW parameters object) and optionally a compression method, so setting vectorSearchProfile is what actually activates HNSW indexing for the field.

Answer

vectorIndexKind

Answer

compressionMethod

Answer

similarityFunction

Question 2

You are optimizing a large language model container inference deployment on AKS using GPU-enabled nodes (NC-series). The inference server experiences frequent out-of-memory errors on the GPU device itself during high context lengths. Which Kubernetes configuration metric should you monitor and alert on?

Accepted Answer

container_gpu_memory_used_bytes via NVIDIA DCGM Exporter. GPU out-of-memory errors on the device itself must be detected by monitoring GPU memory utilization, which is exposed by the NVIDIA DCGM Exporter as container_gpu_memory_used_bytes (and related DCGM metrics like DCGM_FI_DEV_FB_USED). This metric reflects actual framebuffer memory consumption on the GPU, which is what causes CUDA OOM errors during long-context inference.

Answer

container_memory_working_set_bytes via kubelet

Answer

container_cpu_cfs_throttled_periods_total

Answer

node_net_bytes_total

Question 3

You are deploying an AI workload to Azure Container Apps. You need to ensure the container scales down to zero when there is no traffic. What should you configure?

Accepted Answer

Set minimum replicas to 0.. Azure Container Apps scales to zero when the minimum replica count is set to 0. This allows the platform to deprovision all replicas during periods of no traffic, eliminating compute cost, and scale back up when a request arrives (subject to the configured scale rules and HTTP ingress).

Answer

Set maximum replicas to 0.

Answer

Enable the 'Always On' flag.

Answer

Configure an external HTTP trigger.

Question 4

You are evaluating data layer architectures for an enterprise AI application that requires both vector search and structured transactional data management. Which THREE criteria favor choosing Azure Cosmos DB over Azure AI Search as the primary database? (Choose three)

Accepted Answer

Need for sub-10 millisecond point reads and writes on operational business records. Option A is correct because Azure Cosmos DB is designed as a low-latency operational database, offering single-digit millisecond point reads and writes (by key and partition key) for business records, whereas Azure AI Search is a search index optimized for query throughput rather than transactional point operations. Option B is correct because Cosmos DB natively stores schema-flexible JSON documents and, with vector indexing/vector search support, can hold operational documents and their embeddings together in the same container. Option D is correct because Cosmos DB supports multi-document ACID transactions within a single logical partition (transactional batch), which Azure AI Search cannot provide since it is not a transactional database. Option C is not correct because advanced BM25 keyword ranking and neural semantic re-ranking are core capabilities of Azure AI Search, not differentiators of Cosmos DB. Option E is not correct because automatic PDF text extraction, OCR, and document chunking via indexers are Azure AI Search (AI enrichment/indexer) features, not Cosmos DB features.

Answer

Requirement for advanced BM25 keyword search and neural semantic re-ranking

Answer

Requirement to generate automatic PDF text extraction and document chunking via indexers

Question 5

You are evaluating Cosmos DB for NoSQL vs. Azure AI Search for a vector search application. Your application requires strict ACID compliance for metadata updates along with vector search. Which should you choose and why?

Accepted Answer

Cosmos DB because it supports document-level ACID transactions.. Cosmos DB for NoSQL supports document-level ACID transactions via the transactional batch API, ensuring metadata updates are atomic and consistent. When the requirement combines strict ACID compliance for metadata with vector search, Cosmos DB is the correct choice because Azure AI Search does not provide ACID transactional guarantees for document updates. This aligns the service's transactional model with the application's consistency requirement.

Answer

Cosmos DB because it is the only service that supports vectors.

Answer

Azure AI Search because it supports better hybrid search.

Answer

Azure AI Search because it uses HNSW for faster searches.

Question 6

You are designing a disaster recovery and backup strategy for Azure Cosmos DB for NoSQL storing vector embeddings. Which THREE backup options or capabilities are available? (Choose three)

Accepted Answer

Restoring backed-up data to a new Azure Cosmos DB account. Option A is correct because Azure Cosmos DB restore operations always create a new account (or restore into a new account) rather than overwriting the existing account, which is the standard mechanism for both periodic and continuous backup restores. Option C is correct because the periodic backup mode supports configurable backup intervals and retention periods (with defaults of every 4 hours and 2 copies retained, extendable via support), letting you tailor the schedule to your RPO needs. Option D is correct because continuous backup mode enables point-in-time restore (PITR) for the last 30 days (or 7 days in some configurations), which is ideal for protecting vector embedding data against accidental deletes or corruption. Option B is not applicable because Cosmos DB for NoSQL is not SQL Server and does not use .bak files or native SQL Server restore tooling. Option E is not a supported capability because Cosmos DB does not provide FTP-based manual file export into local VM storage as a backup mechanism.

Answer

Direct SQL Server .bak file restoration

Answer

Manual file export via FTP into local VM storage

Question 7

You are configuring authentication and authorization across Azure messaging and eventing services. Which THREE statements regarding security features are correct? (Choose three)

Accepted Answer

Shared Access Signature (SAS) tokens can be generated with specific permissions and expiration times.. Option B is correct because SAS tokens for Service Bus, Event Hubs, and Storage are constructed with a signed resource URI, key name, expiry, and explicit permissions (for example Send, Listen, Manage), allowing least-privilege, time-bound access. Option C is correct because Service Bus and Event Hubs both expose data-plane RBAC roles such as Azure Service Bus Data Owner/Sender/Receiver and Event Hubs Data Owner/Sender/Receiver, letting Microsoft Entra ID identities authenticate without embedding SAS keys. Option D is correct because Event Grid webhook and Azure Function/Service Bus/Event Hubs subscription targets can use a system-assigned or user-assigned managed identity, with the target validating the Entra-issued token, avoiding secrets in the subscription. Option A is wrong because Event Grid custom topics authenticate publishers with SAS keys or Microsoft Entra ID, not SQL database credentials. Option E is wrong because Service Bus queues are accessed via SAS or Entra ID over AMQP; anonymous public access is not required and should not be enabled.

Answer

Event Grid custom topics require SQL database credentials for publisher authentication.

Answer

Azure Service Bus queues require public anonymous access to be enabled for client SDK connections.

Question 8

When configuring vector search in Azure Cosmos DB for NoSQL, which TWO steps are required to prepare a container for vector search before inserting documents? (Choose two)

Accepted Answer

Define the vectorEmbeddingPolicy at container creation or update. Option A is correct because Azure Cosmos DB for NoSQL requires a vectorEmbeddingPolicy on the container to declare the vector properties, including the path to the vector field, the data type (e.g., float32), dimensions, and distance function (cosine, dotproduct, or euclidean) before vectors can be stored and queried. Option B is correct because you must also define vectorIndexes inside the container's indexingPolicy, specifying the vector path and index type (flat, quantizedFlat, or diskANN), so the vector data is actually indexed and searchable. Options C, D, and E are incorrect: NFS shares, the pgvector extension, and relational foreign keys belong to other systems (file storage, PostgreSQL, and relational databases respectively) and are not part of Cosmos DB for NoSQL vector search configuration.

Answer

Mount an NFS network share for vector file storage

Answer

Install the pgvector extension on the container

Answer

Create a relational foreign key constraint to Azure SQL

Question 9

Which Azure feature allows you to scan images in your registry for vulnerabilities before deploying them to your production cluster?

Accepted Answer

Microsoft Defender for Containers.. Microsoft Defender for Containers is the Azure service that provides vulnerability scanning for container images stored in Azure Container Registry (ACR) and other registries. It automatically scans images on push and continuously monitors for new vulnerabilities, giving you a security assessment before deployment. This aligns with the question's requirement to scan images in your registry for vulnerabilities.

Answer

Azure Monitor.

Answer

Azure Key Vault.

Answer

Azure Policy.

Question 10

You are developing an AI application in Azure and need to monitor custom telemetry, trace dependencies, and track dependency failures for calls made to an external machine learning model endpoint. Which Azure service should you use?

Accepted Answer

Application Insights. Application Insights is Azure's application performance monitoring (APM) service, providing custom telemetry via the SDK, distributed tracing across dependencies, and failure tracking for outbound calls. It is the correct choice for monitoring an AI app's calls to an external ML endpoint, including latency, failure rates, and dependency maps.

Answer

Azure Advisor

Answer

Microsoft Defender for Cloud

Answer

Azure Policy