Microsoft Azure AI Engineer Associate AI-102 AI-102 Questions 976–988 | Page 14/14

976

Multi-Selectmedium

Which TWO Azure AI services can be used to build a multilingual question-answering bot that retrieves answers from a knowledge base of documents?

Select 2 answers

A.Azure AI Language Understanding (LUIS)

B.Azure OpenAI Service with a RAG pattern

C.Azure AI Translator

D.Azure AI Document Intelligence

E.Azure AI Language - Custom Question Answering

AnswersB, E

Azure OpenAI with RAG can retrieve answers from documents using embeddings.

Why this answer

Options A and D are correct. Custom question answering (A) is designed for FAQ/knowledge bases. Azure OpenAI (D) can be used with a retrieval augmented generation (RAG) pattern.

Option B is wrong because LUIS is for intent/entity recognition, not answer retrieval. Option C is wrong because Translator is for translation, not answering. Option E is wrong because Form Recognizer is for form extraction.

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977

MCQhard

You are designing a generative AI solution that uses Azure OpenAI Service. The solution must generate code snippets in Python and JavaScript. You need to ensure the model reliably outputs code in the correct language based on user input. Which approach should you use?

A.Set the top_p parameter to a low value.

B.Use a system message to specify the desired language.

C.Fine-tune the model on a dataset of code in both languages.

D.Set the temperature to 0 to make the model deterministic.

AnswerB

System messages can instruct the model on the language to use.

Why this answer

Option C is correct because the system message can specify the language and instruct the model to output code in that language. Option A is wrong as temperature controls creativity. Option B is wrong as fine-tuning is overkill.

Option D is wrong as top_p controls diversity.

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978

MCQhard

You are implementing a knowledge mining solution using Azure Cognitive Search with built-in AI enrichment. The pipeline must extract named entities and key phrases from documents. The enrichment pipeline should be triggered only for documents that are larger than 1 MB. Which approach should you use?

A.Configure the skillset to run entity recognition and key phrase extraction on all documents.

B.Implement a custom skill that checks the document size and only calls the built-in skills if size > 1 MB.

C.Use indexer parameters to specify a document size filter.

D.Create two indexers: one for large documents with skills, one for small documents without.

AnswerB

A custom skill can conditionally run built-in skills based on document properties.

Why this answer

Option D is correct because a custom skill can evaluate document size and conditionally call built-in skills. Option A is wrong because skills run on all documents regardless of size. Option B is wrong because indexer parameters cannot conditionally skip skills based on document content.

Option C is wrong because skillset execution order cannot be changed by document size.

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979

MCQmedium

You are deploying a generative AI application. You have the JSON configuration above. You need to ensure that the model stops generating output at the end of each sentence. The current configuration uses a stop sequence of "\n". What should you do?

A.Change the stop sequence to [".", "!" , "?"]

B.Reduce the maxTokens to 500.

C.Set the topP to 0.5.

D.Set the temperature to 0.

AnswerA

These are sentence-ending punctuation marks.

Why this answer

Option A is correct because the goal is to stop generation at the end of each sentence. A sentence can end with a period (.), exclamation mark (!), or question mark (?). The current stop sequence of "\n" only stops at newline characters, which does not guarantee sentence boundaries.

By changing the stop sequence to an array containing [".", "!", "?"], the model will halt output whenever it generates any of these punctuation marks, ensuring each sentence is complete.

Exam trap

The trap here is that candidates often confuse parameters that control output randomness (temperature, topP) or length (maxTokens) with parameters that define explicit stopping conditions, leading them to select options that do not actually enforce sentence boundaries.

How to eliminate wrong answers

Option B is wrong because reducing maxTokens to 500 limits the total number of tokens generated but does not control where generation stops relative to sentence boundaries; the model could still cut off mid-sentence. Option C is wrong because setting topP to 0.5 adjusts the nucleus sampling threshold, affecting token selection diversity, not the stopping condition; it does not enforce sentence-ending punctuation. Option D is wrong because setting temperature to 0 makes the model deterministic (greedy decoding) but does not introduce a stop condition based on sentence-ending characters; the model will still generate until maxTokens or another stop sequence is reached.

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980

MCQmedium

You are building a chatbot that must handle customer inquiries about order status. The solution must use Azure AI Language and support multiple languages. You need to configure the project to detect language automatically from user input. Which setting should you enable?

A.Set the primary language to English only.

B.Use the conversation summarization feature.

C.Enable multi-lingual detection in the project language settings.

D.Enable spelling correction in the project settings.

AnswerC

Allows the project to automatically detect the language of each utterance.

Why this answer

Option B is correct because enabling multi-lingual detection in the language settings allows the project to automatically detect the input language. Option A is wrong because enabling only the primary language will not detect others. Option C is wrong because that setting is for spelling correction, not language detection.

Option D is wrong because that setting is for conversation summarization.

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981

Multi-Selectmedium

Which TWO Azure AI services can be used to extract text from images and PDFs? (Select two.)

Select 2 answers

A.Azure AI Translator

B.Azure AI Search

C.Azure AI Vision OCR

D.Azure AI Document Intelligence

E.Azure AI Language

AnswersC, D

OCR extracts text from images and PDFs.

Why this answer

Options A and D are correct. Azure AI Vision OCR (Read API) extracts printed and handwritten text from images and PDFs. Azure AI Document Intelligence (formerly Form Recognizer) extracts text from documents and also performs layout analysis.

Option B is wrong because Azure AI Language focuses on text analysis, not image processing. Option C is wrong because Azure AI Search is for indexing and search, not text extraction from images. Option E is wrong because Azure AI Translator is for translation, not text extraction from images.

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982

MCQeasy

A company is deploying an Azure AI solution that uses Azure Cognitive Services. The solution must comply with data residency requirements that mandate all customer data be stored within a specific geographic region. Which action should the company take when creating the Cognitive Services resource?

A.Apply a resource tag that specifies the region.

B.Configure the endpoint URL to point to a regional endpoint.

C.Set the SKU to a tier that supports regional restrictions.

D.Select the appropriate region during resource creation.

AnswerD

The resource location determines where data is stored at rest.

Why this answer

Data residency requirements are satisfied by physically storing customer data within a specific geographic boundary. When creating an Azure Cognitive Services resource, selecting the appropriate region (e.g., 'West Europe' or 'East US') during the provisioning process ensures that all data processed and stored by that service instance remains within that Azure datacenter region. This is the fundamental and only guaranteed method to enforce data residency at the resource level.

Exam trap

The trap here is that candidates confuse network-level controls (like endpoint configuration or tagging) with physical data storage guarantees, mistakenly believing that a regional endpoint or a tag can enforce data residency when only the initial region selection during resource creation can do so.

How to eliminate wrong answers

Option A is wrong because resource tags are metadata labels used for organization, cost tracking, or policy enforcement; they do not influence where the underlying service stores data. Option B is wrong because the endpoint URL is automatically generated based on the chosen region and cannot be manually configured to redirect storage; it only determines the network access point, not the physical data location. Option C is wrong because the SKU tier (e.g., S0, F0) determines throughput limits and feature availability, not geographic restrictions; no SKU tier enforces regional data storage.

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983

MCQmedium

Your organization uses Microsoft Purview to govern AI models. You need to ensure that data used for training complies with internal policies. What should you configure?

A.Set up Data Estate Insights to monitor data usage

B.Use Microsoft Purview Data Map to catalog training data

C.Apply sensitivity labels to training data

D.Enable Microsoft Purview Information Protection

AnswerA

Provides governance and compliance monitoring.

Why this answer

Data Estate Insights in Microsoft Purview provides monitoring and analytics over data estate health, including data usage patterns. By configuring Data Estate Insights, you can track how training data is accessed and used, enabling you to verify compliance with internal policies on data consumption for AI model training.

Exam trap

The trap here is confusing data cataloging (Data Map) or classification (sensitivity labels) with usage monitoring, leading candidates to select options that address data discovery or protection rather than compliance monitoring.

How to eliminate wrong answers

Option B is wrong because Microsoft Purview Data Map is used for metadata scanning, classification, and lineage tracking of data assets, not for monitoring data usage compliance. Option C is wrong because applying sensitivity labels classifies data based on sensitivity but does not monitor or enforce usage policies for training data. Option D is wrong because Microsoft Purview Information Protection focuses on data protection through encryption and access controls, not on monitoring data usage for compliance.

Full explanation →

984

MCQmedium

You have an Azure AI solution that uses Azure AI Language to perform sentiment analysis. The solution is experiencing high latency. Which action should you take to reduce latency?

A.Move the service to a different Azure region.

B.Use the Free tier of the Azure AI Language service.

C.Increase the request timeout value.

D.Scale the service by increasing the number of instances or using a higher pricing tier.

AnswerD

More capacity reduces queuing and latency.

Why this answer

Option D is correct because scaling the Azure AI Language service by increasing the number of instances or moving to a higher pricing tier (e.g., from Standard S0 to a tier with higher throughput) directly addresses high latency by providing more capacity to handle concurrent requests. High latency often results from hitting the service's rate limits or throughput constraints, and scaling alleviates this bottleneck without changing the underlying architecture.

Exam trap

The trap here is that candidates often confuse network latency (solved by region proximity) with service throughput latency (solved by scaling), leading them to incorrectly choose Option A when the real bottleneck is capacity, not geography.

How to eliminate wrong answers

Option A is wrong because moving the service to a different Azure region primarily reduces network latency due to geographic proximity, but it does not resolve high latency caused by insufficient service capacity or throttling; the core issue is throughput, not distance. Option B is wrong because the Free tier has strict rate limits (e.g., 5,000 transactions per month) and lower throughput, which would likely worsen latency under load rather than reduce it. Option C is wrong because increasing the request timeout value does not reduce latency; it only allows the client to wait longer for a response, masking the symptom without addressing the underlying performance issue.

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985

MCQhard

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?

A.Parallel agent pattern

B.Sequential agent pattern

C.Sub-agent pattern

D.Custom agent pattern

AnswerB

Sequential pattern ensures ordered task execution with clear handoffs and traceability.

Why this answer

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.

Exam trap

The trap here is that candidates often confuse the sequential pattern with the parallel pattern, assuming that 'sharing context' implies concurrent execution, but the question's emphasis on 'reliable orchestration and traceability' explicitly requires ordered, non-concurrent task handoff.

How to eliminate wrong answers

Option A is wrong because the parallel agent pattern executes agents concurrently, which does not guarantee ordered task passing or shared context across agents in a sequential dependency. Option C is wrong because the sub-agent pattern is used for delegating subtasks to a child agent from a parent, not for peer-to-peer context sharing and task handoff among multiple agents. Option D is wrong because the custom agent pattern is a generic term for user-defined agent logic, not a specific orchestration pattern that ensures reliable sequential task passing and traceability.

Full explanation →

986

MCQeasy

You need to analyze the sentiment of social media posts in real time using Azure AI Language. Which approach should you use?

A.Call the sentiment analysis REST API for each post

B.Use Azure AI Search with cognitive skills

C.Use the batch processing feature in Azure AI Language

D.Send posts to an Event Hub and use Stream Analytics

AnswerA

The API provides real-time analysis.

Why this answer

Azure AI Language supports real-time sentiment analysis via its REST API. Batch processing is for offline analysis; event hubs and stream analytics are overkill for simple API calls.

Full explanation →

987

MCQmedium

You have the above Azure AI Search skillset. The indexer fails with the error 'The skill 'sentiment-skill' cannot find the input '/document/pages/*' because the path does not exist.' What is the most likely cause?

A.The SplitSkill did not split the content because the document content is too short.

B.The SplitSkill is not defined in the skillset.

C.The SentimentSkill is missing a required input.

D.The targetName in SplitSkill is misspelled.

AnswerA

If content is too short, no pages are produced.

Why this answer

The SplitSkill outputs 'pages' but the SentimentSkill input expects '/document/pages/*'. However, the SplitSkill's targetName 'pages' is correct, but note that the SplitSkill context is '/document', so the output path is '/document/pages'. The input source uses '/document/pages/*', which is correct.

The error suggests the SplitSkill did not produce any pages. Option B is correct: the SplitSkill might not have split the content, possibly because the text length is too short. Option A is incorrect because the skill is defined.

Option C is incorrect because targetName is correct. Option D is incorrect because the error is about missing path, not missing skill.

Full explanation →

988

MCQhard

You are a senior AI engineer at a financial services company. You are building a generative AI solution to assist financial advisors with client portfolio recommendations. The solution must use Azure OpenAI Service. The following requirements must be met: 1. Responses must be based on the latest market data and client profiles stored in Azure SQL Database. 2. The solution must not generate investment advice that is not backed by the data. 3. The solution must be cost-effective and minimize API calls. 4. The system must provide citations for the data used in the response. You design a RAG pattern with Azure AI Search indexing the portfolio data. You also implement a system message instructing the model to only use provided context. However, the model occasionally generates advice that contradicts the data or invents new facts. You need to modify the solution to ensure responses are strictly grounded in the retrieved data. What should you do?

A.Set temperature to 0 in the Azure OpenAI completion request.

B.Reduce the top_k parameter in the search query to retrieve fewer documents.

C.Increase the chunk size in the index to provide more context per document.

D.Use Azure OpenAI Service on your own data integration to directly query the SQL database.

AnswerA

Temperature 0 makes output deterministic and grounded in provided context.

Why this answer

Option B is correct because setting temperature to 0 makes the model deterministic and less likely to invent. Option A is wrong because lowering top_k still allows creativity. Option C is wrong because adjusting chunk size affects retrieval but not the model's adherence.

Option D is wrong because integrating with the data doesn't prevent hallucination if temperature is high.

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Microsoft Azure AI Engineer Associate AI-102 (AI-102) — Questions 976–988