AI-102 Practice Exam 53 — 20 Questions

Question 1

A support team wants to build a bot that answers employee questions by using a set of internal HR policy documents. The team does not want to author question-and-answer pairs manually and needs the bot to return the most relevant passage from the documents, with the source cited. The documents are in English and are updated frequently in a blob container. Which Azure AI Language feature should the team use?

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

Accepted Answer

Custom question answering with a project that imports the documents and enables the option to extract answers from the source content. Custom question answering supports importing documents and automatically generating question-and-answer pairs, plus extracting answers directly from source content. It returns the best matching passage with a citation, and it can refresh from a blob container as documents change, meeting the no-manual-authoring and source-citation requirements.

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Named Entity Recognition with a custom model trained on HR policies

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Conversational language understanding with an intent for each HR topic

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Custom text classification with a single-label project

Question 2

You are building an Azure AI solution that uses Azure OpenAI Service to generate text. You need to ensure that the solution can handle up to 10,000 requests per minute. You also need to monitor the usage and set up alerts when the request rate exceeds 80% of the quota. What should you do?

Accepted Answer

Create a deployment in Azure OpenAI Service with a high tokens-per-minute (TPM) quota and configure Azure Monitor alerts on the Azure OpenAI resource metrics.. To handle high request rates, you must ensure the Azure OpenAI deployment has a sufficient tokens-per-minute quota. You can request a quota increase and deploy a model with adequate TPM. Azure Monitor can track metrics like total calls and token usage, and you can create alert rules to notify when usage exceeds a threshold. The other options do not directly address quota management and monitoring of the service.

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Configure autoscaling for the Azure OpenAI Service in the Azure portal and set up alerts using Azure Service Health.

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Deploy multiple Azure OpenAI resources in different regions and use Azure Front Door to load-balance requests.

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Use Azure API Management to throttle requests to the Azure OpenAI Service and configure Application Insights for monitoring.

Question 3

You are designing an Azure AI Search solution that indexes documents from an Azure SQL Database. The documents include a field named 'content' that contains HTML markup. You need to strip the HTML tags and extract only the plain text before applying further enrichment. Which built-in skill should you use?

Accepted Answer

HTML Strip skill. The HTML Strip skill is specifically designed to remove HTML markup and return plain text. It is the correct choice for cleaning HTML content before applying other enrichment skills. The Text Merger and Text Split skills manipulate text structure but do not remove HTML. Language Detection analyzes language but does not alter the text content.

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Text Merger skill

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Text Split skill

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Language Detection skill

Question 4

You are deploying a conversational language understanding project in Azure AI Language for a banking chatbot. Testing shows the model frequently confuses the intents TransferFunds and PayBill because both utterances contain similar wording about moving money. You need to improve the model's ability to distinguish these two intents. What should you do?

Accepted Answer

Add more labeled utterances that are representative of each intent, including boundary examples that clarify the difference between them. When two intents overlap in wording, the model needs more and better-labeled examples that highlight their distinguishing characteristics. Adding representative and boundary utterances for both intents gives the training algorithm the discriminative signal required to separate them. Adjusting confidence thresholds, merging intents, or adding entities that appear in both classes does not address the underlying classification boundary.

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Increase the model's confidence threshold so low-confidence predictions are rejected instead of misclassified

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Merge the two intents into a single intent and let the bot ask a follow-up question to determine the action

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Add a prebuilt entity such as Money to both intents so the model can use amounts to tell them apart

Question 5

You are building an Azure AI Search enrichment pipeline that enriches documents with key phrases detected by Azure AI Language. You need the key phrases to be returned as a structured collection that can be mapped to a Collection(Edm.String) field in the search index, and you must avoid storing the enriched text in the enrichment cache. Which output mapping configuration should you use in the indexer?

Accepted Answer

Define an outputFieldMapping with a source of /document/keyPhrases and a targetField of keyPhrases.. The Key Phrases skill writes its output to the enrichment tree at /document/keyPhrases as an array of strings. To persist that array into a Collection(Edm.String) field, the indexer needs an outputFieldMapping whose source is that exact path and whose target is the index field. Cache behavior is configured separately on the indexer and is not part of output mapping.

Answer

Define an outputFieldMapping with a source of /document/pages/*/keyPhrases and a targetField of keyPhrases.

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Define an outputFieldMapping with a source of /document/keyPhrases and a targetField of keyPhrases, and set the indexer cache to disabled.

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Define an outputFieldMapping with a source of /document/keyPhrases and a targetField of keyPhrases, and mark the field as retrievable and filterable.

Question 6

You are building a customer feedback dashboard with Azure AI Language. Analysts need to see, for each document, the overall sentiment and the sentiment expressed toward specific aspects such as shipping speed and product quality within the same request. The documents are short English reviews. Which feature and configuration should you use?

Accepted Answer

Call the sentiment analysis operation with opinion mining enabled and read the target and assessment pairs from the response.. Aspect-based sentiment analysis, delivered through opinion mining on the sentiment analysis operation, returns the document sentiment plus target and assessment pairs that pair each aspect with its expressed opinion. That single call supplies both the overall sentiment and the per-aspect sentiment the dashboard requires, without needing separate models or manual inference.

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Call the custom text classification operation with a multi-label project that has one label per aspect.

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Call the key phrase extraction operation and infer aspect sentiment from the returned phrases.

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Call the sentiment analysis operation with opinion mining enabled and specify the aspect targets in the request.

Question 7

You are deploying an Azure AI solution that uses multiple Azure AI services resources. You need to ensure that all resources are deployed in a consistent manner and can be managed as a single unit. You also need to be able to assign permissions to the entire group of resources at once. What should you use?

Accepted Answer

Resource group. A resource group is the fundamental logical container for Azure resources. By placing all Azure AI services resources in one resource group, you can deploy them together using a template and assign RBAC permissions at the resource group scope, which cascades to all contained resources. This provides both consistent management and centralized access control without granting overly broad permissions.

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Management group

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Azure Resource Manager template

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Azure subscription

Question 8

You are building an application that uses the Azure OpenAI Assistants API. The assistant must maintain conversational context across multiple user turns and use a code interpreter to analyze uploaded CSV files. Which two actions should you perform? (Choose two.)

Accepted Answer

Create a thread and add user messages to it, then create a run that references the assistant and the thread.. The Assistants API stores multi-turn context in threads, so messages are appended to a thread and runs execute against the assistant and thread. Code interpreter must be enabled as a tool, and CSV files must be uploaded and attached so the tool can read them during the run. Together these actions satisfy both the context and analysis requirements.

Answer

Create a new assistant for each user turn to avoid context conflicts.

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Set the 'stream' parameter to true on every run to preserve conversation state between turns.

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Use the completions endpoint with a manually maintained message array instead of the Assistants API.

Question 9

You are planning a solution that uses Azure AI Document Intelligence to process invoices. The solution must be deployed to a production environment with high availability. You need to use an Azure Resource Manager (ARM) template to deploy the Document Intelligence resource. The template must ensure that the resource is deployed to two Azure regions. What should you do?

Accepted Answer

Create two separate Document Intelligence resources in the ARM template, each in a different region, and use a Traffic Manager profile to distribute requests.. To achieve high availability across regions for Azure AI Document Intelligence, you must deploy multiple resources, each in a different region. An ARM template can define both resources, and you can use Azure Traffic Manager to route requests. This ensures that if one region fails, the other can handle the load. The other options either do not provide cross-region redundancy or rely on non-existent features.

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Use the copy element in the ARM template to deploy multiple instances of the Document Intelligence resource with the same region but different names.

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Deploy a single Document Intelligence resource and configure geo-replication through the Azure portal after deployment.

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Deploy the ARM template with a single Document Intelligence resource and set the location property to a variable that contains multiple regions.

Question 10

You are building an Azure AI Search knowledge mining solution over a repository of scanned product manuals. You need to extract structured entities such as product names and part numbers from the OCR text and store them in an index field. (Choose two.)

Accepted Answer

Define an outputFieldMapping that writes the entity recognition output to an index field.. Extracting structured entities from OCR text requires a skill that performs entity recognition, and the results must be persisted through an output field mapping. The entity recognition skill with configured categories identifies the relevant named entities, while the field mapping writes those values into the index so they can be queried. Skills that only detect language or key phrases do not produce the required structured output.

Answer

Add Microsoft.Skills.Text.LanguageDetectionSkill to the skillset.

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Add Microsoft.Skills.Text.KeyPhraseExtractionSkill to the skillset.

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Configure the indexer to use a JSON parsing mode.

Question 11

You are planning to deploy an Azure AI Language resource that will be used by multiple applications. The applications must authenticate using Azure Active Directory (Azure AD) tokens. You need to assign the appropriate role to the applications' managed identities so they can call the Azure AI Language service. Which role should you assign?

Accepted Answer

Cognitive Services User. For applications to call Azure AI Language using Microsoft Entra ID authentication, they need a role that grants data-plane access. The Cognitive Services User role provides read and write access to the service's data plane without granting management permissions. This aligns with least privilege and is the correct choice for managed identities that only need to invoke the API.

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Reader

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Azure AI Developer

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Cognitive Services Contributor

Question 12

You are planning to deploy a custom neural voice (CNV) model using Azure AI Speech. You need to ensure that the deployment meets Microsoft's responsible AI requirements. Which two actions should you take? (Choose two.)

Accepted Answer

Submit an application to Microsoft for access to custom neural voice and wait for approval.. To deploy a custom neural voice model responsibly, you must obtain explicit consent from the voice talent and submit an application to Microsoft for access. These are mandatory steps in the gated access process. Using standard voices or training without consent does not meet the requirements. Deploying to a public endpoint without restrictions is also against policy.

Answer

Deploy the model to a public endpoint without any access restrictions.

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Train the model using data from publicly available audio sources without consent.

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Use a standard voice model instead of a custom neural voice to avoid the approval process.

Question 13

You are configuring an Azure OpenAI deployment for a generative AI solution that summarizes long legal contracts. Users report that summaries sometimes omit clauses near the end of documents. The documents are up to 120 pages. You need to improve completeness without changing the model. What should you do?

Accepted Answer

Split each document into overlapping chunks, summarize each chunk, and then combine the summaries in a final aggregation step.. Long documents can exceed the model context window, causing later sections to be dropped. Chunking with overlap preserves boundary content, and summarizing chunks before aggregating covers the whole document. Output length, temperature, and streaming do not affect how much input text the model can attend to, so they cannot fix missing clauses.

Answer

Increase the max_tokens parameter of the completion request to its maximum value.

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Enable streaming responses so the client receives partial summaries as they are generated.

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Raise the temperature so the model explores more of the document content.

Question 14

A developer is building a mobile app that uses Azure AI Vision to generate a descriptive caption for user-uploaded photos. The app must return a human-readable sentence describing the main content of each image. Which Image Analysis feature should the developer use?

Accepted Answer

Caption. The Caption feature of Image Analysis is purpose-built to generate a one-sentence description of an image's main content. Tags, Objects, and Read serve different purposes: tags provide keywords, objects give bounding boxes, and Read extracts text. Only Caption produces the natural language sentence needed for the mobile app.

Answer

Tags

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Objects

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Read

Question 15

You are deploying an Azure AI solution that calls Azure OpenAI and Azure AI Language from an Azure Container Apps environment. The solution must authenticate to both services without using service keys, and the container app must be able to access the services even if the network is restricted to private endpoints. What should you configure?

Accepted Answer

Enable a system-assigned managed identity on the container app and assign the Cognitive Services User role to the identity on each Azure AI resource.. Identity-based authentication with a managed identity and the Cognitive Services User role allows the container app to call Azure OpenAI and Azure AI Language without any keys. Because the identity is recognized by Microsoft Entra ID, the calls are authorized even when the services are reachable only through private endpoints. The other options either use keys or assign roles that do not grant data-plane access.

Answer

Create a service principal, store its client secret in the container app environment variables, and assign the Contributor role on each Azure AI resource.

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Use the Azure AI services multi-service account key retrieved from Azure Key Vault and pass it in the Ocp-Apim-Subscription-Key header.

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Configure the container app to use an Azure Front Door origin with a private link and enable token-based authentication by using a shared access signature.

Question 16

You are developing an Azure AI Search solution that indexes scanned PDF invoices. The indexer must extract text from the PDFs and also recognize entities such as organization names and dates. You want to use built-in cognitive skills to minimize custom code. Which combination of skills should you include in the skillset?

Accepted Answer

OCR skill and Entity Recognition skill. The OCR skill extracts text from scanned PDFs, and the Entity Recognition skill identifies organizations and dates from the extracted text. Using these built-in skills avoids custom code and satisfies both requirements efficiently. Other combinations either lack OCR or entity recognition, or provide unrelated functionality.

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Key Phrase Extraction skill and Language Detection skill

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Text Merge skill and Text Split skill

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Image Analysis skill and Sentiment skill

Question 17

You are deploying an Azure AI solution that uses Azure OpenAI Service. The solution must ensure that all API calls are logged for auditing and that the logs are retained for 90 days. You need to configure diagnostic settings. What should you do?

Accepted Answer

Enable diagnostic settings on the Azure OpenAI resource and send logs to a Log Analytics workspace with a 90-day retention policy.. To log Azure OpenAI API calls for auditing, you must enable diagnostic settings on the Azure OpenAI resource. These logs can be sent to a Log Analytics workspace, where you can configure a 90-day retention period. Other options do not provide resource-level API logging. Application Insights is for application telemetry, Azure Policy enforces configurations, and Defender for AI is for security alerts.

Answer

Enable Azure Defender for AI and configure it to export logs to a SIEM with 90-day retention.

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Configure Azure Monitor Application Insights to capture all API calls and set the retention to 90 days.

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Use Azure Policy to enforce that all API calls are logged to an Azure Storage account with a 90-day lifecycle policy.

Question 18

You are building a generative AI assistant on Azure OpenAI Service that must invoke backend business functions, such as checking order status and issuing refunds, in response to natural-language requests. You want the model to decide when a function is needed and to supply structured arguments, while your application retains control over execution. Which two actions should you take? (Choose two.)

Accepted Answer

Define the available functions and their parameters in the tools parameter of the chat completions request.. Function calling works in two phases: you declare callable operations and their parameter schemas in the request, and the model returns a structured call when a function is appropriate. Your application then executes that function and returns the output so the model can finish the response. Both declaring the tools and executing plus returning results are required for a working implementation.

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Fine-tune the base model on historical order and refund conversations so it learns to call the correct endpoints.

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Grant the Azure OpenAI resource a managed identity with permission to call the backend APIs directly on behalf of the model.

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Set the temperature parameter to 0 and increase the max_tokens value to guarantee deterministic function selection.

Question 19

You are developing a solution that uses Azure AI Language to perform sentiment analysis on multilingual product reviews. The reviews are in English, German, and Japanese. You need to ensure that the sentiment score is accurate for each language. What should you do?

Accepted Answer

Specify the correct language code for each review in the request, such as 'en', 'de', or 'ja'.. Azure AI Language sentiment analysis supports multiple languages, and specifying the correct language code for each review ensures the model uses the appropriate linguistic rules. This yields the most accurate sentiment scores. Auto-detection or forcing a single language can lead to misinterpretation, while translation adds unnecessary complexity and potential loss of sentiment nuance.

Answer

Translate all reviews to English using Azure AI Translator, then perform sentiment analysis with language set to 'en'.

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Set the language parameter to 'en' for all requests to force English sentiment analysis.

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Omit the language parameter and let the service auto-detect the language for each review.

Question 20

You are creating an Azure AI Search indexer that processes documents from Azure Blob Storage. The documents include PDFs and images. You need to extract both text and image content, and then use the image content to generate captions via the Image Analysis skill. Which indexer configuration is required to enable image extraction and passing images to the skillset?

Accepted Answer

Set the imageAction to generateNormalizedImages in the indexer's parameters.. To extract images from documents and make them available for enrichment, you must set the imageAction parameter to generateNormalizedImages in the indexer definition. This extracts images and places them in the enriched document under /document/normalized_images/*. The Image Analysis skill can then use those images as input to generate captions. Other parsing modes or parameters do not enable image extraction for skills.

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Set the parsingMode to json.

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Set the allowSkillsetToReadFileData parameter to true.

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Set the parsingMode to delimitedText and specify a delimiter.