AI-102 Practice Test 42 — 15 Questions

Question 1

You are building a conversational AI system using Azure OpenAI Service. The system must maintain context across multiple user turns. Which parameter determines how many previous messages are considered for the next response?

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

Accepted Answer

The length of the messages array in the API call. The Azure OpenAI Service API uses a `messages` array in the request body to represent the conversation history. Each entry in this array corresponds to a previous turn (with roles like 'user', 'assistant', or 'system'), and the length of this array directly determines how many prior messages are considered when generating the next response. By including more messages, you extend the context window; by truncating the array, you limit it.

Answer

max_tokens

Answer

temperature

Answer

top_p

Question 2

You are designing an Azure AI Search solution that indexes customer support tickets from a SQL database and also from a custom CRM system via a custom API. The index must support scoring profiles that boost tickets from VIP customers. The VIP status is stored in the CRM system. You need to ensure that the boost factor is applied correctly. What should you do?

Accepted Answer

Include the VIP status field in the index by pushing it from the CRM system via the push API, then use a scoring profile with a field boost.. The VIP status must be present in the index for a scoring profile to use it. By pushing the VIP status field from the CRM system via the Push API, you include it in the index. Then, a scoring profile with a field boost can apply a higher score to documents where the VIP field matches a specific value, ensuring the boost is applied at query time without external dependencies.

Answer

Use a scoring profile with a tag boost that references a field in the SQL database.

Answer

Use a scoring profile with a function that queries the CRM system at query time.

Answer

Configure a custom analyzer that boosts VIP terms.

Question 3

Drag and drop the steps to set up Azure AI Content Safety for content moderation into the correct order.

Accepted Answer

1. Create Content Safety resource, 2. Get endpoint and key, 3. Call Content Safety API, 4. Analyze response, 5. Act on results. The correct order for setting up Azure AI Content Safety is: first create the Content Safety resource, then obtain its endpoint and key, call the Content Safety API with the content, analyze the response for moderation results, and finally act on the analysis (e.g., block or flag content). This sequence ensures proper authentication and logical flow from setup to action.

Answer

1. Get endpoint and key, 2. Create Content Safety resource, 3. Call Content Safety API, 4. Analyze response, 5. Act on results

Answer

1. Create Content Safety resource, 2. Get endpoint and key, 3. Call Content Safety API, 4. Act on results, 5. Analyze response

Answer

1. Call Content Safety API, 2. Analyze response, 3. Create Content Safety resource, 4. Get endpoint and key, 5. Act on results

Question 4

You need to extract product codes (e.g., 'PRD-12345') from scanned invoices using Azure AI Document Intelligence. The product codes always follow a pattern of three uppercase letters, a hyphen, and five digits. Which approach should you use?

Accepted Answer

Build a custom skill in Azure AI Search using a Python regex. The pre-built invoice model in Azure AI Document Intelligence does not support adding custom fields with regex patterns; custom field extraction with regex is only available in custom models. Option B is correct: you can build a custom skill in Azure AI Search using a Python regex to extract the product codes from the text output of Document Intelligence. This approach allows you to apply a regex pattern to the extracted content, providing accurate and flexible extraction without needing to train a model or use a large language model. Option C is less suitable because training a custom NER model requires labeled data and may not guarantee exact pattern matching. Option D is overkill for a simple regex pattern.

Answer

Use the pre-built invoice model in Azure AI Document Intelligence with a regex field extraction

Answer

Train a custom NER model in Azure AI Language

Answer

Use Azure OpenAI GPT-4 with document vision to extract the codes

Question 5

Which TWO actions should you take to reduce the latency of an Azure AI Computer Vision OCR call on a large image?

Accepted Answer

Resize the image to a smaller resolution before calling the API.. Options B and D are correct. B: Resizing reduces processing time. D: Using the Read API asynchronously allows the client to poll, avoiding timeout. A: Increasing timeout doesn't reduce latency. C: Using CPU doesn't help. E: Cognitive Services container on-premises might add network latency.

Answer

Use a CPU-bound compute instance.

Answer

Increase the API timeout value.

Answer

Deploy the Cognitive Services container on-premises.

Question 6

You need to build a solution that detects whether a person is wearing a hard hat in a construction site image. Which Azure AI service should you use?

Accepted Answer

Azure AI Custom Vision. Azure AI Custom Vision is the correct service because it allows you to train a custom object detection model to identify specific objects—such as a hard hat—in images. Unlike pre-built services, Custom Vision lets you upload labeled images of workers with and without hard hats, train a model, and then use it to detect hard hat presence in construction site photos. This tailored approach is necessary because hard hat detection is a specialized use case not covered by generic vision APIs.

Answer

Azure AI Video Indexer

Answer

Azure AI Face

Answer

Azure AI Document Intelligence

Question 7

Which THREE factors should you consider when selecting an Azure AI service for a solution that processes multilingual content?

Accepted Answer

Availability of custom translation models.. Azure AI Translator and Azure AI Language support custom translation models via the Custom Translator feature, which allows you to build and deploy domain-specific translation models for multilingual content. This is critical when generic translation models fail to handle industry-specific terminology or brand voice, ensuring higher accuracy for specialized use cases like legal or medical documents.

Answer

Geographic region of the resource.

Answer

Pricing per transaction.

Question 8

Your company is developing an AI-powered document processing solution using Azure AI Document Intelligence. The solution must extract data from scanned PDF forms. The forms are in a custom format not supported by prebuilt models. You have 10,000 labeled forms for training. The solution must be deployed in a region that supports Document Intelligence and must be accessible via a REST API. You need to ensure the solution can process forms with high accuracy. What should you do?

Accepted Answer

Train a custom extraction model using the labeled forms. Azure AI Document Intelligence supports training custom extraction models using labeled forms, which is essential for handling custom form layouts not covered by prebuilt models. With 10,000 labeled forms, you have sufficient data to train a high-accuracy model that extracts specific fields via the REST API, meeting the deployment and accessibility requirements.

Answer

Use Azure AI Language to extract entities from the text

Answer

Use a prebuilt model and map fields manually

Answer

Use the Read model and write custom logic to extract fields

Question 9

Which TWO actions should you take to ensure that a generative AI model deployed on Azure Machine Learning is compliant with data privacy regulations?

Accepted Answer

Implement data masking during preprocessing.. Options A and C are correct. A: Data masking during preprocessing helps anonymize sensitive data before it is used, reducing privacy risks. C: Differential privacy adds noise to the training process, preventing the model from memorizing individual data points. B is wrong because storing data in a separate Azure region does not inherently address privacy compliance; it may help with data residency but not the core privacy protection. D is wrong because encrypting the model at rest with Azure Key Vault is a security measure, not a privacy compliance measure. E is wrong because logging raw input data to Azure Monitor could expose sensitive information, violating privacy.

Answer

Store all training data in a separate Azure region.

Answer

Encrypt the model at rest using Azure Key Vault.

Answer

Log all raw input data to Azure Monitor for auditing.

Question 10

You are a senior AI engineer at a manufacturing company. The company has a production line that uses cameras to capture images of assembled products. The current system uses a set of rule-based heuristics to detect defects, but it has high false-positive rates. You have been tasked to design a new computer vision solution using Microsoft Azure AI services. The solution must:

- Detect defects such as scratches, dents, and misalignments in real-time as products move on the conveyor belt (frame rate of 30 fps).
- Support continuous learning: when a new defect type is discovered, the model should be updated without retraining the entire model from scratch.
- Operate with low latency (<100 ms per inference) to keep up with the production speed.
- Use only fully managed services (no custom containers or edge devices).
- The factory network has limited internet bandwidth, so the solution must minimize data transfer.

Which approach should you recommend?

Accepted Answer

Use Azure AI Vision Spatial Analysis to detect defects in real-time by analyzing video feeds. Azure AI Vision Spatial Analysis is designed for analyzing human activities and movements, not for detecting product defects. It typically runs as a container on edge devices, contradicting the requirement for fully managed services. Using Azure AI Custom Vision as a managed service (without exporting to a container) would meet the requirements of real-time defect detection, continuous learning, and low latency, but none of the options provide this. Therefore, no option is correct.

Answer

Use Azure AI Video Indexer to index and search for defects in recorded videos

Answer

Use Azure AI Document Intelligence to analyze images of products

Answer

Train an object detection model using Azure AI Custom Vision, export it as a Docker container, and deploy it on an on-premises server with GPU

Question 11

You executed the Azure CLI command to list Azure OpenAI resources. You need to programmatically access the endpoint of the resource named 'gpt4' in a script. What is the most reliable way to extract the endpoint?

Accepted Answer

Use az cognitiveservices account show with --query to filter by name. `az cognitiveservices account show` with the `--query` parameter allows you to retrieve the specific endpoint property for a named resource using JMESPath filtering. This approach is reliable, scriptable, and avoids parsing unstructured output, which is error-prone. The Azure CLI returns structured JSON, and `--query` extracts the exact value without manual string manipulation.

Answer

Use Azure PowerShell Get-AzCognitiveServicesAccount cmdlet

Answer

Parse the table output using string manipulation

Answer

Deploy a new Azure OpenAI resource with a known endpoint

Question 12

Refer to the exhibit. You are using Azure OpenAI Service to generate summaries of meeting notes. The current configuration produces summaries that are too short and sometimes omit key points. How should you modify the parameters to get more complete summaries?

Accepted Answer

Increase max_tokens to 300.. Increasing max_tokens to 300 allows the model to generate longer completions, which directly addresses the issue of summaries being too short and omitting key points. The max_tokens parameter sets the maximum number of tokens (words and punctuation) the model can produce in a single response, so raising it gives the model more capacity to include all necessary details from the meeting notes.

Answer

Decrease temperature to 0.3.

Answer

Increase frequency_penalty to 0.5.

Answer

Set top_p to 0.9.

Question 13

A company uses Azure AI Language's custom text classification to categorize support tickets. The model was trained with 5000 labeled examples and achieves 90% accuracy. However, for a specific category (e.g., 'billing'), the model frequently misclassifies tickets that contain both billing and technical issues. Which action should you take to improve classification for this category?

Accepted Answer

Add more labeled examples for the 'billing' category, especially those that are mixed with other categories.. Adding more labeled examples for the 'billing' category, especially those that are mixed with other categories, will help the model learn to distinguish them better. Option A is wrong because reducing the number of categories may not address the specific confusion. Option C is wrong because increasing the training epochs may lead to overfitting. Option D is wrong because using a different algorithm is not an option in Azure AI Language's custom text classification.

Answer

Reduce the number of categories to simplify the classification.

Answer

Increase the number of training epochs to further train the model.

Answer

Use a different classification algorithm, such as a neural network.

Question 14

A company uses Microsoft Copilot Studio to create an agent that books meetings. The agent calls an external API to check room availability. The API requires a client certificate for authentication. Which authentication method should the developer configure in the custom connector?

Accepted Answer

Client Certificate. The custom connector in Microsoft Copilot Studio must authenticate with an external API that requires a client certificate. The 'Client Certificate' authentication method is specifically designed for this scenario, where the connector presents an X.509 certificate to the API during the TLS handshake to prove its identity. This is the only option that directly supports certificate-based mutual TLS (mTLS) authentication.

Answer

OAuth 2.0

Answer

Windows Authentication

Answer

API Key

Question 15

You are designing a document processing pipeline using Azure AI Document Intelligence. The pipeline must extract data from both structured forms and unstructured invoices. The solution should support custom models with minimal manual labeling. Which approach should you use?

Accepted Answer

Train a custom neural model with a few labeled samples. Azure AI Document Intelligence's custom neural models can be trained with as few as five labeled samples, leveraging transfer learning to achieve high accuracy on both structured forms and unstructured invoices. This approach minimizes manual labeling effort while supporting the diverse document types in the pipeline, unlike prebuilt models that are limited to specific layouts or rule-based methods that require extensive manual configuration.

Answer

Use prebuilt invoice models for all documents

Answer

Use Azure AI Language to analyze text

Answer

Use OCR and custom regex patterns