AI-102 Practice Test 18 — 15 Questions

Question 1

You need to create a chatbot that uses Azure OpenAI to answer questions about your company's internal policies. The responses must be based only on the provided policy documents. Which approach should you use?

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

Accepted Answer

Use Retrieval-Augmented Generation (RAG) with an Azure AI Search index of the documents.. RAG (Retrieval-Augmented Generation) is the correct approach because it grounds the model's responses in your actual policy documents by retrieving relevant chunks from an Azure AI Search index at query time and injecting them into the prompt. This ensures the chatbot answers only from the provided documents, avoids hallucination, and keeps the source of truth external and updatable without retraining. Azure OpenAI's 'On Your Data' feature implements exactly this pattern using Azure AI Search as the vector/keyword store.

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Use the model's pre-existing knowledge about common policies.

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Fine-tune a GPT model on the policy documents.

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Use prompt engineering to instruct the model to only use policy knowledge.

Question 2

A developer is building a mobile app that uses Azure Computer Vision to analyze images. The app needs to handle many requests with low latency. Which pricing tier should they choose?

Accepted Answer

Computer Vision S1 tier.. The Computer Vision S1 tier is designed for production workloads requiring high throughput and low latency, making it suitable for a mobile app that handles many image analysis requests. Unlike the Free F0 tier, which has strict rate limits (e.g., 20 transactions per minute), the S1 tier offers higher transactions per second (e.g., up to 10 TPS) and guaranteed performance for real-time scenarios.

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Video Analyzer S1 tier.

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Free F0 tier.

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Custom Vision S0 tier.

Question 3

You are designing an Azure AI solution that uses Language Understanding (LUIS) for intent detection. The solution must handle multiple languages dynamically based on the user's locale. What should you do?

Accepted Answer

Create separate LUIS applications for each language and route based on locale.. LUIS does not natively support multi-language within a single application; each LUIS app is designed for a single language. To handle multiple languages dynamically, you must create separate LUIS applications for each language and route user utterances based on the detected locale, ensuring accurate intent and entity recognition per language.

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Use Azure Translator to translate user input to English before sending to LUIS.

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Train a single LUIS app with utterances in all languages.

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Enable the 'Multi-Language' feature in the LUIS app.

Question 4

Refer to the exhibit. You are calling the Azure AI Language NER API. The response returns no entities. What is the most likely reason?

Accepted Answer

The text does not contain any recognized entities. The NER API returns entities only if the input text contains recognized entity types (e.g., Person, Location, Organization, DateTime, etc.). If no entities are found, the API returns an empty entities array. This is the most straightforward and common reason for a zero-entity response, assuming the request is otherwise valid.

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The API version is incorrect

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The document language should be 'es' for Spanish

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The endpoint URL is for the wrong region

Question 5

You are building a generative AI chatbot using Microsoft Copilot Studio. The chatbot must answer questions from a PDF document and a SQL database. Which THREE data sources can you configure? (Choose three.)

Accepted Answer

Custom connector to SQL database. A is correct because Microsoft Copilot Studio supports custom connectors to access external data sources like SQL databases. This allows the chatbot to query the SQL database directly using the connector's API, enabling real-time data retrieval for generative AI responses.

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Azure AI Search index

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Azure Blob Storage

Question 6

You are building a generative AI solution using Azure Machine Learning prompt flow. The solution must allow business analysts without coding experience to modify prompts and evaluate different model versions. What should you do?

Accepted Answer

Use Azure Machine Learning prompt flow with the visual designer and variant management. Azure Machine Learning prompt flow provides a visual designer that enables non-technical users to modify prompts without coding, and its variant management feature allows them to evaluate different model versions side-by-side. This directly addresses the requirement for business analysts to iteratively refine prompts and compare model outputs in a controlled, no-code environment.

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Provide the analysts with a Jupyter notebook using the OpenAI Python SDK

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Deploy a chatbot in Microsoft Copilot Studio and let analysts configure it

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Implement a custom web UI using Azure Static Web Apps and Azure Functions

Question 7

A retail company uses Azure Computer Vision to analyze customer traffic in stores. They deploy a custom object detection model to count customers and detect occupancy. After deployment, the model consistently underestimates the number of customers during peak hours. The company has retrained the model with more data but the issue persists. What is the most likely cause?

Accepted Answer

The training data does not adequately represent peak-hour scenarios.. The model consistently underestimates customer counts during peak hours, which indicates a distribution shift between the training data and the inference environment. Even after retraining with more data, the issue persists because the additional data likely still lacks sufficient representation of peak-hour scenarios (e.g., high density, occlusion, rapid movement). In Azure Custom Vision, object detection models learn from labeled examples; if the training set does not include diverse peak-hour images with varied lighting, crowd densities, and angles, the model will fail to generalize to those conditions.

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The model is not being batch-processed for inference.

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The model is overfitting to the training data.

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The Computer Vision API version is outdated.

Question 8

A company uses Azure OpenAI to generate marketing copy. They want to ensure that the generated content does not contain offensive language. Which feature should they enable?

Accepted Answer

Enable content filtering at the deployment level.. Azure OpenAI provides built-in content filtering at the deployment level that automatically detects and blocks offensive or harmful language in both input prompts and generated outputs. This feature uses Microsoft's Responsible AI models to enforce safety policies without requiring custom code or manual review, making it the most reliable and scalable solution for preventing offensive content in marketing copy.

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Use DALL-E to generate images instead of text.

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Use a system message instructing the model to avoid offensive language.

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Enable diagnostic logging to review all outputs.

Question 9

You deploy an Azure AI Services resource using the ARM template shown in the exhibit. You need to test the Language service API from your local machine. What should you do first?

Accepted Answer

Add your public IP address to the ipRules array in the networkAcls. The ARM template in the exhibit sets `defaultAction` to `Deny`, which blocks all traffic not explicitly allowed by the `ipRules` array. To test the Language service API from your local machine, you must add your public IP address to the `ipRules` array so that the resource's network firewall permits inbound requests from your IP. Without this step, all API calls from your local machine will be rejected with a 403 Forbidden error.

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Configure a managed identity for the resource

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Change the defaultAction to Allow

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Use Azure CLI to enable the resource

Question 10

Your company is deploying an Azure AI solution that uses multiple AI services. You need to ensure that all API calls are authenticated securely using managed identities. Which of the following steps is required to enable managed identity authentication for an Azure AI service?

Accepted Answer

Assign a managed identity to the Azure AI service and grant it the required RBAC role. Managed identity authentication for Azure AI services requires assigning either a system-assigned or user-assigned managed identity to the resource, and then granting that identity the appropriate RBAC role (e.g., Cognitive Services User) on the target AI service. This eliminates the need for keys or secrets in the code and leverages Microsoft Entra ID tokens for secure, passwordless authentication.

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Enable system-assigned managed identity at the subscription level

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Store the authentication key in Azure Key Vault and reference it in the application

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Configure a shared access key in the Azure AI service

Question 11

You are using Azure OpenAI Service to summarize customer emails. The summaries must be concise and contain only key information. Which prompt engineering technique should you apply?

Accepted Answer

Use few-shot prompting with examples of desired summaries. Few-shot prompting is the correct technique because it provides the model with explicit examples of desired input-output pairs (e.g., a verbose email and its concise summary). This guides the model to learn the exact format, tone, and level of detail required for the summaries, which is critical for consistency in a production summarization pipeline. Without examples, the model may default to its training distribution and produce overly verbose or irrelevant output.

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Use chain-of-thought prompting

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Use zero-shot prompting with a one-sentence instruction

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Use negative prompting to avoid verbose output

Question 12

You are using Azure AI Foundry to fine-tune a GPT-3.5 model on a dataset of customer service conversations. The fine-tuning job fails with an error indicating that the training data format is invalid. What is the most likely issue?

Accepted Answer

The training data is not in JSONL format with the correct structure.. Azure AI Foundry requires fine-tuning data to be in JSONL format with a specific structure: each line must be a JSON object containing a 'messages' array with 'role' and 'content' fields for system, user, and assistant turns. The error indicates the training data format is invalid, and the most likely cause is that the data is not in this required JSONL structure, as JSONL is the only accepted format for GPT-3.5 fine-tuning in Azure OpenAI Service.

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The training data is in CSV format instead of JSON.

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The training data contains only one conversation example.

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The training data does not include the assistant's responses.

Question 13

You are using Azure OpenAI Service to generate marketing copy. The marketing team reports that the generated content sometimes contains factual inaccuracies. You need to improve the factual accuracy of the generated content. What should you do?

Accepted Answer

Include relevant context and facts in the prompt. The most effective way to improve factual accuracy in Azure OpenAI generations is to ground the model with relevant context and facts in the prompt — this is the core of Retrieval-Augmented Generation (RAG). By supplying authoritative source content, the model conditions its output on verified information rather than relying solely on parametric memory, which reduces hallucinations. This directly addresses the marketing team's complaint about factual inaccuracies.

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Increase the max_tokens parameter

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Decrease the temperature parameter

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Disable content filtering

Question 14

You need to generate an image of a cat wearing a hat using Azure OpenAI. Which model should you use?

Accepted Answer

DALL-E. DALL-E is the Azure OpenAI model specifically designed for generating images from natural language descriptions. It uses a diffusion-based architecture to create high-quality, original images based on text prompts, making it the correct choice for generating an image of a cat wearing a hat.

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Codex

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GPT-4

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Whisper

Question 15

You are developing a solution that uses Azure OpenAI to generate customer support responses. You want to prevent the model from repeating the same phrases. Which parameter should you adjust?

Accepted Answer

frequency_penalty. The frequency_penalty parameter (option D) is correct because it directly reduces the likelihood of the model repeating the same phrases by penalizing tokens that have already appeared in the generated text. A higher frequency_penalty value (e.g., 0.5 to 1.0) decreases the probability of reusing tokens, making the output more diverse and less repetitive. This is specifically designed to address repetition in generative AI responses.

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top_p

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presence_penalty

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temperature