AI-102 Practice Exam 56 — 20 Questions

Question 1

You are using Azure OpenAI Service to generate text. You need to reduce the likelihood of the model generating repetitive sequences. Which TWO parameters should you adjust?

Accepted Answer

frequency_penalty. Frequency penalty reduces the likelihood of the model repeating the same tokens or phrases by subtracting a fixed penalty from the log-probability of tokens that have already appeared in the generated text. This directly discourages repetitive sequences, making it one of the two correct parameters to adjust.

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max_tokens

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top_p

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temperature

Question 2

You need to generate a poem using Azure OpenAI. The poem should be about nature and have a cheerful tone. Which parameter should you adjust to influence the tone?

Accepted Answer

system message. The system message (D) is the correct parameter to influence the tone of a generated poem because it acts as a high-level instruction that sets the behavior, persona, and style of the model. By including a directive like 'You are a cheerful poet writing about nature,' you directly control the tone without altering randomness or output length.

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top_p

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temperature

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max_tokens

Question 3

You need to translate a document from English to Spanish using Azure OpenAI. Which parameter should you include in the prompt to specify the target language?

Accepted Answer

user message. To specify the target language in an Azure OpenAI translation prompt, you include the instruction within the user message (e.g., 'Translate the following English text to Spanish: ...'). The user message is the primary input where the model receives the task and context, making it the correct parameter for language specification.

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max_tokens

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temperature

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system message

Question 4

You have created a Copilot Studio agent that uses a Power Automate flow to send an email when a user requests a password reset. The flow works correctly in test mode, but when the agent is published, the flow does not run. What should you check first?

Accepted Answer

Check if the flow is shared with the Copilot Studio agent.. When a Copilot Studio agent uses a Power Automate flow, the flow must be explicitly shared with the agent (the agent's application registration) so that the agent can trigger the flow on behalf of the user. In test mode, the flow runs under the creator's identity, but in published mode, the agent runs under its own identity, which requires the flow to be shared with the agent's service principal. Option A is correct because this is the most common cause of a flow working in test but failing after publication.

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Check if the user has permission to run the flow.

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Check if the trigger condition is correct.

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Check if the flow is turned on.

Question 5

You are troubleshooting an agentic solution where the agent is not returning responses within acceptable time limits. You suspect the agent is making too many sequential calls to external tools. Which strategy should you recommend to reduce latency?

Accepted Answer

Enable parallel tool execution. Enabling parallel tool execution allows the agent to invoke multiple external tools simultaneously rather than sequentially, directly reducing the total latency caused by serial tool calls. This is a core optimization in agentic frameworks like Semantic Kernel or AutoGen, where tool calls are independent and can be dispatched concurrently.

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Increase the max token limit

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Add more tools to distribute the load

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Reduce the thread history length

Question 6

You are creating an agent in Microsoft Copilot Studio that needs to escalate to a human agent when it cannot resolve a query. Which feature should you use?

Accepted Answer

Add a 'Transfer to agent' topic.. In Microsoft Copilot Studio, the 'Transfer to agent' topic is the correct feature to escalate unresolved queries to a human agent. This topic is specifically designed to hand off the conversation to a live agent, often by triggering a handoff mechanism such as a Dynamics 365 Customer Service queue or a custom integration. It ensures that the bot gracefully transfers context and conversation history, maintaining a seamless user experience.

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Add an 'Escalate' system topic.

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Configure the 'Fallback' topic to call a Power Automate flow.

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Use the 'End conversation' node.

Question 7

You are developing a custom agent using the Microsoft Bot Framework SDK. The agent must handle multiple languages and use the Azure AI Translator service to translate user messages to English before processing. The solution should minimize latency. Where should the translation logic be implemented?

Accepted Answer

As a custom middleware component that intercepts incoming activities and translates them.. Implementing translation logic as a custom middleware component intercepts incoming activities before they reach the bot's main message handler, allowing translation to occur asynchronously and in parallel with other pipeline processing. This minimizes latency because the translation happens early in the request pipeline, and the middleware can be configured to run only when needed (e.g., based on detected language), avoiding unnecessary overhead in the OnMessageActivityAsync method.

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In the OnMessageActivityAsync method after receiving the user's message.

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By using the Bot Framework's built-in language detection feature.

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By sending the user's message to a separate translation endpoint from the client application.

Question 8

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 A) is the correct strategy because it directly addresses the need for consistent style and tone by providing the model with explicit examples of the desired output within the prompt. This guides the model to mimic the given patterns without altering the base model's weights, making it a quick and effective method for controlling output style in Azure OpenAI.

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Set max_tokens to a small value to limit output length.

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

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Increase the temperature to 1.0 for more creativity.

Question 9

You are designing a generative AI solution that uses Azure OpenAI GPT-4 to answer customer support questions. The solution must comply with Microsoft's Responsible AI principles, particularly transparency and accountability. Which implementation approach best meets these requirements?

Accepted Answer

Enable content filtering, log all interactions, and include a disclaimer that responses are AI-generated.. It directly addresses Microsoft's Responsible AI principles of transparency and accountability. Enabling content filtering (via Azure AI Content Safety) ensures harmful outputs are blocked, logging all interactions provides an audit trail for accountability, and including a disclaimer that responses are AI-generated satisfies transparency by clearly informing users they are interacting with an AI system.

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Use the model without any modifications, and have a human review all responses.

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Fine-tune the model on a curated dataset of support tickets and disable content filtering.

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Use the default model deployment and rely on the model's inherent safety.

Question 10

You are building a generative AI solution using Azure OpenAI Service. The application must retrieve information from a large private knowledge base. You need to ensure the model uses only relevant documents from the knowledge base to generate answers. Which feature should you configure?

Accepted Answer

Use Azure OpenAI On Your Data with vector search. B is correct because Azure OpenAI On Your Data with vector search enables the model to retrieve only the most semantically relevant documents from a private knowledge base by converting both the user query and the documents into high-dimensional vectors and performing similarity search. This ensures the model's responses are grounded in the specific, relevant information without exposing the entire knowledge base to the model.

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Implement a custom prompt flow

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Configure a content filter

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Fine-tune the model with the knowledge base

Question 11

Your company uses Azure AI Search to power a customer support portal. The search index includes product documentation and known issues. Recently, the portal's search performance has degraded, and users report slow response times. You need to identify the cause of the performance issue. What should you check first?

Accepted Answer

Review the search service metrics for high query latency and CPU usage.. High query latency and CPU usage are direct indicators of performance bottlenecks in Azure AI Search. The search service metrics in the Azure portal provide real-time data on query execution time and resource consumption, which are the first signals to investigate when users report slow response times. Checking these metrics helps identify whether the issue stems from excessive query load, insufficient replicas, or inefficient query execution.

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Check the size of the index storage in the Azure portal.

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Ensure the index schema does not have too many fields.

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Verify that the skillset is not running during peak hours.

Question 12

You are developing a solution for a hospital that uses the Face API to identify patients from photos taken at check-in. The system must be HIPAA compliant. You need to ensure that face data is protected and not retained longer than necessary. The hospital wants to use the Face API for identification only during the patient's visit. After discharge, the face data should be deleted. What is the recommended approach?

Accepted Answer

Create a Person Group for each day, add patients, and delete the Person Group after the day ends.. Creating a Person Group per day allows you to logically group face data for that day's patients. After the day ends, deleting the entire Person Group removes all associated face data from the Face API service in a single operation, ensuring HIPAA compliance by not retaining data longer than necessary. This approach aligns with the Face API's lifecycle management, where Person Groups are the container for face IDs and their deletion cascades to all stored face data.

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Use the Face API with a subscription key and export face data to local storage, then delete from the cloud.

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Store the face IDs in a database and delete them manually after discharge.

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Use the Face API Identify operation and then delete the face ID from the Person Group.

Question 13

You are using Azure AI Language to perform sentiment analysis on customer feedback. You need to monitor the number of requests sent to the endpoint per minute. Which Azure service should you use?

Accepted Answer

Azure Monitor. Azure Monitor is the correct service because it provides platform metrics, including the 'Requests Per Minute' metric for Azure AI Language endpoints. This metric is automatically collected and can be viewed in the Azure portal, set up alerts on, or queried via the Azure Monitor REST API. It directly answers the need to monitor request rate without additional configuration.

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Azure Cost Management

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Azure Advisor

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Azure Log Analytics

Question 14

You are building a solution to detect if a person is wearing a hard hat in construction site images. You have a small dataset of labeled images. Which Azure service should you use?

Accepted Answer

Azure Custom Vision. Azure Custom Vision is the correct choice because it allows you to train a custom image classification model with your own small dataset of labeled construction site images to detect whether a person is wearing a hard hat. Unlike pre-built services, Custom Vision specializes in fine-tuning models for specific visual concepts that are not covered by general-purpose APIs, making it ideal for niche object detection tasks like hard hat detection.

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Azure AI Vision Image Analysis

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Azure Video Indexer

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Azure AI Document Intelligence

Question 15

You are using Azure OpenAI Service with the system message shown in the exhibit. The model sometimes answers questions using general knowledge even when the context does not contain the answer. Which modification should you make to enforce the behavior?

Accepted Answer

Set the 'strict' parameter to true in Azure OpenAI On Your Data configuration. The 'strict' parameter in Azure OpenAI On Your Data configuration forces the model to rely exclusively on the provided data sources and refuse to answer when the context lacks the information. This directly enforces the desired behavior of not falling back on general knowledge.

Answer

Add a user message repeating the instruction

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Reduce the temperature to 0

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Fine-tune the model with examples of refusing to answer

Question 16

You are deploying a chatbot using Azure AI Bot Service and Language Understanding (LUIS). The bot must understand user intent from free-text input. Which component should you train?

Accepted Answer

Language Understanding (LUIS) model. The Language Understanding (LUIS) model is the correct component to train because the bot needs to interpret free-text user input and extract intent. LUIS is a natural language processing service specifically designed for intent recognition and entity extraction from conversational phrases. Training the LUIS model with labeled utterances teaches it to map user expressions to predefined intents, enabling the bot to understand and respond appropriately.

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Speech-to-text model

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QnA Maker knowledge base

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Computer Vision model

Question 17

You are building a solution that must translate customer chat messages from Spanish to English in real-time. The solution must also detect the language of incoming messages to handle cases where users write in other languages. Which TWO Azure AI service features should you use?

Accepted Answer

Azure AI Translator - Language Detection. Azure AI Translator's Language Detection feature can identify the language of incoming chat messages, which is necessary to handle cases where users write in languages other than Spanish. Option B is correct because Azure AI Translator's Translation feature can then translate those detected Spanish messages into English in real-time, meeting the core requirement of the solution.

Answer

Azure AI Speech - Speech Translation

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Azure AI Language Understanding (LUIS)

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Azure AI Custom Question Answering

Question 18

You need to extract printed text from scanned invoices in multiple languages. Which Azure service should you use?

Accepted Answer

Azure Computer Vision Read API. Azure Computer Vision Read API is designed specifically for extracting printed and handwritten text from images and documents, including scanned invoices. It uses optical character recognition (OCR) to handle multiple languages, making it the correct choice for this multilingual text extraction task.

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Azure Form Recognizer

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Azure Custom Vision

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Azure Video Indexer

Question 19

You deploy the above ARM template to create an Azure AI Vision resource. After deployment, you try to call the Vision API from your application but receive a 403 Forbidden error. What is the most likely cause?

Accepted Answer

The network ACLs block all traffic by default. When you deploy an Azure AI Vision resource using an ARM template, the default network configuration sets the 'defaultAction' to 'Deny' for IP firewall rules, meaning all traffic is blocked unless explicitly allowed. Since you did not configure any network ACLs to permit your application's IP address or virtual network, the 403 Forbidden error occurs because the API endpoint rejects the request at the network layer before any authentication or authorization checks.

Answer

The apiVersion in the template is incorrect

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The custom subdomain name is not configured correctly

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The SKU 'S1' does not support API calls

Question 20

You have a real-time video processing pipeline using Azure AI Video Indexer. You need to detect when a specific person appears in archived video footage. Which approach minimizes latency and cost?

Accepted Answer

Use Video Indexer's face detection and indexing, then search. Video Indexer's built-in face detection and indexing automatically identifies and tracks faces during the indexing process, storing the results in a searchable metadata index. To detect when a specific person appears, you can then search the indexed metadata for that person's face ID or name, which avoids re-processing the video and minimizes both latency and cost. This approach leverages the one-time indexing cost and optimized search capabilities rather than running additional AI services on every frame.

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Extract keyframes and use Custom Vision to detect the person

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Run face detection on every frame using Azure AI Face and store results

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Use Azure AI Vision to detect faces in video frames and compare against a database