Microsoft · Free Practice Questions · Last reviewed May 2026
30 real exam-style questions organised by domain, each with the correct answer highlighted and a plain-English explanation of why it's right — and why the others are wrong.
Your development team is building a global customer support portal. Incoming tickets arrive in dozens of different languages. You need to automatically route tickets to the correct regional team based on the language of the ticket text. Which Azure AI Language feature should you call?
Sentiment analysis
Named Entity Recognition (NER)
Key phrase extraction
Language detection
Language detection returns the detected language and ISO code for input text, satisfying the requirement to route tickets by language. Calling it first identifies each ticket's language, letting the portal direct it to the correct regional team without manual triage.
You are calling the Azure AI Language REST API for Named Entity Recognition (NER) and receive an HTTP 429 status code. How should your application handle this error?
Switch the API endpoint to use the Free pricing tier.
Regenerate the subscription key and restart the client service.
Immediately re-submit the request with doubled payload size.
Implement an exponential backoff retry policy and resend the request after a delay.
HTTP 429 signals throttling, meaning the request exceeded the resource's rate limit. Retrying immediately worsens the condition, so exponential backoff spaces retries progressively, letting the quota window recover before resending. This is the prescribed handling for transient rate-limit responses from the Azure AI Language API.
You are processing medical intake forms using Azure AI Language. You need to identify and categorize medical terms, medications, and dosages. Which feature should you use?
Health text analytics
Health text analytics applies a domain-specific model that extracts and categorises medical entities such as medications, dosages, and clinical terms from unstructured text. This directly satisfies the stem's requirement to identify and categorise medical terminology within intake forms, which generic entity extraction cannot reliably achieve.
Key phrase extraction
Standard Named Entity Recognition (NER)
Custom text classification
You need to extract key concepts and main ideas from long-form technical support articles using the Azure AI Language service. Which feature should you invoke?
Personal Personally Identifiable Information (PII) detection
Language detection
Summarization
Key phrase extraction
Key phrase extraction returns the salient terms and concepts from each document, which directly satisfies the requirement to surface key concepts and main ideas from long-form articles. It operates on unstructured text without training, unlike custom classification or summarisation.
You are building an application that analyzes user feedback. You need to detect instances where users share their phone numbers, email addresses, and credit card numbers so that you can automatically redact them. Which feature should you use?
Sentiment analysis
Language detection
Named Entity Recognition (NER)
PII detection
PII detection identifies and returns entities such as phone numbers, email addresses and credit card numbers, with optional redaction masking in the response. This directly satisfies the requirement to detect and automatically redact those specific data types from user feedback.
You are configuring text summarization using the Azure AI Language REST API. You need to generate a summary that consists of a few distinct sentences pulled directly from the source document without altering their wording. Which summarization kind should you specify?
Key phrase extraction
Extractive summarization
Extractive summarization returns sentences copied verbatim from the source, satisfying the requirement to preserve original wording. Abstractive summarization instead generates new phrasing, which would alter the text. Specifying extractive therefore produces the few distinct source sentences the scenario demands.
Abstractive summarization
Custom single-label classification
Want more Text Analysis practice?
Practice this domainYou are provisioning a new Azure AI Vision resource in the Azure Portal. You need to choose between a 'F0' free pricing tier and an 'S0' standard pricing tier. What is a key limitation of selecting the F0 pricing tier?
It does not support regional data residency guarantees.
It disables diagnostic log streaming to Azure Monitor.
It restricts the total number of calls allowed per month and limits transaction rates.
F0 caps both the monthly transaction volume and the per-second call rate, so production workloads exhaust the quota quickly. S0 removes those caps and bills per call, making F0 suitable only for trials and evaluation.
It prohibits the use of managed identities for authentication.
You are deploying an Azure OpenAI resource and want to authenticate client applications using Microsoft Entra ID instead of static API keys. Which role must be assigned to the application's managed identity to permit it to invoke model deployments?
Storage Blob Data Contributor
Key Vault Secrets Officer
Cognitive Services OpenAI User
Cognitive Services OpenAI User grants the managed identity data-plane access to invoke model deployments on the Azure OpenAI resource. Assigning it at the resource scope lets the application authenticate via Microsoft Entra ID tokens, removing the need for static API keys.
Contributor
Your development team needs to deploy an Azure AI Language resource that can be accessed from an on-premises datacenter via an ExpressRoute circuit without traversing the public internet. Which Azure feature should you configure?
Azure Bastion host subnet
Azure Private Endpoint
Azure Private Endpoint assigns a private IP from your VNet to the Azure AI Language resource, so traffic from on-premises over ExpressRoute reaches it via private peering and never traverses the public internet, satisfying the stated no-public-internet constraint.
NAT gateway resource attachment
Azure Front Door with Web Application Firewall
Your company's security policy requires that all Azure AI Services accounts disable public network access completely. However, internal applications hosted inside a specific Azure Virtual Network must still communicate with the AI services. How should you configure network access?
Configure an inbound Network Security Group (NSG) rule on the AI resource subnet.
Disable public network access and configure a Private Endpoint for the resource.
A Private Endpoint assigns a private IP from the virtual network to the Azure AI Services resource, so traffic stays on the Microsoft backbone. Disabling public network access then blocks internet reachability while internal applications retain connectivity, meeting the security policy.
Keep public network access enabled and rely on API keys for security.
Deploy a VPN gateway inside the Azure AI resource group.
Your enterprise organization provisions multiple Azure AI Services accounts across different subscriptions. Security mandates that all API keys must be automatically rotated every 30 days without human intervention. Which Azure service configuration should you implement?
Microsoft Defender for Cloud JIT access policies
Azure Key Vault automated secret rotation using an Azure Function
Azure Key Vault automated rotation uses an Azure Function to regenerate and update secrets on a schedule, eliminating manual intervention. This satisfies the mandate that all API keys rotate every 30 days automatically across the provisioned Azure AI Services accounts.
Azure AI Services built-in automatic key rotation policy
Azure Monitor alert rules tied to Microsoft Entra ID
You are deploying an Azure AI multi-service account in the West US region. The finance department requires that all resource deployment costs be tracked by department code using Azure Resource Manager tags. Which Azure feature should you use to enforce the mandatory application of the department tag across all future Azure AI resource deployments?
Azure Cost Management budgets
Azure role-based access control (RBAC)
Azure Blueprints
Azure Policy
Azure Policy evaluates resource deployments against rules and can deny or auto-remediate non-compliant ones, so a policy with a deny effect and tag requirement forces the department tag onto every future Azure AI resource regardless of who deploys it.
Want more Plan And Manage AN Azure AI Solution practice?
Practice this domainYou are building a customer service agent in Azure AI Foundry. The agent needs to call an external inventory management REST API when customers ask about stock levels. Which feature of Azure AI Agent Service should you implement to allow the agent to interact with this external API?
Function calling (Tools)
Function calling lets the agent invoke declared tools, so you define the inventory REST API as a callable function with parameters. The agent then issues the call and incorporates the response, satisfying the requirement to interact with an external API.
Prompt shields
Azure AI Content Safety filters
Azure Cognitive Search index
You are designing a RAG solution using Azure AI Search and Azure OpenAI. Users report that when querying technical documentation, the system misses highly relevant paragraphs because the exact keywords do not match the user's conversational query. How should you configure Azure AI Search to improve retrieval accuracy?
Switch the index analyzer to a standard whitespace analyzer to prevent stemming issues.
Enable semantic ranking on the Azure AI Search index and use hybrid search combining vector embeddings and keyword BM25.
Semantic ranking re-ranks results using deep-learning language models that capture conversational intent, while hybrid search fuses BM25 keyword scores with vector similarity. Together they retrieve paragraphs whose meaning matches the query even when exact keywords differ, directly resolving the missed-relevance problem.
Increase the number of retrieved documents (top_k) from 3 to 20 without changing the query type.
Convert all documents to uppercase before indexing to ensure case-insensitive matching.
You are developing a multi-agent orchestration solution using Azure AI Agent Service and want to ensure that user inputs are safely evaluated before they are passed to the model. Which component should you configure to inspect and filter incoming prompts for malicious content and restricted topics?
Azure Key Vault access policies
Azure AI Content Safety integrated into the model gateway
Azure AI Content Safety integrated into the model gateway inspects prompts in transit, applying harm categories and restricted-topic filters before the model receives them. This satisfies the stem's requirement to evaluate user inputs safely, blocking malicious content at the gateway layer rather than relying on post-generation moderation or agent-level prompt instructions.
Azure Cognitive Search semantic ranker
Azure API Management rate limiting policy
You are evaluating an agentic workflow in Azure AI Foundry that performs multi-step reasoning. The agent occasionally enters an infinite loop, repeatedly calling the same tool with identical arguments. Which architectural pattern or configuration should you apply to prevent this behavior?
Increase the vector store chunk overlap size
Configure a maximum iteration step limit in the agent orchestration loop
A maximum iteration step limit caps the orchestration loop, terminating execution once the count is reached. This directly satisfies the stem's constraint of preventing infinite loops from repeated identical tool calls, since the agent cannot exceed the configured ceiling regardless of its reasoning path.
Enable Azure API Management caching for the tool endpoint
Set the model frequency penalty to 2.0
You are designing a RAG solution in Azure AI Foundry where sensitive enterprise documents must be retrieved. Users should only see search results corresponding to their security clearance level. How should you implement document-level access control in Azure AI Search?
Store user security groups in document metadata and apply OData filter expressions during search queries
Storing group identifiers in document metadata and filtering with OData expressions lets Azure AI Search return only documents matching the caller's clearance. The filter is applied at query time, enforcing document-level access control within the RAG retrieval step.
Configure Microsoft Entra ID authentication on the Azure OpenAI endpoint
Encrypt the search index using customer-managed keys (CMK) in Azure Key Vault
Use Azure AI Content Safety to redact sensitive keywords before indexing
You are writing a system prompt for an Azure OpenAI model deployed in Azure AI Foundry. You want to ensure the model responds only using the provided context and refuses to answer if the context is insufficient. What technique should you use?
Configure Azure AI Content Safety severity levels to high
Set the model temperature to 1.0 and top_p to 0.0
Enable semantic caching on the Azure OpenAI deployment
Include explicit grounding instructions and a negative constraint in the system prompt
Explicit grounding instructions direct the model to answer solely from supplied context, while a negative constraint tells it to refuse when that context is insufficient. Together they satisfy the requirement to prevent answers drawn from outside knowledge.
Want more Implement Generative AI And Agentic Solutions practice?
Practice this domainYou are integrating Azure AI Vision Read API into a high-throughput workflow. You receive an HTTP 202 Accepted response. What action should your application take next?
Poll the URL specified in the Operation-Location response header until the status is succeeded.
The Read API processes documents asynchronously, so a 202 Accepted means the job is queued rather than finished. The Operation-Location header supplies the polling endpoint; your application must repeatedly query it until the status reports succeeded, satisfying the high-throughput requirement without blocking on a synchronous response.
Switch to synchronous Read API endpoint.
Parse the response body directly for the extracted text results.
Immediately retry the POST request with an increased timeout value.
You have trained a Custom Vision classification model. You need to export the model to run locally on an edge device using ONNX format. Which project domain must you select when creating the project to support export?
General (compact)
The General (compact) domain uses a smaller exported model footprint while retaining classification accuracy, and it is the domain that supports ONNX export for edge deployment. Selecting it satisfies the stem's requirement to run the Custom Vision model locally on an edge device.
Retail
Food (compact) for edge deployment
General
You are developing a .NET application that extracts bounding boxes of text from scanned documents using Azure AI Vision 4.0. Which asynchronous API operation should you call first to initiate the OCR process?
POST /computervision/imageanalysis:analyze?api-version=2024-02-01
The POST imageanalysis:analyze endpoint initiates the OCR request, submitting the document for processing under the specified API version. It returns the operation location for polling, making it the required first call before retrieving extracted bounding-box results asynchronously.
POST /vision/v3.2/ocr
POST /vision/v2.1/recognizeText
POST /customvision/v3.3/Prediction
Your company wants to generate descriptive captions for thousands of product images automatically using Azure AI Vision. Which visual feature parameter should you specify in the analyze request?
features=read
features=smartCrops
features=caption
Specifying features=caption invokes the Caption visual feature, which returns a single-sentence natural-language description of an image via the Azure AI Vision analyse endpoint. This directly satisfies the stem's requirement to generate descriptive captions for thousands of product images automatically, without custom model training.
features=objects
You need to determine the primary dominant colors and whether an image is predominantly black-and-white using Azure AI Vision. Which visual feature parameter provides this information?
features=denseCaptions
features=color
The color visual feature returns dominant foreground and background colours plus an isBwImg boolean indicating whether the image is predominantly black-and-white. Requesting features=color therefore supplies exactly the dominant-colour and monochrome detection the stem requires from Azure AI Vision.
features=people
features=tags
Your application uploads an image to Azure AI Vision for analysis. You need to generate thumbnail images with specific aspect ratios while maintaining the primary region of interest. Which API endpoint or feature should you use?
Use Custom Vision Training API to train a cropping model.
Call the Analyze Image API with features=objects and crop the image locally.
Call the Get Thumbnail API passing width, height, and smartCropping parameters.
The Get Thumbnail API accepts width, height, and smartCropping parameters; smartCropping analyses saliency to keep the primary region of interest inside the requested aspect ratio, satisfying the requirement to generate thumbnails without cropping the subject.
Use Azure AI Document Intelligence layout analysis.
Want more Computer Vision practice?
Practice this domainYou are developing a custom extraction model using Azure AI Document Intelligence Studio. Your training dataset consists of multi-page invoices with varying structures, and some fields appear only on the final page. How should you structure your tagging configuration?
Create a separate custom model for the final page and chain them using an orchestration model.
Split every multi-page invoice into individual single-page documents before uploading.
Define all late-appearing fields as optional metadata parameters in the training configuration JSON file.
Tag the fields on whatever page they appear across the sample documents in Document Intelligence Studio.
Tagging each field on the page where it actually occurs lets the custom neural model learn page-position context, which matters because some fields appear only on the final page of these variably structured invoices. Tagging every field on page one would misrepresent that layout and degrade extraction accuracy.
You are configuring an Azure AI Document Intelligence orchestration model to automatically classify and extract data from incoming customer correspondence, which includes invoices, work orders, and purchase orders. One of the component models is failing to classify documents correctly. How should you resolve this issue?
Modify the orchestration model routing threshold in the Azure portal settings.
Switch the orchestration model from a template-based classifier to a neural classifier using the REST API.
Retrain the failing component custom model with more distinct sample documents that represent the specific document class.
Retraining the failing component custom model directly addresses misclassification by supplying more distinct samples of the specific document class, sharpening the model's learned feature boundaries. Since orchestration routes documents to component models, each classifier's accuracy depends on representative training data; additional distinct samples reduce ambiguity between invoices, work orders and purchase orders.
Update the API version to bypass classification and force direct extraction via the general document model.
Your team needs to extract data from a large volume of standardized tax forms using Azure AI Document Intelligence. The forms have a fixed layout where fields are always in the exact same location. Which model type provides the fastest training and highest accuracy for this specific use case?
Custom template model
A custom template model learns fixed field positions from as few as five labelled samples, giving rapid training and high accuracy when layout is consistent. It suits standardised tax forms where fields never move, unlike custom neural or prebuilt models.
Prebuilt receipt model
Custom neural model
General document model
You are configuring an Azure AI Document Intelligence prebuilt model to extract key-value pairs from standard invoices. Which API operation should your application call to process the document asynchronously?
POST /documentintelligence/documentModels/prebuilt-invoice:train?api-version=2024-02-29-preview
POST /documentintelligence/models/prebuilt-invoice/analyze?api-version=2024-02-29-preview
POST /formrecognizer/v2.1/prebuilt/invoice/analyze
POST /documentintelligence/documentModels/prebuilt-invoice:analyze?api-version=2024-02-29-preview
The `:analyze` operation submits the invoice and returns a 202 with an `Operation-Location` header, letting the application poll for results rather than block. This satisfies the asynchronous processing constraint, unlike synchronous `:analyze` variants or extraction endpoints that return results inline.
You are integrating Azure AI Document Intelligence into a web application. You call the analyzeDocument API and receive a 202 Accepted response. What is the correct pattern for your application to retrieve the final extraction results?
Wait for exactly 60 seconds and then send a GET request to the original analyze endpoint.
Immediately parse the response body of the 202 Accepted response for the extracted JSON data.
Poll the URL provided in the Operation-Location response header until the status returns 'succeeded', then fetch the results.
The 202 response means extraction is asynchronous, so the Operation-Location header carries the polling URL. Repeatedly GET that URL until the status field reads 'succeeded', then retrieve the results from the same endpoint. This satisfies the stem's requirement for the correct retrieval pattern after an accepted submission.
Open a WebSocket connection to the streaming endpoint specified in the WebSocket-Location header.
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?
prebuilt-invoice
prebuilt-idDocument
prebuilt-businessCard
prebuilt-receipt
The prebuilt-receipt model is trained specifically on point-of-sale receipts, returning fields including merchant name, transaction date, line items and total tax. Other prebuilt models target invoices, identity documents or business cards, so they lack the receipt-specific field schema the scenario demands.
Want more Information Extraction practice?
Practice this domainThe vendor does not publish an exact question count for the AI-103; the exam lasts 100 minutes. The passing score is 700 (scaled 1–1,000). Always confirm current details on the vendor's official exam page.
Scenario-based questions covering exam objectives with detailed answer explanations.
The exam covers 5 domains: Text Analysis, Plan And Manage AN Azure AI Solution, Implement Generative AI And Agentic Solutions, Computer Vision, Information Extraction. Questions are weighted by domain — higher-weight domains appear more on your actual exam.
No. These are original exam-style practice questions written against the official Microsoft AI-103 exam objectives. They are not copied from the real exam. Courseiva focuses on genuine understanding, not memorisation of braindumps.
Courseiva tracks your accuracy per domain and routes you toward weak areas automatically. Free, no account required.