AI-103 Practice Test 21 — 15 Questions

Question 1

You are provisioning a multi-service Azure AI resource using an Azure Resource Manager (ARM) template. Which resource type should you specify in the template for a generic multi-service cognitive services account?

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

Accepted Answer

Microsoft.CognitiveServices/accounts. In Azure Resource Manager templates, a multi-service Azure AI (Cognitive Services) account is declared with the resource type 'Microsoft.CognitiveServices/accounts'. This resource type provisions a single account that exposes multiple AI service endpoints (such as Vision, Language, and Speech) under one key and endpoint, which is exactly what a generic multi-service resource requires.

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Microsoft.AI/cognitiveServices

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Microsoft.CognitiveServices/projects

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Microsoft.MachineLearningServices/workspaces

Question 2

Your organization wants to ensure that all Azure AI resources created in any subscription across the tenant adhere to a standardized naming convention (e.g., prefixing with 'ai-'). Which Azure governance tool should you use?

Accepted Answer

Azure Policy with custom naming convention rules. Azure Policy is the governance service designed to enforce organizational standards such as naming conventions across subscriptions and resources. A custom policy definition using a naming pattern rule (e.g., enforcing an 'ai-' prefix) can be assigned at the management group or subscription scope to audit or deny non-compliant resources tenant-wide.

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Microsoft Defender for Cloud

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Azure Cost Management budgets

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Azure Advisor

Question 3

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.

Answer

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

Answer

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

Question 4

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 5

You have an Azure AI endpoint exposed to the internet. You want to ensure traffic is inspected for malicious patterns. What is the recommended service?

Accepted Answer

Azure Web Application Firewall (WAF).. Azure Web Application Firewall (WAF) inspects HTTP/HTTPS traffic at Layer 7 and applies OWASP rule sets to block SQL injection, cross-site scripting, and other malicious patterns before they reach the AI endpoint. It is the recommended service for inspecting internet-facing web traffic for attacks. NSGs operate at Layers 3/4 and cannot see payload content, so they cannot detect application-layer attacks.

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Network Security Group (NSG) rules.

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Azure Bastion.

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Azure AI Content Safety.

Question 6

Which THREE security principles should you follow when managing Azure AI resources?

Accepted Answer

Auditability and logging.. Option A (Auditability and logging) is correct because managing Azure AI resources requires enabling diagnostic settings, Azure Monitor logs, and activity logs so that access to keys, models, and data can be traced and anomalous use detected. Option C (Defense in Depth) is correct because Azure AI workloads should be protected with layered controls such as network isolation (private endpoints, VNet integration), identity controls (Microsoft Entra ID, managed identities), encryption (Microsoft-managed or customer-managed keys), and monitoring, so no single failure exposes the resource. Option E (Principle of Least Privilege) is correct because you should grant only the minimum RBAC roles and scopes needed (for example, Cognitive Services User rather than Contributor/Owner) and use managed identities instead of embedding keys, limiting blast radius if credentials are compromised. Option B is wrong because maximum access for all administrators violates least privilege and expands the attack surface. Option D is wrong because disabling security features to improve speed removes protections like encryption, authentication, and logging, which is never an acceptable trade-off for Azure AI resources.

Answer

Maximum access for all administrators.

Answer

Disable all security features to improve speed.

Question 7

When setting up an Azure AI service, you choose the 'Standard' tier. What is the primary benefit of this compared to the 'Free' tier?

Accepted Answer

Higher rate limits and production-level SLAs.. The Standard tier of Azure AI services provides higher rate limits (e.g., transactions per second) and production-level SLAs (e.g., 99.9% availability) compared to the Free tier, which has strict rate limits and no SLA. The Standard tier is intended for production workloads, while Free is for evaluation and small-scale testing.

Answer

Lower latency for all requests.

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Access to experimental AI models.

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Automatic data encryption.

Question 8

What is the purpose of 'Batch Inference' in Azure AI Foundry?

Accepted Answer

To run multiple prompts against a large dataset for evaluation.. Batch Inference in Azure AI Foundry is designed to process a large dataset of prompts asynchronously against a deployed model, producing outputs for evaluation, scoring, or bulk generation. It is the mechanism used to run evaluation datasets through a model to compute metrics like groundedness, relevance, and coherence at scale. This makes it the correct answer for running multiple prompts against a large dataset for evaluation.

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To create a backup of the agent.

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To test the agent in real-time.

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To debug individual chat turns.

Question 9

You are configuring custom named entity recognition in Language Studio. Which TWO types of entity components can you configure when defining custom entities? (Choose two.)

Accepted Answer

Regex component. In Azure AI Language custom NER, when you define an entity you can add a Regex component (option B), which matches entity instances using a regular expression pattern, and a Learned component (option D), which uses labeled training examples so the model learns to recognize the entity from context. These two component types are the ones supported in the entity definition workflow in Language Studio, and they can be combined within a single entity. The other options are unrelated to custom NER entity components: quantum computation (A), neural speech synthesis (C), and optical character recognition (E) are not configurable entity component types in Language Studio.

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Quantum computation component

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Neural speech synthesis component

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Optical character recognition component

Question 10

Your application processes images submitted by users. You want to ensure that images containing inappropriate or explicit content are rejected before further processing. Which Azure AI Vision feature should you evaluate first?

Accepted Answer

Analyze Image with features=adult. The Analyze Image API with features=adult returns adult and racy content scores (0-1) plus gory and suggestive flags, which is exactly what you need to reject inappropriate images. This is the built-in content moderation capability of Azure AI Vision. Custom Vision and OCR do not perform content moderation, and Azure AI Language Content Moderation is a text-based service, not an image service.

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Custom Vision Object Detection

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Image Analysis Read feature

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Azure AI Language Content Moderation

Question 11

You are configuring a Microsoft Foundry project to deploy a multimodal model. You need to ensure that the model deployment scales automatically based on request volume while minimizing management overhead. Which deployment type should you select?

Accepted Answer

Standard deployment. A Standard deployment in Microsoft Foundry (Azure AI Foundry) for a multimodal model provides automatic scaling based on request volume while minimizing management overhead. It is a pay-as-you-go deployment type that dynamically allocates compute resources to handle traffic, making it ideal for variable workloads without the need for manual provisioning or capacity planning.

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Local container deployment

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Provisioned Throughput deployment

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Batch deployment

Question 12

You are using Language Studio to build a custom named entity recognition (custom NER) project. You have tagged your training data and successfully trained your first model. You want to test the model interactively without writing code. Which tool in Language Studio should you use?

Accepted Answer

Test model pane. In Language Studio, after training a custom NER model, you can test it interactively using the 'Test model' pane. This pane allows you to input text and see the model's predictions without writing any code. It is specifically designed for quick validation and debugging.

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Deployment settings

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Data labeling page

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Model performance tab

Question 13

You are troubleshooting a performance issue where requests to an Azure OpenAI model deployment experience intermittent HTTP 429 errors. You examine Azure Monitor metrics and see that the 'Processed Tokens' metric is well below your provisioned limit, but the 'Requests' metric spikes. What is the root cause of the error?

Accepted Answer

The request rate (Transactions Per Second) has exceeded the provisioned limit, even though token volume is within bounds.. HTTP 429 (Too Many Requests) from Azure OpenAI is triggered when the request rate — measured in Transactions Per Second (TPS) or Requests Per Minute (RPM) — exceeds the deployment's provisioned throughput, independent of token consumption. Since the Processed Tokens metric is below the limit but Requests is spiking, the rate limit on call count is the cause.

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The model weights have become corrupted and require redeployment.

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The Azure subscription has run out of monthly financial budget.

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The Private Endpoint DNS resolution has failed.

Question 14

You are deploying an Azure AI service resource in the Azure portal. Which option specifies the geographic location where your data will be processed and stored?

Accepted Answer

Region. In Azure, the region specifies the geographic location where your resource's data is processed and stored. When you create an Azure AI service resource, you select a region (e.g., East US, West Europe), and that choice determines data residency, latency, and which features are available. This is a core compliance and architecture decision.

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Tenant

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Domain

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Zone

Question 15

Which prebuilt Azure AI Document Intelligence model should you use to extract line items, merchant name, transaction date, and total tax from point-of-sale purchase receipts?

Accepted Answer

prebuilt-receipt. The prebuilt-receipt model is designed to extract key fields from sales receipts, including line items, merchant name, transaction date, and total tax. It is specifically trained on receipt layouts and is the correct choice for point-of-sale purchase receipts.

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prebuilt-invoice

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prebuilt-idDocument

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prebuilt-businessCard