AI-102 Practice Test 12 — 15 Questions

Question 1

A company uses Azure OpenAI to generate product descriptions. They want to ensure that the descriptions are consistent in style and tone. Which strategy should they use?

Accepted Answer

Provide a few examples of desired style in the prompt (few-shot learning).. Few-shot learning (option B) is the correct strategy because it directly controls style and tone by providing examples of desired output within the prompt. This leverages the model's in-context learning ability without modifying the underlying model weights, making it ideal for enforcing consistency without the cost and complexity of fine-tuning.

Answer

Fine-tune the model on a dataset of product descriptions.

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Set max_tokens to a small value to limit output length.

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Increase the temperature to 1.0 for more creativity.

Question 2

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?

Accepted Answer

The SplitSkill did not split the content because the document content is too short.. The error indicates that the input path '/document/pages/*' does not exist. This path is generated by the SplitSkill when it splits the document content into pages. If the SplitSkill does not produce any output, the path will not exist. The most likely cause is that the document content is too short to be split, so no pages are created. Therefore, option A is correct. Option B is incorrect because the SplitSkill is defined. Option C is incorrect because the SentimentSkill's input path is correct, but the source is missing. Option D is incorrect because the error is about a missing path, not a misspelled targetName.

Answer

The SplitSkill is not defined in the skillset.

Answer

The SentimentSkill is missing a required input.

Answer

The targetName in SplitSkill is misspelled.

Question 3

You are testing a Conversational Language Understanding application. You send the JSON request shown in the exhibit. What is the purpose of this request?

Accepted Answer

Analyze the utterance for intent and entities.. The JSON request sends a user utterance to a Conversational Language Understanding (CLU) endpoint, which is designed to analyze natural language input. The response will include the predicted intent (e.g., 'GetWeather') and extracted entities (e.g., 'location: Seattle'), fulfilling the core function of CLU: intent and entity recognition. This is not a generative or translation task; it is a classification and extraction operation.

Answer

Translate the text to another language.

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Generate a response to the user.

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Summarize the conversation.

Question 4

A company plans to deploy a Copilot Studio agent to Microsoft Teams. The agent should be available to all employees in the company. The security team requires that only authenticated users from the company's Microsoft Entra ID tenant can access the agent. Which channel configuration should be used?

Accepted Answer

Publish the agent to the Teams channel and configure authentication to require Microsoft Entra ID with the company's tenant ID.. Publishing the Copilot Studio agent to the Teams channel and configuring authentication to require Microsoft Entra ID with the company's tenant ID ensures that only authenticated users from that specific tenant can access the agent. This meets the security requirement by restricting access to the company's Entra ID tenant, while the Teams channel provides native integration for all employees.

Answer

Publish the agent to the Direct Line channel and embed it in a Teams tab.

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Publish the agent to the Web channel and share the link in Teams.

Answer

Publish the agent to the Teams channel and turn off authentication.

Question 5

Which TWO Azure AI services provide capabilities to detect and analyze faces in images? (Choose two.)

Accepted Answer

Azure AI Face API. Azure AI Face API (B) is correct because it is specifically designed for face detection, recognition, and analysis, including attributes like age, emotion, and landmarks. Azure AI Vision Image Analysis (D) is correct because it includes the 'Detect Faces' capability within its broader image analysis feature set, allowing extraction of face bounding boxes and attributes without requiring a dedicated Face API resource.

Answer

Azure AI Custom Vision

Answer

Azure AI Document Intelligence

Answer

Azure Video Indexer

Question 6

You are building a solution to generate product descriptions using Azure OpenAI Service. You need to ensure that the output adheres to a specific tone (professional, friendly) and length (50-100 words). Which parameter should you adjust?

Accepted Answer

Set the system message with instructions about tone and length.. The system message in Azure OpenAI Service is specifically designed to set the overall behavior and context for the model, including tone and length constraints. By providing instructions like 'Respond in a professional and friendly tone, and keep the output between 50 and 100 words,' the model will adhere to these guidelines throughout the conversation. This is the primary mechanism for controlling qualitative aspects of the output, as opposed to parameters that control randomness or token limits.

Answer

Configure max_tokens to limit response length.

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Modify the top_p parameter.

Answer

Adjust the temperature parameter.

Question 7

A development team is using the Azure Cognitive Service for Language to perform sentiment analysis on social media posts. They notice that the returned sentiment scores are often neutral for posts that are clearly positive or negative. What is the most likely reason?

Accepted Answer

The posts are too short, causing the sentiment detection to default to neutral.. The Azure Cognitive Service for Language sentiment analysis API has a minimum text length requirement for reliable scoring. When input text is very short (e.g., a few words or a single sentence), the model lacks sufficient context to confidently assign a positive or negative score, so it defaults to a neutral score (often around 0.5). This is a documented behavior of the API, not a limitation of social media language support.

Answer

The service does not support sentiment analysis for social media language.

Answer

The service is not configured to detect mixed sentiment.

Answer

The posts are in a language that is not supported by the sentiment analysis API.

Question 8

Which TWO Azure services can be used to perform optical character recognition (OCR) on images?

Accepted Answer

Azure Computer Vision Read API. Azure Computer Vision Read API is correct because it provides a dedicated OCR capability that extracts printed and handwritten text from images and documents. It uses deep learning models to detect text regions, recognize characters, and return structured output with bounding boxes and confidence scores.

Answer

Azure Face API

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Azure Video Indexer

Answer

Azure Custom Vision

Question 9

You are reviewing an ARM template for deploying Azure OpenAI Service. The template includes a deployment for gpt-35-turbo with a capacity of 100. You need to ensure that the deployment uses provisioned throughput instead of standard. What should you modify?

Accepted Answer

Change the sku name to 'ProvisionedManaged'.. To use provisioned throughput (PTU) with Azure OpenAI Service, you must set the SKU name to 'ProvisionedManaged' in the ARM template. The default SKU is 'Standard', which uses pay-per-token consumption. Changing the SKU name to 'ProvisionedManaged' tells the resource provider to allocate dedicated throughput capacity for the deployment, ensuring consistent latency and throughput regardless of other workloads.

Answer

Remove the raiPolicyName property.

Answer

Increase the capacity to 200.

Answer

Change the model format to 'GPT-4'.

Question 10

You need to extract handwritten text from scanned forms. Which Azure Computer Vision feature should you use?

Accepted Answer

Read API. The Read API is specifically designed for extracting printed and handwritten text from images and documents, including scanned forms. It uses advanced deep-learning models optimized for text recognition and is the correct service for this task in Azure Computer Vision.

Answer

OCR API (optical character recognition)

Answer

Tag API

Answer

Describe API

Question 11

Which THREE components are required to deploy a bot using Azure AI Language's conversational language understanding (CLU) and Azure Bot Service?

Accepted Answer

An Azure Bot Service bot with the CLU model as a skill.. Azure Bot Service can integrate a CLU model as a skill, enabling the bot to route user intents to the CLU service for natural language understanding. This is done by configuring the bot's skill endpoint to point to the CLU project's prediction URL, allowing the bot to leverage the trained model for intent classification and entity extraction.

Answer

A QnA Maker resource for FAQs.

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A Translator resource for multilingual support.

Question 12

Your company uses Azure OpenAI Service to generate product descriptions. You need to reduce costs while maintaining response quality for common requests. What should you implement?

Accepted Answer

Enable semantic caching to reuse responses for similar prompts.. Semantic caching stores responses for semantically similar prompts, allowing Azure OpenAI Service to return cached results for common requests without incurring per-token inference costs. This directly reduces costs while preserving response quality, as the cached responses are identical to what the model would generate. Unlike other options, it avoids degrading output quality or requiring architectural changes.

Answer

Use a smaller model variant (e.g., GPT-3.5 instead of GPT-4).

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Batch multiple requests into a single API call.

Answer

Reduce the max_tokens parameter for all requests.

Question 13

You are using Azure OpenAI Service to generate marketing copy. You have a requirement to reduce the cost of inference without significantly impacting output quality. Which parameter should you adjust?

Accepted Answer

Decrease the max_tokens.. Decreasing max_tokens directly reduces the number of tokens generated per API call, which lowers the compute cost because Azure OpenAI charges per token (both input and output). Since the requirement is to reduce inference cost without significantly impacting output quality, reducing max_tokens is the most direct and effective parameter. It caps the response length, preventing unnecessarily verbose output while preserving the model's ability to generate high-quality, concise copy.

Answer

Increase the frequency_penalty.

Answer

Decrease the temperature.

Answer

Increase the top_p.

Question 14

You are building a multilingual chatbot using Azure AI Language. For a given user utterance, you need to first detect the language, then route to the appropriate language-specific intent model. Which combination of Azure AI Language features should you use?

Accepted Answer

Language Detection and Conversational Language Understanding. The scenario requires first detecting the language of the user utterance (using Language Detection) and then routing to a language-specific intent model (using Conversational Language Understanding, which supports multiple languages in separate projects or deployments). This combination directly fulfills the requirement of language-aware intent routing.

Answer

Key Phrase Extraction and Conversational Language Understanding

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Translator and Conversational Language Understanding

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Language Detection and Custom Text Classification

Question 15

You need to implement a solution that searches through a collection of scanned invoices and extracts invoice numbers, dates, and total amounts. The solution must run on a schedule without manual intervention. Which Azure service should you use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is specifically designed to extract structured data like invoice numbers, dates, and total amounts from scanned documents. It can be scheduled to run automatically via APIs or pipelines. Azure Bot Service is for conversational AI, not document extraction. Azure AI Search with built-in skills can index and search documents but is not optimized for extracting specific fields from invoices. Azure AI Foundry model catalog is for discovering and deploying AI models, not for direct document extraction.

Answer

Azure Bot Service

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Azure AI Search with built-in skills

Answer

Azure AI Foundry model catalog