AI-102 Hard Questions — 25 Difficult Questions

Question 1

You are building a multilingual chatbot using Azure AI Language. The chatbot must handle English, Spanish, and French. You need to configure the LUIS (Language Understanding) model to support multiple languages efficiently. What is the best practice?

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

Accepted Answer

Create a separate LUIS app for each language and use the same intents and entities structure.. LUIS does not support a single multilingual model; each LUIS app is designed for a single primary language. To handle multiple languages efficiently, you must create a separate LUIS app per language, each with the same intent and entity schema, and route user queries to the appropriate app based on the detected language. This ensures optimal language-specific model training and accuracy.

Answer

Use the prebuilt multilingual LUIS model and fine-tune it with your intents.

Answer

Create a single LUIS app and add utterances in all three languages.

Answer

Create one LUIS app with language set to 'Multilingual' and add utterances in all languages.

Question 2

You are a data scientist for Contoso Pharmaceuticals. The company has thousands of research documents in PDF format stored in Azure Blob Storage. You need to build an Azure Cognitive Search solution that enables researchers to search for documents based on chemical compound names, disease mentions, and experimental results. The solution must extract these entities using a custom AI model built in Azure AI Language. Additionally, the solution must support semantic search for natural language queries. The search index must be updated daily with new documents. You have an existing Azure AI Language custom entity extraction model that recognizes chemical compounds and diseases. The model is deployed as an endpoint. You need to configure the enrichment pipeline. What should you do?

Accepted Answer

Create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP.. To integrate a custom AI model from Azure AI Language into an Azure Cognitive Search enrichment pipeline, you need to create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP. The built-in Entity Recognition skill only supports prebuilt models and cannot be configured to use a custom model endpoint. Deploying the model to Azure AI Document Intelligence and using a Document Intelligence skill is not appropriate because the model is already deployed in Azure AI Language as a custom entity extraction model. Field mappings in the indexer are for direct field-to-field mappings from the data source to the index, not for calling external AI services. Therefore, Option A is the correct approach.

Answer

Deploy the custom model to Azure AI Document Intelligence and use a Document Intelligence skill.

Answer

Add the custom entity extraction as a field mapping in the indexer.

Answer

Use the built-in Entity Recognition skill and configure it to use your custom model endpoint.

Question 3

A company uses Azure AI Search to index a large collection of scanned invoices stored in Azure Blob Storage. They have a skillset that includes an OCR skill to extract text from the invoices. The indexer is configured to run every night. They notice that the indexer takes a long time to complete and sometimes times out. They want to optimize the indexer performance without reducing the quality of the extracted text. What should they do?

Accepted Answer

Enable incremental enrichment and cache the enriched data to avoid reprocessing unchanged documents.. Enabling incremental enrichment and caching is the best approach to optimize indexer performance for recurring runs. It avoids reprocessing documents that have not changed, thus reducing the workload on the OCR skill and other enrichments. This maintains the quality of extracted text because only new or modified documents are processed, while unchanged documents reuse cached enrichments. Other options either reduce quality or do not address the performance bottleneck.

Answer

Switch from the OCR skill to the Text Merge skill to combine text from multiple pages.

Answer

Increase the indexer's batch size and reduce the number of parallel indexers.

Answer

Reduce the OCR skill's image resolution by setting the 'imageAction' to 'none' and relying on the document's embedded text.

Question 4

You are reviewing a skillset definition for an Azure AI Search indexer. The indexer is configured to index 1000 PDF documents. After running the indexer, you notice that only 500 documents have sentiment scores. What is the most likely cause?

Accepted Answer

The SplitSkill outputs are not correctly mapped to the SentimentSkill input; the skill runs only on the first page of each document. The SplitSkill divides each PDF into pages, and by default the SentimentSkill runs in the context of each page (e.g., /document/pages/*). If the SplitSkill outputs are not correctly mapped to the SentimentSkill input, the sentiment skill may only process the first page or fail to receive the split content, resulting in sentiment scores for only a subset of documents. Correct mapping ensures that each page's content is passed to the SentimentSkill for analysis.

Answer

The skills are defined in the wrong order; sentiment should run before split

Answer

The SentimentSkill is not supported in this region

Answer

The context of the SentimentSkill should be "/document" instead of "/document/pages/*"

Question 5

You are designing a multilingual chatbot using Azure AI Language. The chatbot must support English, Spanish, and French. You need to minimize development effort and ensure consistent intent recognition across languages. What should you do?

Accepted Answer

Use a single CLU multilingual project that supports all three languages.. A single CLU multilingual project in Azure AI Language natively supports multiple languages within one project, allowing you to define intents and entities once and provide utterances in English, Spanish, and French. The model learns shared semantic representations across languages, so intent recognition remains consistent without building separate models or translation pipelines. This directly minimizes development effort because you maintain one project, one deployment, and one set of intent definitions.

Answer

Use the Azure AI Translator to translate all utterances to English before sending to a single English-only CLU project.

Answer

Create a separate CLU project for each language and combine them with a routing mechanism.

Answer

Use the Language Understanding (LUIS) service with a single app that includes language-specific utterances.

Question 6

You manage an Azure AI Search service that indexes legal documents. The search latency is high, and you need to improve query performance without reducing index size. Which action should you take?

Accepted Answer

Increase the number of replicas. Increasing the number of replicas distributes query load across multiple copies of the index, which directly improves query throughput and reduces latency. Replicas are designed for scaling query operations without changing the index size or storage capacity.

Answer

Upgrade to a higher pricing tier

Answer

Increase the number of partitions

Answer

Reduce the number of searchable fields

Question 7

You are deploying an Azure AI Search solution for a global e-commerce platform. The index must support real-time updates from a product catalog stored in Azure Cosmos DB, with a maximum indexing latency of 10 seconds. The solution must also handle 5000 queries per second (QPS) during peak hours. What should you configure?

Accepted Answer

Use Standard S3 tier with 12 replicas and enable incremental indexing.. The Standard S3 tier supports high query volumes (up to 5000 QPS) with 12 replicas, and incremental indexing enables near-real-time updates from Cosmos DB by processing only changed documents, keeping latency under 10 seconds. The combination of sufficient replicas for throughput and incremental indexing for low-latency indexing meets both requirements.

Answer

Use the Storage Optimized L2 tier with 6 partitions and 4 replicas.

Answer

Use the Free tier and enable change tracking on Cosmos DB.

Answer

Use Standard S2 tier with 3 replicas and indexer scheduling every 5 minutes.

Question 8

You are designing a solution that uses Azure AI Document Intelligence to extract data from invoices. The invoices are in various formats (PDF, TIFF, and JPEG) and languages. You need to ensure high accuracy for extraction. Which approach should you take?

Accepted Answer

Use a custom extraction model trained on a sample set of invoices covering all formats and languages.. Azure AI Document Intelligence’s custom extraction models are trained on your specific invoice samples, enabling the model to learn the unique layouts, fields, and languages present across PDF, TIFF, and JPEG formats. This approach directly addresses the need for high accuracy on varied formats and languages, as the model adapts to the exact patterns in your data rather than relying on a generic prebuilt model or brittle post-processing.

Answer

Use the prebuilt invoice model and configure language detection.

Answer

Use Azure AI Vision OCR to extract all text and then parse using regular expressions.

Answer

Convert all invoices to a single format (e.g., PDF) before processing.

Question 9

Which THREE considerations are essential when designing a cost management strategy for an enterprise Azure AI solution that uses multiple AI services, including Azure OpenAI Service, Azure AI Language, and Azure AI Vision?

Accepted Answer

Choose provisioned throughput units (PTUs) for Azure OpenAI Service for predictable workloads.. Option B is correct because provisioned throughput units (PTUs) in Azure OpenAI Service reserve dedicated model capacity with a fixed hourly/monthly cost, which is the recommended approach for predictable, steady-state workloads and avoids the variable per-token charges of pay-as-you-go. Option C is correct because a single multi-service Cognitive Services (Azure AI Services) resource lets you access Azure OpenAI, Azure AI Language, and Azure AI Vision under one account and one billing key, simplifying cost tracking, consolidating quotas, and reducing management overhead across services. Option E is correct because enabling autoscaling on Azure AI Language lets capacity scale with demand so you pay only for the throughput you need during peak periods while scaling down during idle times, directly optimizing consumption-based cost. Option A is not appropriate because Free tiers have strict request/rate limits and are unsuitable for enterprise production workloads, and they do not support the scale or SLAs required. Option D is not a cost management consideration: Azure Site Recovery is a business continuity/disaster recovery service that replicates workloads across regions and adds cost rather than optimizing it.

Answer

Use the Free tier for all services to minimize upfront costs.

Answer

Deploy Azure Site Recovery to replicate AI services across regions.

Question 10

You deploy the ARM template shown in the exhibit. After deployment, you need to allow access to the Language service from your on-premises application. What should you do?

Accepted Answer

Add an IP rule with your on-premises public IP address.. The ARM template deploys an Azure Cognitive Services Language service with a network ACL that defaults to denying all traffic (defaultAction: Deny). To allow your on-premises application to access the service, you must add an IP rule that permits traffic from your on-premises public IP address. This is because the network ACL evaluates IP rules before the default action, and adding a rule with your public IP overrides the default deny for that specific source.

Answer

Remove the customSubDomainName property.

Answer

Set the defaultAction to Allow.

Answer

Change the SKU to F0 to allow public access.

Question 11

Refer to the exhibit. You are testing a conversational language understanding (CLU) application in Azure AI Language. You send the JSON request to the prediction endpoint and receive a 200 response but with an empty topIntent. What is the most likely cause?

Accepted Answer

The conversationItem is missing a required 'role' field.. B is correct because the JSON request for a conversational language understanding (CLU) application requires a 'role' field within the 'conversationItem' object when using the conversation analysis endpoint. Without this field, the service cannot properly interpret the utterance's context (e.g., user vs. system role), leading to a successful HTTP 200 response but an empty 'topIntent' as the model fails to classify the intent.

Answer

The projectName or deploymentName is incorrect.

Answer

The language parameter is not supported.

Answer

The utterance text is too short.

Question 12

Refer to the exhibit. You deployed a Conversational Language Understanding (CLU) project. A user says 'hi, where is my order?' The model returns a single intent 'Greeting' with confidence 0.85. You need the model to detect both intents. What should you change?

Accepted Answer

Set 'multipleIntents' to true.. The Conversational Language Understanding (CLU) service supports multi-intent detection only when the 'multipleIntents' flag is explicitly set to true in the project settings. By default, CLU returns only the highest-confidence intent; enabling this flag allows the model to return all intents whose confidence exceeds the threshold, enabling detection of both 'Greeting' and 'OrderStatus' from a single utterance.

Answer

Lower the confidenceThreshold to 0.5.

Answer

Add more utterances to the 'OrderStatus' intent.

Answer

Change the project kind to 'Orchestration'.

Question 13

You are designing a responsible AI solution for a healthcare application that uses Azure AI Vision to analyze medical images. The solution must minimize bias across demographic groups and provide explainability for predictions. Which combination of services should you use?

Accepted Answer

Azure AI Content Safety for content moderation and Fairlearn with Azure Machine Learning interpretability.. Azure AI Content Safety helps filter harmful content and reduce bias in model outputs, while Fairlearn with Azure Machine Learning interpretability provides tools to assess fairness across demographic groups and explain model predictions. This combination directly addresses the requirements of minimizing bias and providing explainability for medical image analysis.

Answer

Azure AI Document Intelligence for extracting metadata and Azure AI Language for sentiment analysis.

Answer

Azure AI Translator for multilingual support and Azure AI Metrics Advisor for monitoring.

Answer

Azure AI Search for indexing results and Azure AI Anomaly Detector for outliers.

Question 14

You are deploying an Azure AI multi-agent solution on Microsoft Foundry. The solution uses three agents that must share context and pass tasks among themselves. To ensure reliable orchestration and traceability, which agent pattern should you implement?

Accepted Answer

Sequential agent pattern. The sequential agent pattern is correct because the scenario requires agents to share context and pass tasks among themselves in a reliable, traceable manner. In Microsoft Foundry, the sequential pattern ensures each agent executes in a defined order, passing state and results to the next agent, which provides clear orchestration and full traceability of the workflow.

Answer

Parallel agent pattern

Answer

Sub-agent pattern

Answer

Custom agent pattern

Question 15

Your company has an existing Azure AI Search index used for e-commerce product search. The index is populated by an indexer that runs every hour. Recently, the product team added a new field 'promotionPrice' that should be searchable. You need to update the index to include this field without reindexing all documents. The field should be of type Edm.Double, searchable, filterable, and sortable. What should you do?

Accepted Answer

Update the index schema to add the new field using the portal or REST API. Then update the indexer to map the field.. Azure AI Search allows you to add new fields to an existing index schema without reindexing all documents, as long as the new field is nullable or has a default value. After updating the index schema via the portal or REST API, you must update the indexer's field mappings to map the new 'promotionPrice' field from the data source to the index. The indexer will then populate the field for existing documents on its next run, and new documents will include the field automatically.

Answer

Add the new field to the data source mapping in the indexer. The indexer will automatically update the index schema.

Answer

Change the indexer schedule to run every 5 minutes to pickup the new field faster.

Answer

Delete the index and recreate it with the new field. Then run the indexer to repopulate all documents.

Question 16

Which THREE components are required to build a custom chat application using Azure OpenAI Service that can answer questions based on your own private data?

Accepted Answer

The 'Add your data' feature configured in Azure OpenAI Studio.. The 'Add your data' feature in Azure OpenAI Studio provides a no-code interface to connect your private data sources (e.g., Azure Blob Storage, local files) to an Azure OpenAI chat model. It automatically chunks the data, creates an Azure AI Search index, and configures the retrieval-augmented generation (RAG) pipeline, enabling the model to answer questions grounded in your proprietary content without fine-tuning.

Answer

A fine-tuned custom model.

Answer

An Azure OpenAI embeddings model deployment.

Question 17

A healthcare organization uses Azure AI Health Insights to analyze patient data. They must ensure that PHI is not logged by the AI services. Which configuration should you apply?

Accepted Answer

Enable diagnostic settings to stream logs to a Log Analytics workspace with a data masking rule. Azure AI Health Insights processes protected health information (PHI), and enabling diagnostic settings to stream logs to a Log Analytics workspace with a data masking rule allows you to redact sensitive PHI from logs before they are stored. Data masking rules in Log Analytics can be configured to hide patterns like patient IDs or names, ensuring compliance with HIPAA while still retaining operational logs.

Answer

Disable all logging for the Azure AI resource

Answer

Configure the resource to use a system-assigned managed identity

Answer

Use Azure Policy to restrict logging to metadata only

Question 18

Refer to the exhibit. The indexer using this skillset fails with an error that the skill cannot be executed. The Azure Function is deployed and responds correctly when tested directly. What is the most likely cause?

Accepted Answer

The skill endpoint is using HTTP instead of HTTPS.. Azure AI Search indexers that invoke custom skills via Azure Functions require the endpoint to use HTTPS. If the endpoint is configured with HTTP, the indexer will fail with a 'skill cannot be executed' error, even if the function itself works when tested directly. This is because the indexer enforces secure communication to protect data in transit.

Answer

The context is set incorrectly to '/document'.

Answer

The input source '/document/content' is invalid.

Answer

The skill's output targetName does not match the index field.

Question 19

You are designing a solution that uses Azure AI Vision to analyze images for moderation. The solution must detect adult content and identify text in images. You need to minimize latency and cost. Which approach should you recommend?

Accepted Answer

Call the Analyze Image API with the 'adult' and 'read' visual features. The Analyze Image API in Azure AI Vision supports multiple visual features in a single call, including 'adult' for adult content detection and 'read' for OCR (text extraction). This minimizes latency by avoiding multiple API calls and reduces cost since you are billed per API call, not per feature.

Answer

Call the Analyze Image API twice: once for adult content and once for OCR

Answer

Use the Computer Vision 3.2 API with the 'adult' and 'OCR' parameters

Answer

Use the Read API for text and the Content Moderator API for adult content

Question 20

Your Azure AI Search index contains millions of documents. Users report that search results are slow for complex queries. You need to improve query performance without reducing result quality. Which action should you take?

Accepted Answer

Increase the number of replicas. Increasing the number of replicas in Azure AI Search distributes query load across multiple copies of the index, enabling parallel processing of complex queries. This directly improves query throughput and latency without altering the index schema or reducing result quality, as replicas provide dedicated resources for query execution.

Answer

Reduce the maximum number of results returned per query

Answer

Remove all facet fields from the index

Answer

Disable complex query types such as fuzzy and regex