AI-103 Plan And Manage AN Azure AI Solution Practice Test 7 — 15 Questions

Question 1

Your development team needs to monitor the latency and total call volume of an Azure AI Document Intelligence resource in near real-time. Where can you view these pre-aggregated metrics without writing custom log queries?

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

Accepted Answer

The Metrics blade of the Azure AI Document Intelligence resource in the Azure portal. The Metrics blade of an Azure AI Document Intelligence resource provides pre-aggregated, near-real-time platform metrics such as latency, call volume, and error rates without requiring any KQL queries. These metrics are emitted automatically by the Azure Monitor platform and are viewable directly in the portal.

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The Cost Management + Billing cost analysis dashboard

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The Log Analytics workspace query editor using KQL

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The Azure Advisor recommendations blade

Question 2

You are managing an Azure AI Custom Vision project and need to automate model training and evaluation updates as new training images are tagged. Which REST API or SDK component should you use to initiate the training iteration process?

Accepted Answer

Custom Vision Training API TrainProject method. The Custom Vision Training API provides the TrainProject method, which initiates a training iteration for a specified project. This is the correct API to automate model training when new tagged images are added. It allows you to programmatically trigger training and then evaluate the resulting iteration.

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Azure Machine Learning Pipeline REST endpoint

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Azure AI Vision Prediction API

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Azure AI Content Safety moderation batch API

Question 3

Your organization requires that all data sent to Azure OpenAI Service remains encrypted using a key that your security team fully controls and rotates. Which feature should you configure during resource creation?

Accepted Answer

Customer-managed keys (CMK) with Azure Key Vault. Customer-managed keys (CMK) with Azure Key Vault allow an organization to supply and control the encryption keys used to protect data at rest in Azure OpenAI Service. This satisfies the requirement for full key control and rotation by the security team, as opposed to Microsoft-managed keys.

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Azure Information Protection client-side encryption

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Service-managed keys (Default encryption)

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Transparent Data Encryption with a SQL Managed Instance

Question 4

You are configuring diagnostic settings for an Azure AI Search service to audit administrative actions and query logs. Which destination should you choose if you want to stream these logs into a SIEM tool like Microsoft Sentinel for real-time security analysis?

Accepted Answer

Send to an Event Hub. Azure AI Search diagnostic settings support three export destinations: Log Analytics workspace, Azure Storage account, and Azure Event Hubs. Event Hubs is the correct choice for streaming telemetry in near real-time to external SIEM platforms such as Microsoft Sentinel, Splunk, or QRadar, because Sentinel natively ingests from Event Hubs via its data connectors. This enables continuous, low-latency forwarding of administrative and query logs for correlation and alerting.

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Send to an Azure DevOps pipeline

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Export to a local CSV file via Azure CLI

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Archive to a storage account

Question 5

You are deploying an Azure OpenAI GPT-4 model in a region experiencing high demand, and you need to guarantee a dedicated throughput level without contending with other tenants. Which deployment type should you select?

Accepted Answer

Provisioned (PTU) deployment. Provisioned Throughput Units (PTU) deployments reserve dedicated model capacity for your subscription, guaranteeing consistent throughput and latency without contending with other tenants. This is the only deployment type that provides the dedicated capacity and isolation the scenario requires. Standard and serverless deployments share pooled capacity and can be throttled under high regional demand.

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Serverless deployment

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Standard deployment

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Batch deployment

Question 6

You are implementing a multi-region disaster recovery strategy for an Azure OpenAI deployment. If the primary region goes down, how should you configure traffic redirection to the secondary region using Azure Front Door?

Accepted Answer

Use priority routing with the primary region set to priority 1 and the secondary region set to priority 2.. Azure Front Door priority routing assigns a priority number to each origin, and traffic is sent to the lowest-numbered (highest-priority) healthy origin first. Setting the primary region to priority 1 and the secondary to priority 2 ensures all traffic goes to the primary during normal operation and automatically fails over to the secondary only when the primary becomes unhealthy. This is the standard active-passive DR pattern.

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Use geographic routing based on the client IP location.

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Use weighted routing with a 50/50 split between regions.

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Use performance-based routing to the lowest latency node.

Question 7

Your development team needs to test a new Azure AI Language service feature without incurring ongoing charges when the service is idle. Which billing model is appropriate for initial prototyping?

Accepted Answer

Pay-as-you-go (Consumption-based metering). Pay-as-you-go (consumption-based metering) charges only for the transactions and features actually used, with no upfront commitment or idle cost, making it ideal for prototyping a new Azure AI Language feature. It lets the team experiment freely and stop incurring charges when the service is idle. This is the standard billing model for initial development and testing.

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Dedicated hardware hosting

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Reserved Instance 3-year term

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Commitment tier (Pre-purchased capacity)

Question 8

You are auditing your Azure AI services for cost optimization. You discover an Azure AI Search service that has been over-provisioned with too many search units and replica nodes. How can you right-size the service without losing your existing indexes?

Accepted Answer

Modify the scale settings in the Azure portal to reduce the number of replicas and partitions.. Azure AI Search lets you change scale settings — the number of replicas and partitions — directly in the portal without deleting the service, and existing indexes are preserved. Reducing replicas lowers query throughput capacity while reducing partitions lowers storage and indexing capacity, so you can right-size to match actual load. This is the non-destructive way to optimize cost.

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Switch the pricing tier from Standard to Free.

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Export all index definitions, delete the service, and re-import the indexes into a new Basic tier service.

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Delete the search service and recreate it with fewer replicas and partitions.

Question 9

You are provisioning an Azure AI Language resource and want to ensure that all telemetry and activity logs are retained for compliance auditing over a 365-day period. Where should you configure this retention policy?

Accepted Answer

In the resource's Diagnostic settings by specifying a retention period on a storage account destination.. Diagnostic settings in Azure allow you to configure the collection and retention of telemetry and activity logs. By specifying a retention period (e.g., 365 days) on a storage account destination, you ensure logs are retained for compliance auditing. This is the correct place to set retention policies for Azure AI Language resource logs.

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In the Microsoft Defender for Cloud security policy blade.

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In the Azure AI Language service Studio configuration panel.

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In the Azure Cost Management budget alert settings.

Question 10

Your development team wants to deploy a new version of an Azure OpenAI model while ensuring zero downtime for client applications currently calling the existing model deployment. What is the recommended deployment strategy?

Accepted Answer

Create a new deployment with a distinct deployment name for the new model version, then update application configuration to point to the new deployment.. Azure OpenAI supports multiple simultaneous deployments of the same or different models under distinct deployment names within a single resource. By creating a new deployment for the updated model version and then switching application configuration to the new deployment name, traffic continues to be served by the old deployment until the cutover, achieving zero downtime. This also enables easy rollback by reverting the configuration.

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Overwrite the existing model deployment name with the new model version.

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Delete the Azure OpenAI resource and provision a new one in the same region.

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Downgrade the pricing tier to pause traffic while updating the model version.

Question 11

You need to secure communications between an Azure App Service hosting your web app and an Azure AI Computer Vision service. The web app must authenticate to Computer Vision without storing API keys in application settings. Which feature should you enable?

Accepted Answer

Managed Identity on the App Service and RBAC role assignment on the Computer Vision resource.. Enabling a managed identity on the App Service and assigning an appropriate RBAC role on the Computer Vision resource lets the app obtain Microsoft Entra ID tokens automatically, eliminating the need to store API keys in application settings. The managed identity is trusted by Microsoft Entra ID, and the RBAC role grants access to the Computer Vision resource. This is the recommended keyless authentication pattern for service-to-service communication in Azure.

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API Management subscription keys

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Cross-Origin Resource Sharing (CORS)

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Storage account shared access signatures (SAS)

Question 12

You are monitoring the health and operational status of an Azure AI multi-service account. Which TWO built-in metrics can you track in Azure Monitor to evaluate performance and reliability? (Choose two.)

Accepted Answer

Server Errors (failed requests due to server-side issues). Option B, Server Errors, is correct because Azure Monitor exposes this metric for an Azure AI multi-service account to count failed requests caused by server-side issues, which directly reflects reliability and helps diagnose service-side faults. Option D, Total Calls, is correct because it tracks the overall request volume hitting the account, a key performance and usage indicator for capacity and trend analysis. The remaining options do not belong: employee payroll deductions (A) is an HR/finance concept unrelated to Azure Monitor metrics, virtual machine disk defragmentation percentage (C) is a VM storage metric not exposed for an AI multi-service account, and browser cache hit ratio (E) is a client-side web performance measure rather than an Azure Monitor metric for this resource.

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Employee payroll deductions

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Virtual machine disk defragmentation percentage

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Browser cache hit ratio

Question 13

You are hardening an Azure OpenAI service deployment for a financial application that handles sensitive customer data. Which THREE security configurations should you apply? (Choose three.)

Accepted Answer

Authenticate client applications using Microsoft Entra ID (Azure AD) tokens instead of static API keys.. Option B is correct because authenticating with Microsoft Entra ID (Azure AD) tokens enables role-based access control (RBAC), short-lived credentials, and conditional access policies, eliminating the risk of leaked long-lived static API keys. Option D is correct because disabling public network access and using Azure Private Link with private endpoints keeps all traffic to the Azure OpenAI resource on the Microsoft backbone/private IP space, preventing exposure to the public internet and data exfiltration. Option E is correct because customer-managed keys (CMK) stored in Azure Key Vault give the financial organization control over the encryption keys protecting data at rest, satisfying regulatory and key-rotation requirements. Option A is wrong because storing API keys in plain text in source control exposes credentials to anyone with repository access and is a critical secret-management anti-pattern. Option C is wrong because enabling anonymous access removes authentication entirely, allowing unauthenticated clients to invoke the model and access sensitive data.

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Store API keys in plain text within the client application's source code repository for easy developer access.

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Enable anonymous access on the Azure OpenAI endpoint to simplify client connection testing.

Question 14

Your organization is planning to deploy multiple Azure AI services and needs to implement robust cost management and governance practices. Which TWO actions should you take? (Choose two.)

Accepted Answer

Implement Azure Policy to enforce mandatory cost-tracking tags on all AI resource deployments.. Option A is correct because Azure Policy can enforce tagging requirements (such as cost center, owner, or environment tags) at deployment time, and consistent tags are the foundation for cost tracking, chargeback, and filtering in Azure Cost Management and Billing. Option D is correct because separating AI resources into distinct resource groups and subscriptions by business unit and environment provides clear cost boundaries, enables per-subscription budgets and access control, and prevents dev/test spend from being commingled with production. Option B is not appropriate because consolidating all environments into one resource group removes cost and access separation and makes it hard to attribute spend or apply environment-specific governance. Option C is not appropriate because disabling diagnostic logging sacrifices auditability, security monitoring, and troubleshooting, and cost reduction should not come at the expense of required telemetry. Option E is not appropriate because the F0 free tier has strict call and transaction limits and no production SLA, so it cannot support production AI workloads.

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Deploy all development, test, and production AI resources into a single shared resource group to simplify administration.

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Disable diagnostic logging across all AI resources to reduce Azure storage and log ingestion costs.

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Use the Free (F0) pricing tier for all production workloads to eliminate infrastructure costs.

Question 15

When planning the model selection and versioning lifecycle for Azure OpenAI deployments in an enterprise environment, which TWO practices should you follow? (Choose two.)

Accepted Answer

Monitor Microsoft documentation and deprecation notices for model version lifecycles.. Option B is correct because Azure OpenAI model versions have defined lifecycles and Microsoft publishes deprecation and retirement notices; monitoring official documentation lets you plan migrations before a version is retired and avoid unexpected service disruption. Option D is correct because new model versions can change output quality, latency, and behavior, so validating them in a staging or development environment with representative workloads before promoting to production is the standard safe deployment practice. Option A is wrong because deprecated model versions are eventually retired and cannot be used indefinitely, and pinning to them does not guarantee identical outputs across the service. Option C is wrong because automatically upgrading production to the latest preview version bypasses testing and exposes production workloads to unvalidated, non-GA behavior. Option E is wrong because refusing to ever update model versions leads to forced migrations when retirement occurs and accumulates unmanaged technical debt.

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Use deprecated model versions indefinitely to maintain identical outputs.

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Automatically upgrade all production model deployments to the latest preview version the moment it is released.

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Never update model versions once deployed to avoid any future validation work.