AI-103 Practice Test 14 — 15 Questions

Question 1

You are provisioning an Azure AI services account and want to view your current API key. Where can you retrieve the API key in the Azure portal?

Accepted Answer

Keys and Endpoint blade of the resource. In the Azure portal, the API key and endpoint for an Azure AI services (Cognitive Services) resource are found on the 'Keys and Endpoint' blade of that resource. This blade displays Key1 and Key2, the endpoint URL, and the region, and allows regenerating the keys. It is the standard location for retrieving credentials used by SDKs and REST calls.

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Resource graph explorer

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Azure Advisor cost optimization pane

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Azure Active Directory overview page

Question 2

You are using Prompt Flow to iterate on an agent. What is the benefit of using 'Environment Variables' in your flow?

Accepted Answer

To manage configuration settings like API keys and endpoints across environments.. Environment variables in Prompt Flow let you externalize configuration such as API keys, endpoints, and deployment names so the same flow can run against different environments (dev, test, prod) without code changes. This is the standard twelve-factor app pattern and is the primary benefit of using environment variables in a flow.

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To reduce the model response time.

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To store large data files.

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To increase the prompt length.

Question 3

You are planning the security architecture for an enterprise Azure AI multi-service account. Management requires that access to the resource be strictly controlled and audited. Which TWO security features or configurations should you implement? Each correct answer presents part of the solution.

Accepted Answer

Disable local authentication (API key access) and require Microsoft Entra ID authentication.. Option A is correct because disabling local authentication on the Azure AI multi-service account removes API-key-based access, forcing all callers to authenticate through Microsoft Entra ID (formerly Azure AD), which enables identity-based access control, conditional access, and full audit logging of who accessed the resource. Option C is correct because configuring an Azure Private Endpoint assigns a private IP address from your virtual network to the Azure AI service, so data-plane traffic stays on the Microsoft backbone and the resource can be restricted from public network access, satisfying strict network-level control and auditability. Option B is wrong because storing admin API keys permanently in an unencrypted Blob container exposes credentials and directly contradicts the requirement for strictly controlled, audited access. Option D is wrong because granting the Contributor role to all end-user client applications violates least privilege and gives broad management-plane rights rather than scoped data access. Option E is wrong because enabling anonymous public access across all subnets removes authentication and network restrictions entirely, which is the opposite of the required security posture.

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Store all admin API keys permanently in an unencrypted Azure Blob Storage container.

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Grant the Contributor role to all end-user client applications.

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Enable anonymous public access across all subnets.

Question 4

You are analyzing multi-language documents containing mixed English and Japanese text using Azure AI Vision Read API. How should you specify the language parameter to ensure optimal OCR accuracy?

Accepted Answer

Omit the language parameter or set language=unk to enable automatic language detection across supported languages.. The Read API supports automatic language detection when the language parameter is omitted or set to unk, allowing it to identify and process multiple languages within the same document, including mixed English and Japanese text. This is the recommended approach for multi-language documents because forcing a single language can degrade recognition of the other language's characters.

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Use the legacy OCR v3.0 endpoint with multi-language flag.

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You must submit English and Japanese pages separately in distinct API calls.

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Set language=en-US exclusively.

Question 5

Your organization is deploying an Azure OpenAI resource. Your security policy requires that all network traffic destined for the OpenAI service must traverse a secure hub-and-spoke virtual network topology and pass through an Azure Firewall instance. How should you route outbound traffic from your application subnet to Azure OpenAI?

Accepted Answer

Configure User Defined Routes (UDR) on the application subnet with a next hop pointing to the Azure Firewall.. User Defined Routes (UDR) let you override Azure's default system routes by associating a custom route table with the application subnet. By setting the next hop type to 'Virtual appliance' and pointing to the Azure Firewall's private IP, all egress traffic (including to the Azure OpenAI endpoint) is forced through the firewall for inspection and policy enforcement. This is the standard hub-and-spoke pattern for centralized network security.

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Configure Azure Traffic Manager dns routing.

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Attach an Azure Bastion subnet to the firewall.

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Rely on automatic default system routing without route tables.

Question 6

You are using Azure AI Vision 4.0. You want to obtain a concise, single-sentence human-readable description of an image. Which feature parameter should you request?

Accepted Answer

features=caption. The features=caption parameter in Azure AI Vision 4.0 returns a single, concise, human-readable sentence describing the image — exactly what the question asks for. It is the direct match for 'concise, single-sentence description.'

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features=tags

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features=denseCaptions

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features=read

Question 7

You are configuring an agent that must call a REST API to get current stock prices. Where must you define this integration to ensure the agent can reliably call the tool?

Accepted Answer

In the Agent Tool Definition.. In Azure AI Foundry, tools (including REST API calls, function calling, and OpenAPI-defined tools) are declared in the Agent Tool Definition, which the agent runtime uses to decide when and how to invoke the tool. This is where you register the API schema, authentication, and parameters so the agent can reliably call the stock price endpoint.

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In the Azure OpenAI deployment configuration.

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In the Search index field mapping.

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In the System Prompt.

Question 8

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 9

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 10

You need to ensure that an Azure AI services resource is accessible only from a specific virtual network. Which configuration should you update in the Azure portal?

Accepted Answer

The Networking blade to configure Private endpoints.. To restrict an Azure AI services (Cognitive Services) resource so it is reachable only from a specific virtual network, you configure a Private Endpoint on the resource's Networking blade. A Private Endpoint creates a private IP address from your VNet, mapping the service into your network via Azure Private Link, so public internet access can be disabled entirely. This is the standard portal-based approach for VNet-level isolation of Azure AI resources.

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The Resource tags to add a network security group.

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The Authentication blade to disable local keys.

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The Identity blade to assign a User Assigned Managed Identity.

Question 11

Which TWO monitoring strategies are effective for detecting 'data drift' in a deployed AI model?

Accepted Answer

Tracking changes in input data distribution.. Option A is correct because data drift is fundamentally a shift in the statistical distribution of the model's input features relative to the training data, so tracking changes in input data distribution (e.g., via statistical tests like KL divergence or PSI on feature histograms) directly detects it. Option B is correct because monitoring prediction accuracy against ground truth labels reveals performance degradation that typically results from drift, providing a downstream signal that the input-output relationship has changed. Option C is not relevant since CPU temperature is a hardware health metric and has no bearing on statistical data drift. Option D does not belong because total API request volume only measures traffic load, not changes in data characteristics or model behavior. Option E is incorrect because periodically restarting the endpoint is an operational action, not a monitoring strategy, and does nothing to detect drift.

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Checking the physical temperature of the CPU.

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Monitoring total API request volume.

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Restarting the endpoint every hour.

Question 12

You are implementing an agentic workflow where an agent needs to search a vector database, inspect the retrieved results, and if the results are insufficient, reformulate the query and search again before answering. What agent design pattern are you implementing?

Accepted Answer

Self-Correction / Reflection pattern. The Self-Correction / Reflection pattern is an agentic design where the agent evaluates its own intermediate output (retrieved results), determines whether it is sufficient, and if not, reformulates its approach and retries before producing a final answer. This iterative evaluate-and-retry loop is the defining characteristic of reflection-based agents. It enables the agent to improve answer quality without human intervention.

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Static zero-shot generation pattern

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Direct REST API proxy pattern

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Database indexing batch pattern

Question 13

You need to provision an Azure AI resource that allows developers to access multiple AI capabilities (such as Vision, Speech, and Language) using a single endpoint and a single API key. Which resource type should you create?

Accepted Answer

Azure AI multi-service account. An Azure AI multi-service account (Cognitive Services multi-service resource) bundles Vision, Speech, Language, Translator, and other services under one endpoint and one API key, simplifying provisioning and billing. It is the correct resource type when developers need multiple AI capabilities through a single credential. Single-service resources like Custom Vision or Azure OpenAI expose only their own capability.

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Azure Cognitive Search resource

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Azure OpenAI resource

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Azure AI Custom Vision resource

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.

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Azure Monitor alerts

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Microsoft Defender for Cloud pricing settings

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Azure Advisor cost recommendations

Question 15

When configuring a custom Named Entity Recognition (NER) project in Language Studio using the Azure AI Language service, which THREE steps or configuration options are required during the model lifecycle?

Accepted Answer

Define your entity schema and types. Option A is correct because a custom NER project requires you to define the entity schema — the entity types (labels) and their descriptions — in Language Studio before you can label data, as this schema drives the labeling experience and the model's output classes. Option D is correct because after labeling your documents you must start a training job in Language Studio (or via the REST API/az CLI) that builds the model from your labeled dataset; the service handles the underlying training without you writing training code. Option E is correct because a trained custom NER model is not queryable until it is assigned to a deployment (a named deployment in the Language resource), which the client application then calls via the analyze-text endpoint. Option B is not required because Azure AI Language abstracts the base model and performs fine-tuning internally; you do not write GPU gradient backpropagation code. Option C is not required because Language Studio accepts plain-text/JSON documents in its supported formats and performs tokenization internally, so manual pre-tokenized JSON vectors are unnecessary.

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Fine-tune the underlying base transformer weights via direct GPU gradient backpropagation code

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Manually convert all input text files into pre-tokenized JSON vectors