AI-200 Practice Test 18 — 15 Questions

Question 1

Which TWO of the following features help manage traffic to containerized applications?

Accepted Answer

Service Mesh (Istio/Linkerd).. A Service Mesh such as Istio or Linkerd (option C) is correct because it provides layer-7 traffic management for containerized workloads, including intelligent routing, load balancing, traffic splitting for canary/blue-green deployments, retries, timeouts, and mutual TLS between pods via sidecar proxies. A Kubernetes Ingress Controller (option E) is correct because it exposes HTTP/HTTPS routes from outside the cluster to Services inside the cluster, performing host- and path-based routing, TLS termination, and load balancing to containerized applications. Azure Monitor (option A) is an observability platform for metrics, logs, and alerts, not a traffic-routing mechanism. Azure Key Vault (option B) is a secrets, keys, and certificate management service, unrelated to directing application traffic. Azure Blob Storage (option D) is object storage for unstructured data and does not manage or route network traffic to containers.

Answer

Azure Monitor.

Answer

Azure Key Vault.

Answer

Azure Blob Storage.

Question 2

You are configuring vector search in Azure Cosmos DB for NoSQL. You want to ensure that vectors are indexed using the DiskANN algorithm for high recall and fast search performance. Which index type should you specify in the vector embedding policy?

Accepted Answer

diskANN. Azure Cosmos DB for NoSQL supports the DiskANN algorithm for vector indexing, which is specified as `diskANN` in the vector embedding policy. DiskANN is optimized for high recall and low-latency search on large-scale vector datasets, making it the correct choice for the requirement.

Answer

faissHNSW

Answer

hnsw

Answer

ivfFlat

Question 3

You are containerizing a large language model (LLM) inference API to run in Azure Container Apps. The model weights exceed 15 GB and cause container startup timeouts because the image takes too long to pull from Azure Container Registry (ACR). You need to optimize the container startup performance without reducing the model size. What is the best approach?

Accepted Answer

Enable ACR artifact streaming for the container registry.. ACR artifact streaming lets ACR stream image layers directly to the container runtime on demand rather than requiring the full image to be pulled and extracted before startup. This dramatically reduces cold-start time for large images like LLM weight layers, without shrinking the model. It is the purpose-built ACR feature for exactly this startup-latency problem.

Answer

Upgrade the Azure Container Apps environment to use HTTP/2 protocol.

Answer

Store the model weights in an Azure Blob Storage account and mount them via an ephemeral volume.

Answer

Configure ACR content trust and geo-replication.

Question 4

You are designing a big data telemetry ingestion pipeline using Azure Event Hubs. Which THREE configuration options or properties must you consider when planning Event Hubs capacity and scaling? (Choose three)

Accepted Answer

Partition Count. Partition Count (A) is correct because partitions are the fundamental unit of scale and parallelism in Event Hubs; the number of partitions determines the maximum concurrent readers per consumer group and directly caps ingress/egress throughput, so it must be planned up front since it cannot be reduced after creation. Throughput Units (TUs) or Processing Units (PUs) (D) are correct because they define the provisioned capacity of the namespace (TUs for Standard/Dedicated, PUs for Premium), governing ingress (e.g., 1 MB/s per TU) and egress limits and enabling auto-inflate scaling. Message Retention Period (E) is correct because it determines how long events remain available in the partition stream (1–7 days Standard, up to 90 days Premium/Dedicated), which affects storage consumption, replay/recovery windows, and downstream checkpointing design. Service Bus Lock Duration (B) is incorrect because it is a Service Bus queue/subscription peek-lock setting, not an Event Hubs property. Event Grid Webhook Handshake Timeout (C) is incorrect because it belongs to Event Grid event subscription delivery validation, not Event Hubs capacity planning.

Answer

Service Bus Lock Duration

Answer

Event Grid Webhook Handshake Timeout

Question 5

You are designing an MLOps pipeline on Azure Kubernetes Service (AKS) for a computer vision model. You need to configure GPU sharing among multiple inference pods on the same node to optimize cost. Which TWO actions should you take? Each correct answer presents part of the solution.

Accepted Answer

Enable GPU time-slicing or NVIDIA Multi-Instance GPU (MIG) configuration.. Option B is correct because GPU sharing on AKS is achieved through NVIDIA's time-slicing or Multi-Instance GPU (MIG) configuration, which partitions a single physical GPU so multiple inference pods can run concurrently on the same node, directly reducing cost. Option E is correct because the NVIDIA GPU Operator automates the deployment and lifecycle of the GPU device plugin, drivers, container runtime, and related components on AKS, which is a prerequisite for enabling and managing time-slicing or MIG configurations. Option A is incorrect because a system-node-critical priority class only affects scheduling priority and eviction behavior; it does not enable GPU sharing. Option C is incorrect because KEDA with an Azure Queue trigger provides event-driven autoscaling and has nothing to do with partitioning or sharing a GPU among pods. Option D is incorrect because Azure CNI dynamic IP allocation addresses pod IP address management and networking, not GPU resource sharing.

Answer

Set the pod priority class to system-node-critical.

Answer

Deploy the KEDA scaler with an Azure Queue trigger.

Answer

Configure the Azure CNI plugin for dynamic IP allocation.

Question 6

When configuring vector search in Azure Cosmos DB for NoSQL, which TWO properties must be defined within the container's vector embedding policy? Choose two.

Accepted Answer

Path (the JSON property containing the vector array). Option D is correct because the vector embedding policy must specify the "path" — the JSON property path in each document that holds the vector array — so Cosmos DB knows which field to index for vector search. Option E is correct because the policy must also declare the vector's "dataType" (e.g., float32, float16, int8) and "dimensions" (e.g., 1536), which define the element type and length of the vector array and must match the actual embeddings. The container partition key definition (A) is set in the container's key definition, not the vector embedding policy, and is unrelated to vector indexing. BM25 k1 and b parameters (B) belong to full-text search scoring configuration, not vector search. Replication lag threshold (C) is a consistency/global replication setting and has no place in a vector embedding policy.

Answer

Container partition key definition

Answer

BM25 k1 and b tuning parameters

Answer

Replication lag threshold

Question 7

Which THREE of the following are benefits of using ACR Tasks for containerized AI workloads?

Accepted Answer

Triggering builds on base image updates.. Option B is correct because ACR Tasks can automatically trigger a rebuild of an image whenever its base image is updated in the registry, which keeps containerized AI workloads current with patched or upgraded dependencies. Option C is correct because ACR Tasks provides automated image building from source code, using a Dockerfile or buildpacks, so commits to a Git repository or source archive can produce new container images without a local Docker daemon. Option D is correct because ACR Tasks supports multi-step tasks, defined in a YAML file, that chain build, run, and push steps for complex container workflows. Option A is not a benefit of ACR Tasks, since automatic scaling of the registry is a feature of the Azure Container Registry service tier (for example, Premium with geo-replication and throughput scaling), not of ACR Tasks. Option E is not applicable because ACR Tasks builds and manages container images; it does not directly deploy to Azure SQL, which is a relational database service rather than a container deployment target.

Answer

Automatic scaling of the registry.

Answer

Direct deployment to Azure SQL.

Question 8

You are optimizing an enterprise search architecture using Azure AI Search. Which THREE mechanisms help control latency and resource consumption during heavy vector query loads? (Choose three)

Accepted Answer

Tuning HNSW efSearch parameter to balance recall speed and latency. Option A is correct because the HNSW efSearch parameter controls the size of the dynamic candidate list during vector search; lowering efSearch reduces the number of distance computations, cutting latency at some cost to recall, which is exactly the recall/latency trade-off needed under heavy vector query loads. Option C is correct because scalar quantization compresses float32 vectors into smaller representations (e.g., int8), reducing memory footprint and memory bandwidth pressure so more vectors fit in RAM and queries execute faster. Option E is correct because adding search replicas horizontally distributes query traffic across multiple nodes, increasing concurrent query throughput and preventing any single node from becoming a latency bottleneck. Option B is incorrect because disabling caching removes a latency-reducing mechanism and pushing documents per partition beyond limits is unsupported and degrades performance. Option D is incorrect because exhaustive k-NN scans every vector for each query, which dramatically increases compute and latency and is unsuitable as a default under heavy load.

Answer

Disabling caching and increasing index document count per partition beyond limits

Answer

Running exhaustive k-NN searches for every query by default

Question 9

When migrating data into Azure AI Search for vector search, you encounter issues with ingestion. Which TWO of the following are valid reasons why an indexer might fail to ingest vector data?

Accepted Answer

The vector dimensions in the source data exceed the index field's configured dimensions.. Option A is correct because Azure AI Search validates vector length at ingestion time: if the source vector has more (or fewer) dimensions than the index field's configured 'dimensions' value, the indexer fails with a dimension mismatch error. Option D is correct because the vector field must be defined as Collection(Edm.Single) (or another supported vector type) and the source data must supply a numeric array; an array of strings or other non-numeric type cannot be cast to the vector field and causes ingestion to fail. Option B is not a valid reason because a deprecated embedding model still produces numeric vectors that the indexer can ingest; deprecation affects the model's availability, not the indexer's type/dimension validation. Option C is not a valid reason because marking a Collection(Edm.Single) field as 'filterable' is a supported configuration (though it consumes extra storage) and does not by itself cause ingestion failure. Option E is not a valid reason because an 'AzureOpenAI' skill is only needed to generate embeddings at index time; if the source already contains vectors, no such skill is required, and its absence does not cause an ingestion failure.

Answer

The embedding model used is deprecated.

Answer

The 'Collection(Edm.Single)' field is marked as 'filterable'.

Answer

The indexer is not configured with an 'AzureOpenAI' skill.

Question 10

You are analyzing security and compliance logs in an Azure Log Analytics workspace. You want to write KQL queries to investigate potential security incidents or token acquisition failures. Which THREE of the following KQL tables or operators are valid and useful for this investigation? (Choose three)

Accepted Answer

The exceptions table to analyze application error stack traces and failure types.. Option A is correct because the exceptions table in Application Insights / Log Analytics stores exception telemetry including type, message, and stack traces, which is exactly what you need to diagnose application errors and token acquisition failures. Option D is correct because summarize is a core KQL operator that groups rows by one or more columns and applies aggregations such as count(), avg(), or percentile(), making it essential for spotting spikes or patterns in security and compliance telemetry. Option E is correct because the dependencies table records outbound calls (HTTP, SQL, Azure service calls) with target, duration, and success/failure result, which helps trace failed token or API calls. Option B is not valid because KQL does not use SQL's SELECT * FROM syntax; queries start with a table name followed by pipe operators. Option C is not valid because DROP TABLE is a database DDL command and is not supported inside Application Insights or Log Analytics KQL queries.

Answer

The SELECT * FROM command syntax traditionally used in relational SQL databases.

Answer

The DROP TABLE administrative command inside Application Insights log streams.

Question 11

You are writing a query in Azure Cosmos DB for NoSQL that sorts results by vector similarity and also filters results by a category property. How must you order the clauses in your SQL statement?

Accepted Answer

The WHERE clause must come before the ORDER BY VectorDistance() clause. In Azure Cosmos DB for NoSQL, SQL clause order is fixed: SELECT, FROM, WHERE, ORDER BY. When using VectorDistance() in ORDER BY, the WHERE clause must still appear before ORDER BY, so filtering by category happens first and then results are sorted by vector similarity. This is standard SQL clause ordering, not a vector-specific exception.

Answer

VectorDistance must be placed in the SELECT clause instead of ORDER BY

Answer

WHERE and ORDER BY clauses cannot be used in the same query when using vectors

Answer

The ORDER BY clause must come before the WHERE clause

Question 12

Your application publishes events to an Azure Event Grid custom topic. You need to inspect the operational health and delivery success metrics of your event subscriptions. Where should you view these metrics natively in the Azure Portal?

Accepted Answer

The Metrics blade of the Event Grid Topic or Domain in the Azure Portal. Azure Event Grid exposes native platform metrics on the Event Grid Topic or Domain resource itself, including publish/delivery success, matched/attempted/dropped events, and delivery latency. These are surfaced under the Metrics blade of the topic or domain in the Azure Portal, where you can chart and alert on them. This is the built-in operational health view for event subscriptions without needing external tooling.

Answer

The Azure Storage Account Access Logs

Answer

The Service Bus Explorer tool

Answer

The App Service Kudu diagnostic console

Question 13

You need to deploy a containerized AI model to Azure Container Apps and configure it to scale based on Azure Service Bus queue messages. Which scaler should you specify?

Accepted Answer

azure-service-bus. Azure Container Apps uses KEDA-based scaling, and the azure-service-bus scaler is the built-in KEDA scaler designed to read Service Bus queue or topic subscription message counts and scale replicas accordingly. Specifying azure-service-bus as the scale rule type with the queue name and connection metadata lets Container Apps scale in/out based on message backlog. This directly matches the requirement to scale on Service Bus queue messages.

Answer

azure-blob

Answer

memory

Answer

cron

Question 14

You need to monitor the monthly spending on Azure OpenAI token consumption and receive an alert when expenditures exceed a predefined threshold. Which tool should you use?

Accepted Answer

Azure Cost Management Budgets and Alerts. Azure Budgets in Cost Management allow you to set spending thresholds and automated email alerts when thresholds are reached.

Answer

Log Analytics Workspace usage and estimated costs view

Answer

Application Insights availability tests

Answer

Azure Security Center compliance dashboard

Question 15

You are building an event-driven application where messages sent to an Azure Service Bus queue must not be processed until a specific date and time in the future (e.g., 2 hours from now). What property should you set on the outgoing message?

Accepted Answer

ScheduledEnqueueTimeUtc. ScheduledEnqueueTimeUtc is the Service Bus message property that defers a message's visibility in the queue until a specified UTC timestamp. Setting it to two hours in the future means the broker holds the message and only makes it available to receivers after that time, exactly matching the delayed-processing requirement. This is the native, broker-side scheduling mechanism for Service Bus.

Answer

TimeToLive

Answer

LockDuration

Answer

SequenceNumber