AI-200 Practice Test 13 — 15 Questions

Question 1

You are deploying a high-performance AI model in AKS. You need to ensure the container has access to the underlying host's NVIDIA GPU. What must be configured in the Pod spec?

Accepted Answer

Define a Resource Request for 'nvidia.com/gpu'.. To give a container access to an NVIDIA GPU in Kubernetes/AKS, you must declare a resource request (and typically a limit) for `nvidia.com/gpu` in the container spec. The NVIDIA device plugin on the node advertises GPUs as extended resources, and the scheduler uses this request to bind the pod to a GPU-equipped node and allocate the device.

Answer

Mount the host path '/dev/nvidia0'.

Answer

Use an InitContainer to install the driver.

Answer

Set 'privileged: true' in the security context.

Question 2

You are querying an Azure Cosmos DB for NoSQL container using vector search. You execute a query using the VectorDistance() system function. Which query structure is required to properly execute this vector search?

Accepted Answer

SELECT TOP 10 c.id, VectorDistance(c.vectorProp, [0.1, 0.2]) AS score FROM c ORDER BY VectorDistance(c.vectorProp, [0.1, 0.2]). Cosmos DB for NoSQL vector search requires the VectorDistance() function to be used in both the SELECT clause (to return the score) and the ORDER BY clause (to rank results). The query must also use TOP N to limit results, as vector search in Cosmos DB requires a top-k pattern. This structure allows the query engine to use the vector index for efficient nearest-neighbor search.

Answer

SELECT TOP 10 c.id FROM c WHERE VectorDistance(c.vectorProp, [0.1, 0.2]) > 0.9

Answer

JOIN vectorIndex ON c.id = vectorIndex.id

Answer

GROUP BY VectorDistance(c.vectorProp, [0.1, 0.2])

Question 3

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 specific algorithm Azure AI Search uses to merge results from parallel vector (kNN) and keyword (BM25) queries in a hybrid search. It converts each document's rank in each result set into a reciprocal score (1/(k + rank)) and sums them, producing a single unified ranked list without needing to normalize the raw, incomparable scores from BM25 and cosine similarity.

Answer

BM25 Scoring

Answer

Semantic Ranker

Answer

Vector Normalization

Question 4

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.. 'Request Rate Too Large' (HTTP 429) in Cosmos DB means the container's provisioned or autoscale RU/s has been exhausted by the vector search workload. The most direct and effective mitigation is to increase the throughput allocated to the container so that the RU cost of vector queries is covered without throttling.

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 5

You are troubleshooting slow query performance in an enterprise Azure AI Search vector deployment. Which THREE architectural or configuration adjustments can improve query throughput and latency? Choose three.

Accepted Answer

Scale out the number of search service replicas. Option A is correct because adding replicas to an Azure AI Search service distributes query load across more nodes, directly increasing query throughput (QPS) and reducing per-query latency, since each replica holds a full copy of the index and can serve queries independently. Option C is correct because vector search is memory-intensive: the HNSW graph and vector indexes must reside in RAM, so deploying on Standard tier or higher (rather than Basic/Free, which have limited memory and no vector support at scale) ensures adequate RAM to avoid disk-bound traversal and throttling. Option D is correct because lowering the efSearch parameter reduces the size of the HNSW candidate list explored during query-time traversal, trading a small amount of recall for faster search and lower latency, which is a standard tuning lever for throughput-sensitive vector workloads. Option B is not correct because ASCII folding tokenizers apply to text/lexical fields, not vector fields, and cannot replace vector embeddings or improve vector similarity search. Option E is not correct because exhaustive KNN scans the entire vector space and is far slower than HNSW approximate search, making it unsuitable for high-QPS production workloads.

Answer

Replace all vector fields with standard ASCII folding tokenizers

Answer

Configure the index to use exhaustive KNN exclusively for high-QPS production workloads

Question 6

You need to export Azure Monitor metric and log data to an external SIEM solution for security auditing. Which Azure feature should you use to stream this telemetry in real-time to an external Event Hub?

Accepted Answer

Diagnostic settings configured to stream to an Azure Event Hub. Diagnostic settings in Azure Monitor are the mechanism that routes platform metrics and resource logs to supported sinks, including Azure Event Hubs, Log Analytics workspaces, and Storage accounts. Configuring a diagnostic setting with Event Hub as the destination streams telemetry in near real-time, which an external SIEM can consume via its Event Hub connector. This is the native, supported path for exporting Azure telemetry to third-party security platforms.

Answer

Azure Backup vault scheduled jobs

Answer

Azure Advisor recommendations export

Answer

Azure Resource Graph queries

Question 7

Which TWO of the following are valid ways to authenticate an AKS cluster to an Azure Container Registry?

Accepted Answer

Azure Managed Identity.. Option A (Azure Managed Identity) is correct because AKS can use a system-assigned or user-assigned managed identity to authenticate to ACR via the AcrPull role assignment, eliminating stored credentials; the kubelet identity is granted the AcrPull role on the registry. Option B (Microsoft Entra ID Service Principal) is correct because you can create an AAD service principal, assign it the AcrPull role on the ACR, and store its appId/password as a Kubernetes imagePullSecret referenced by the pod spec. Option C (Azure Monitor) is incorrect because it is a monitoring/observability service, not an authentication mechanism for pulling images. Option D (Hardcoded Docker config file) is incorrect because embedding credentials in a plaintext docker config.json is insecure and not a supported AKS-to-ACR authentication method. Option E (Azure Policy) is incorrect because it enforces governance/compliance rules, not registry authentication.

Answer

Azure Monitor.

Answer

Hardcoded Docker config file.

Answer

Azure Policy.

Question 8

You are designing an asynchronous AI image classification workflow. Client applications upload images to Azure Blob Storage and need to be notified immediately when processing completes. The notification must include custom metadata generated by the AI worker. Which architecture should you implement?

Accepted Answer

The AI worker publishes a custom event to an Azure Event Grid Topic upon completion.. The AI worker publishing a custom event to an Azure Event Grid Topic upon completion is correct because Event Grid supports custom events with metadata and can push notifications to subscribers immediately. This allows clients to receive the custom metadata generated by the worker. Other options do not provide immediate notification with custom metadata.

Answer

Use Azure Service Bus topics with session IDs set to the image hash.

Answer

Configure an Azure Storage queue that triggers whenever a blob is modified.

Answer

Poll the Azure Blob Storage container metadata every 5 seconds from the client app.

Question 9

When configuring an Azure AI Search vectorizer for integrated vectorization, which TWO parameters or connections must be specified to link to an Azure OpenAI deployment? (Choose two)

Accepted Answer

Model name (e.g., text-embedding-ada-002). Option A is correct because an Azure AI Search vectorizer that targets Azure OpenAI must specify the embedding model name (for example, text-embedding-ada-002 or text-embedding-3-large) so the service knows which deployed model to invoke for generating vectors. Option E is correct because the vectorizer also requires the Azure OpenAI resource endpoint URL plus authentication details — either an API key or a managed identity — to authorize calls to that deployment. Options B, C, and D are incorrect because SQL Server connection strings, Cosmos DB container database names, and Blob storage SAS token expiration times are unrelated to linking a vectorizer to an Azure OpenAI embedding deployment; they pertain to other data sources or storage access scenarios.

Answer

SQL Server connection string

Answer

Cosmos DB container database name

Answer

Blob storage SAS token expiration time

Question 10

Which similarity metric measures the cosine of the angle between two vectors, focusing on orientation rather than magnitude, and is commonly used in Azure AI Search?

Accepted Answer

cosine. Cosine similarity measures the cosine of the angle between two vectors, which captures their orientation (direction) while ignoring their magnitude. This makes it ideal for comparing text embeddings of different lengths, and it is the default and most commonly used similarity metric in Azure AI Search vector profiles. The option 'cosine' directly names this metric.

Answer

manhattan

Answer

euclidean

Answer

dotProduct

Question 11

You are storing vector embeddings generated by Azure OpenAI in Azure Cosmos DB for NoSQL. Which built-in SQL function must you use within your query to compute vector distance when executing a similarity search?

Accepted Answer

VectorDistance(). Azure Cosmos DB for NoSQL exposes the built-in VectorDistance() function, which computes the distance between a stored vector and a query vector using the configured distance function (cosine, dot product, or Euclidean). It is used in the ORDER BY clause of a query to return the nearest neighbors. This is the only correct built-in function name for vector similarity in Cosmos DB for NoSQL.

Answer

EmbeddingDistance()

Answer

GetVectorDistance()

Answer

CosineSimilarity()

Question 12

You are building an AI container image and need to minimize its final size to speed up deployment times. Which multi-stage build practice should you follow?

Accepted Answer

Use a builder stage for compilation and copy only runtime artifacts into a slim base image. Multi-stage builds use a builder stage containing compilers, SDKs, and build dependencies, then copy only the compiled artifacts (binaries, wheels, model files needed at runtime) into a minimal runtime base image such as python:3.11-slim or alpine. This keeps the final image free of build toolchains, caches, and intermediate layers, dramatically reducing image size and pull time. The other options either bloat the image or are unrelated to build strategy.

Answer

Store model weights directly inside the container image layers

Answer

Install all development tools and pip packages in a single RUN instruction

Answer

Disable container image compression when pushing to ACR

Question 13

You need to store sensitive database connection strings and AI API keys used by containerized applications running in Azure Container Apps. Which feature should you use to securely store and reference these values?

Accepted Answer

Container App Secrets. Container App Secrets provide an encrypted, managed store for sensitive values such as connection strings and API keys, and they can be referenced from environment variables or volume mounts without exposing the plaintext in the app definition. They are scoped to the Container App and integrated with the platform's secret management, making them the correct secure storage mechanism.

Answer

A standard DNS TXT record

Answer

Azure Storage account public blob container

Answer

Plain text Dockerfile ENV directives

Question 14

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. In Azure AI Search, an HNSW algorithm profile exposes three tunable graph parameters: efConstruction (option A), which controls the size of the dynamic candidate list during index construction and thus the quality of the built graph; efSearch (option D), which controls the size of the candidate list at query time and trades recall against latency; and m (option E), which sets the number of bidirectional links created per node and governs graph connectivity and memory usage. These are exactly the parameters documented for the HNSW configuration object in Azure AI Search. Option B (bm25K1) is a parameter of the BM25 ranking function used for keyword relevance scoring, not vector search, and option C (chunkOverlapSize) relates to how documents are split during chunking in a skillset or integrated vectorization pipeline, not to the HNSW graph itself.

Answer

bm25K1

Answer

chunkOverlapSize

Question 15

Which Azure AI Search metric or tool helps administrators monitor query volume, latency, and throttling rates across search units?

Accepted Answer

Azure Monitor metrics and diagnostic logs. Azure Monitor metrics and diagnostic logs are the native observability tools for Azure AI Search, exposing query volume (SearchQueriesPerSecond), latency (SearchLatency), and throttling (ThrottledSearchQueriesPercentage) broken down by search unit. Diagnostic logs can be routed to Log Analytics, Event Hubs, or Storage for deeper analysis of query and indexing operations. These are the only options that directly instrument the search service itself.

Answer

App Service stream logs

Answer

Azure Storage Explorer

Answer

Cosmos DB Data Explorer