AI-103 Plan And Manage AN Azure AI Solution Practice Test 6 — 15 Questions

Question 1

You are designing a disaster recovery and high availability strategy for an enterprise Azure OpenAI deployment. Which THREE architectural components or practices should you include? Each correct answer presents part of the solution.

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

Accepted Answer

Configure Azure Monitor metric alerts to detect latency spikes or availability drops.. Option A is correct because Azure Monitor metric alerts on latency and availability metrics provide the observability needed to detect degradation of an Azure OpenAI resource and trigger operational or automated responses before users are severely impacted. Option B is correct because Azure OpenAI returns transient errors such as HTTP 429 (rate limiting) and 5xx responses, so client-side retry with exponential backoff and failover to a secondary endpoint/region is essential for resilience and continuity. Option D is correct because deploying secondary Azure OpenAI resources in a paired Azure region enables regional redundancy and supports failover if the primary region becomes unavailable, which is a core HA/DR pattern. Option C is not correct because Azure Backup does not snapshot live model memory states or the internal runtime state of an Azure OpenAI model; it is for supported Azure resource data, not model inference memory. Option E is not correct because Azure OpenAI models run as a managed cloud service and cannot be executed offline on client laptops, and storing model weights locally is neither supported nor part of an Azure HA/DR design.

Answer

Rely exclusively on Azure Backup to snapshot live model memory states.

Answer

Store all model weights locally on client laptops for offline execution.

Question 2

You are preparing an ARM template to deploy an Azure AI multi-service account. Which TWO properties are mandatory within the ARM template resource definition? Each correct answer presents part of the solution.

Accepted Answer

sku. Option B (sku) is mandatory because an Azure AI multi-service (Cognitive Services) account resource requires a pricing/tier definition, e.g. "sku": {"name": "S0"}, to specify the billing tier and feature set of the account. Option E (location) is mandatory because every Azure resource, including a Cognitive Services multi-service account, must declare the Azure region where it is deployed, e.g. "location": "eastus". Options A (VirtualMachineSize), C (OperatingSystemType), and D (KernelVersion) do not belong because they are compute/VM-related properties relevant to virtual machines or VM scale sets, not to a Cognitive Services multi-service account, which is a PaaS resource with no OS or VM sizing attributes.

Answer

VirtualMachineSize

Answer

OperatingSystemType

Answer

KernelVersion

Question 3

Your organization is implementing strict network isolation for Azure OpenAI. Direct internet access from worker nodes is prohibited. Which THREE mechanisms must be correctly configured to allow secure communication? Each correct answer presents part of the solution.

Accepted Answer

Public network access disabled on the Azure OpenAI resource. Option A is correct because disabling public network access on the Azure OpenAI resource ensures that the service is not reachable over the public internet, forcing all traffic through approved private paths. Option D is correct because a Private DNS zone (privatelink.openai.azure.com) must be integrated with the virtual network so that the FQDN <resource>.openai.azure.com resolves to the private endpoint's private IP instead of the public IP. Option E is correct because an Azure Private Endpoint placed in the application virtual network subnet provides the private IP interface that worker nodes use to reach the Azure OpenAI service over the Microsoft backbone. Option B is wrong because assigning public IP addresses to the AI service endpoint contradicts the strict isolation requirement and would expose the service publicly. Option C is wrong because Azure Bastion is a jump-host service for RDP/SSH access to VMs and has no role in connecting to Azure OpenAI backend storage.

Answer

Public IP addresses assigned directly to the AI service endpoint

Answer

Azure Bastion host attached to the OpenAI backend storage

Question 4

You are managing Azure OpenAI deployments and need to monitor token consumption and error rates. Which TWO Azure Monitor features should you use to analyze this telemetry? Each correct answer presents part of the solution.

Accepted Answer

Azure Monitor Metrics Explorer to graph real-time metrics like Total Calls and Latency.. Option A is correct because Azure Monitor Metrics Explorer lets you plot near real-time platform metrics for Azure OpenAI, including Total Calls, Token Usage (prompt/completion tokens), and Latency, which directly addresses monitoring token consumption and error rates at a glance. Option C is correct because Azure OpenAI diagnostic settings can route request/response logs and metrics to a Log Analytics workspace, where Kusto Query Language (KQL) queries enable deep analysis of per-request token counts, status codes, and error patterns over time. Together, Metrics Explorer provides fast visual monitoring while Log Analytics with KQL provides granular, queryable telemetry. Option B is incorrect because Azure DevOps commit history tracks source code changes, not Azure OpenAI runtime telemetry. Option D is incorrect because Microsoft 365 compliance center content search is for eDiscovery of Microsoft 365 content, not Azure Monitor telemetry. Option E is incorrect because Azure Bastion session monitoring relates to secure RDP/SSH access to VMs, not OpenAI token or error metrics.

Answer

Azure DevOps commit history dashboard

Answer

Microsoft 365 compliance center content search

Answer

Azure Bastion session monitor

Question 5

When configuring role-based access control (RBAC) for an Azure AI Services account, which TWO built-in roles are appropriate for assigning to users who only need to invoke AI models without managing the underlying resource infrastructure? Each correct answer presents part of the solution.

Accepted Answer

Cognitive Services OpenAI User. Option B, Cognitive Services OpenAI User, is correct because it grants the ability to call Azure OpenAI model inference endpoints (such as chat completions and embeddings) without granting permissions to manage the account, keys, or deployments. Option D, Cognitive Services User, is correct because it allows invoking the broader set of Azure AI Services (Cognitive Services) endpoints for inference while excluding resource-management actions. Both roles satisfy the least-privilege requirement of invoking AI models without managing underlying infrastructure. Option A, Owner, is incorrect because it grants full control including managing resources and assigning roles, far exceeding inference-only needs. Option C, Contributor, is incorrect because it allows creating and managing resources (though not role assignment), which is more than invoking models requires. Option E, Storage Blob Data Reader, is incorrect because it only permits reading blob data and has no relevance to invoking AI model endpoints.

Answer

Owner

Answer

Contributor

Answer

Storage Blob Data Reader

Question 6

You are designing a deployment strategy for an Azure AI Custom Vision project in an enterprise pipeline. Which THREE practices should you follow to ensure secure and repeatable deployments? Each correct answer presents part of the solution.

Accepted Answer

Store API keys and connection strings securely in Azure Key Vault rather than hardcoding them.. Option A is correct because storing API keys and connection strings in Azure Key Vault keeps secrets out of source code and pipeline logs, and Custom Vision training/prediction keys can be referenced at runtime via Key Vault references or managed identities, supporting secure deployments. Option C is correct because Infrastructure as Code with ARM templates or Bicep provisions Azure resources (Custom Vision accounts, storage, compute) idempotently and consistently across environments, which is essential for repeatable deployments. Option D is correct because automated integration and validation tests in CI/CD pipelines verify that the deployed Custom Vision endpoint, keys, and model behavior work before production release, catching regressions and misconfigurations early. Option B is incorrect because manual portal configuration is error-prone, undocumented, and not repeatable, undermining both security and consistency. Option E is incorrect because embedding production API keys as plaintext comments in Bicep files exposes secrets in source control and violates secure secret-management practices.

Answer

Manually create and configure all production resources directly in the Azure portal without documentation.

Answer

Store production API keys as plaintext comments in Bicep template files.

Question 7

Your company's compliance mandate requires that all data processed by Azure AI Speech and Document Intelligence services remain encrypted using keys managed entirely by your organization. Which THREE configuration steps are required? Each correct answer presents part of the solution.

Accepted Answer

Assign appropriate permissions for the AI service managed identity to access the Key Vault key.. Option A is correct because when using customer-managed keys (CMK) with Azure AI services, the AI service's managed identity must be granted access to the Key Vault key via an access policy or RBAC role (e.g., Key Vault Crypto Service Encryption User) so the service can wrap/unwrap the data encryption key. Option C is correct because you must provision an Azure Key Vault to hold the CMK, and enabling soft-delete and purge protection is mandatory for CMK scenarios to prevent accidental or malicious key deletion that would render encrypted data unrecoverable. Option D is correct because the CMK must be bound to the Azure AI services account by setting the customer-managed key identifier (the Key Vault key URI) in the account's encryption properties, which is what actually switches encryption from Microsoft-managed keys to your organization's keys. Option B is wrong because storing private keys in plain text in source code is a severe security anti-pattern and is never a valid configuration step. Option E is wrong because disabling diagnostic logging has no bearing on encryption key management and would actually undermine compliance and auditing requirements.

Answer

Store the private key as plain text in the application source code.

Answer

Disable all diagnostic logging across all Azure subscriptions.

Question 8

You are designing a secure, production-grade architecture for an Azure AI Search service integrated with Azure OpenAI. Which THREE security and operational best practices should you implement? Each correct answer presents part of the solution.

Accepted Answer

Configure Azure Monitor diagnostic settings to stream query performance and error logs to a Log Analytics workspace.. Option B is correct because Azure Monitor diagnostic settings let you route Azure AI Search query and operation logs, plus metrics, into a Log Analytics workspace, enabling KQL-based alerting, auditing, and performance troubleshooting required for production operations. Option C is correct because managed identities (system- or user-assigned) allow Azure AI Search to authenticate to Azure OpenAI and other resources via Microsoft Entra ID tokens, eliminating hardcoded API keys and enabling RBAC-scoped, credential-rotation-free access. Option E is correct because Private Endpoints create Azure Private Link connections that assign private IP addresses from your VNet to both services, so index/query traffic and Azure OpenAI calls traverse the Microsoft backbone rather than the public internet, and public network access can be disabled. Option A is wrong because publishing admin API keys in public repositories exposes full control-plane access to the search service and is a severe credential leak. Option D is wrong because the Free tier caps storage, index count, and throughput (no SLA, limited partitions/replicas), so it cannot support high-throughput production workloads.

Answer

Publish administrative API keys publicly in GitHub repositories for easy developer access.

Answer

Use the Free pricing tier for high-throughput production workloads.

Question 9

You are managing cost allocation for multiple Azure AI services across different departments. Which TWO Azure tools or features can help you analyze and attribute these costs accurately? Each correct answer presents part of the solution.

Accepted Answer

Azure resource tags combined with Cost Management cost analysis grouping. Option D is correct because Azure resource tags let you attach department or cost-center metadata to each Azure AI service resource, and Cost Management cost analysis can then group and filter costs by those tag keys, directly attributing spend to the right department. Option E is correct because Azure Cost Management budgets track spend against thresholds and its exported CSV cost reports provide the raw, granular usage and charge data needed for accurate offline analysis and chargeback. Option A is wrong because Azure Bastion session logs record RDP/SSH connectivity activity, not billing or cost data. Option B is wrong because Entra ID (Azure AD) enterprise group memberships govern identity and access, not cost attribution. Option C is wrong because Microsoft Defender for Cloud security recommendations address security posture, not cost analysis or allocation.

Answer

Azure Bastion session logs

Answer

Azure Active Directory enterprise user group memberships

Answer

Microsoft Defender for Cloud security recommendations

Question 10

You are troubleshooting a deployment failure where an ARM template fails to provision an Azure OpenAI account. Which TWO potential causes should you investigate? Each correct answer presents part of the solution.

Accepted Answer

The subscription has exceeded its regional quota or model deployment quota limit.. Option A is correct because Azure OpenAI provisioning fails when the target subscription has hit its regional quota or per-model deployment quota limit, so you must verify available quota in the chosen region before redeploying. Option D is correct because Azure OpenAI model deployments are only offered in specific regions, and requesting a region that does not support the model causes the ARM template to fail. Options B, C, and E do not belong: Azure DevOps IP whitelisting, browser cache corruption, and uncommitted local git changes affect pipeline connectivity, client rendering, and source control state respectively, none of which block ARM template provisioning of an Azure OpenAI account.

Answer

The local workstation IP address is not whitelisted in Azure DevOps.

Answer

The client web browser cache is corrupted.

Answer

The local git repository has uncommitted changes.

Question 11

When planning the deployment of an Azure AI Search service, which TWO sizing and capacity parameters must you configure during creation? Each correct answer presents part of the solution.

Accepted Answer

Replicas. During creation of an Azure AI Search service, you must choose the number of replicas (Option B) and partitions (Option C) as part of the pricing tier and capacity configuration. Replicas determine high availability and query throughput by creating multiple copies of the index that also enable read replicas for query load balancing. Partitions determine storage capacity and write throughput by splitting the index across multiple shards, and together replicas and partitions define the service's scaling units (SUs). Options A, D, and E are not Azure AI Search creation parameters: virtual CPU core count per hypervisor, Linux kernel version, and Active Directory domain controller IP are infrastructure or OS-level settings that Azure manages and are not exposed when provisioning an Azure AI Search service.

Answer

Virtual CPU core count per hypervisor

Answer

Linux kernel version

Answer

Active Directory domain controller IP

Question 12

You are conducting a security review of an Azure AI Search service and Azure OpenAI integration. You discover potential security misconfigurations. Which THREE remediation actions should you take to align with Azure security benchmarks? Each correct answer presents part of the solution.

Accepted Answer

Enable diagnostic logging and configure alerts for unauthorized access attempts and HTTP 401/403 errors.. Option B is correct because enabling diagnostic logging and configuring alerts for unauthorized access attempts and HTTP 401/403 errors provides the audit trail and detection capability required by Azure security benchmarks to identify and respond to authentication and authorization failures against Azure AI Search and Azure OpenAI. Option C is correct because disabling public network access once private endpoints are fully implemented enforces network isolation, ensuring traffic reaches the services only through the private endpoint (private link) rather than the public internet, which is a core Azure security benchmark recommendation. Option E is correct because rotating static API keys regularly limits the blast radius of leaked keys, and transitioning clients to Microsoft Entra ID managed identities eliminates long-lived shared secrets entirely by using token-based, identity-driven authentication. Option A does not belong because granting Contributor to anonymous external users violates least privilege and exposes the services to unauthorized modification. Option D does not belong because embedding admin API keys in client-side single-page applications exposes full administrative credentials to anyone inspecting the browser, which is a critical secret-management anti-pattern.

Answer

Grant Contributor permissions to anonymous external users.

Answer

Expose admin API keys in client-side single-page applications for direct access.

Question 13

Your company requires that all Azure AI service endpoints are accessible only from within a specific Azure Virtual Network (VNet) and that public internet access is completely disabled. How should you configure the Azure AI resource networking settings?

Accepted Answer

Disable public network access on the Networking blade and create a Private Endpoint associated with your VNet and subnet.. Disabling public network access and creating a Private Endpoint associated with the VNet and subnet ensures the Azure AI resource is reachable only through private IP addresses inside the virtual network. This fully removes public internet exposure, which matches the requirement.

Answer

Set the routing preference to Microsoft network routing and enable service endpoints on the default subnet.

Answer

Configure the firewall to allow only the corporate public IP address range and keep public network access enabled.

Answer

Deploy an Azure Application Gateway in front of the Azure AI multi-service account endpoint.

Question 14

You are setting up budget alerts for your Azure AI services to prevent unexpected overspending. Which Azure tool should you use to create these alerts and automatically trigger an action group when the budget threshold reaches 90%?

Accepted Answer

Azure Cost Management + Billing budgets. Azure Cost Management + Billing budgets are the native mechanism for defining spending thresholds (e.g., 90% of a monthly budget) and attaching action groups that fire when thresholds are crossed. Budgets support scopes like subscriptions, resource groups, and management groups, and can trigger email, Logic App, or Automation Runbook actions via action groups. This directly satisfies the requirement to alert and act at 90% of budget.

Answer

Azure Monitor alerts

Answer

Microsoft Defender for Cloud pricing settings

Answer

Azure Advisor cost recommendations

Question 15

You need to implement token-based authentication using Azure AD (Microsoft Entra ID) for a Python application consuming an Azure OpenAI resource. Which authorization header format must your application use to authenticate successfully?

Accepted Answer

Authorization: Bearer . Azure OpenAI and most Microsoft Entra ID-protected REST APIs expect OAuth 2.0 bearer tokens in the `Authorization: Bearer ` header. When using Microsoft Entra ID (Azure AD) token-based authentication, the application acquires an access token via MSAL or the Azure Identity SDK and passes it in this standard header format. This is the RFC 6750-compliant way to present a bearer token.

Answer

Authorization: API-Key <api_key_value>

Answer

X-MS-Token: <access_token>

Answer

Ocp-Apim-Subscription-Key: <access_token>