AI-102 Timed Practice Test 3 — 10 Questions

Question 1

An agent uses Azure OpenAI with function calling to perform actions. The agent is not executing functions correctly. Which THREE factors should the team check to diagnose the issue?

Accepted Answer

The token limit is too low, truncating the function definitions.. Option B is correct because if the token limit is too low, the model may not receive the full function definitions, causing it to omit or misinterpret available functions. This truncation prevents the model from correctly selecting or formatting function calls, leading to execution failures.

Answer

The temperature parameter is set too high.

Answer

The model version is outdated.

Question 2

A healthcare company is developing an agent that processes patient records and suggests treatment plans. The agent must comply with HIPAA regulations. Which service should the team use to ensure data privacy and compliance?

Accepted Answer

Azure AI Services deployed in a private endpoint with no data leaving the network.. Option D is correct because deploying Azure AI Services within a private endpoint, combined with ensuring no data leaves the network, aligns with HIPAA's requirement for data privacy and compliance. This configuration uses Azure Private Link to keep all traffic within the Microsoft backbone network, preventing exposure to the public internet and meeting the strict data residency and encryption standards mandated by HIPAA.

Answer

Microsoft Bot Framework SDK with a custom connector.

Answer

Azure Cognitive Search with custom analyzers.

Answer

Azure OpenAI Service with data processing enabled.

Question 3

A company is building an agent that needs to perform tasks like sending emails and updating a CRM system. The agent uses Azure OpenAI with function calling. The team defines functions for these tasks. When the agent is tested, it sometimes calls the wrong function or invents function names. What should the team do to improve the reliability of function calling?

Accepted Answer

Provide better function descriptions with examples of when to use each function.. Option D is correct because providing better function descriptions with examples directly improves the model's ability to select the appropriate function. Azure OpenAI's function calling relies on the semantic understanding of the function definitions; clear descriptions and usage examples reduce ambiguity, helping the model map user intent to the correct function signature without hallucinating names.

Answer

Fine-tune the model on a dataset of correct function calls.

Answer

Reduce the number of functions to only the most common ones.

Answer

Set the temperature parameter to 0 for deterministic output.

Question 4

A company is developing an agent that uses Azure AI Vision to analyze images uploaded by users. The agent must identify objects and read text in images. The team uses the Azure AI Vision API. During testing, the agent fails to read text from images with low contrast. What should the team do to improve optical character recognition (OCR) accuracy for such images?

Accepted Answer

Pre-process the image to adjust contrast and brightness before calling the OCR API.. Option B is correct because Azure AI Vision's OCR API performs best on images with sufficient contrast and brightness. Pre-processing the image (e.g., using OpenCV or PIL to adjust contrast and brightness) enhances text visibility, directly improving OCR accuracy for low-contrast images without changing the API or training a custom model.

Answer

Use a different OCR API from Azure Cognitive Services.

Answer

Train a custom OCR model using Azure Custom Vision.

Answer

Increase the confidence threshold for text detection.

Question 5

A healthcare provider uses Azure Video Analyzer for Media to extract insights from surgical videos. They need to ensure that no patient health information (PHI) is stored in the transcriptions. What is the best approach?

Accepted Answer

Enable content moderation in Video Analyzer for Media settings.. Option C is correct because enabling content moderation in Azure Video Analyzer for Media automatically redacts or flags sensitive content, including PHI, from transcriptions during the indexing process. This built-in feature ensures that PHI is not stored in the output without requiring additional post-processing or manual deletion.

Answer

Use Azure AI Content Safety to post-process transcriptions.

Answer

Disable indexing for videos containing PHI.

Answer

Use the 'delete' API to remove all transcripts after processing.

Question 6

Which TWO actions can reduce the cost of using Azure Custom Vision for image classification? (Choose two.)

Accepted Answer

Use the compact domain for faster training.. Option B is correct because using the compact domain in Azure Custom Vision reduces model complexity and training time, which directly lowers compute costs. Compact domains are optimized for edge deployment and require fewer resources, making them more cost-effective for image classification tasks.

Answer

Include negative samples in the dataset.

Answer

Use a larger image size for higher accuracy.

Answer

Increase the number of training iterations.

Question 7

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.

Answer

Video Analyzer S1 tier.

Answer

Free F0 tier.

Answer

Custom Vision S0 tier.

Question 8

A company uses Azure Custom Vision to classify images of defective parts. After deploying the model, the accuracy is low. The team only has 10 images per class. What is the most effective way to improve accuracy?

Accepted Answer

Add at least 50 more images per class with variations.. Option B is correct because Azure Custom Vision relies on deep learning models that require a sufficient number of diverse training images to generalize well. With only 10 images per class, the model is severely underfit and prone to overfitting; adding at least 50 more images per class with variations in lighting, angle, and background provides the necessary data diversity to improve accuracy significantly.

Answer

Use a different classification algorithm.

Answer

Reduce the image resolution to speed up training.

Answer

Increase the number of training iterations (epochs).

Question 9

Which THREE are required to build a custom video indexing solution using Azure Video Analyzer for Media? (Choose three.)

Accepted Answer

An Azure Video Analyzer for Media account.. An Azure Video Analyzer for Media account is required because it is the core service that provides the video indexing capabilities, including AI-powered insights like speech transcription, face detection, and sentiment analysis. Without this account, you cannot access the indexing APIs or manage indexed videos.

Answer

A custom indexing pipeline.

Answer

A custom language model.

Question 10

A company is using Azure OpenAI Service to power a customer support agent. The agent sometimes generates incorrect information when it cannot find an answer in the knowledge base. The team wants to ensure the agent only responds using information from the knowledge base and explicitly states when it does not know the answer. Which configuration should the team use?

Accepted Answer

Use 'use your own data' feature with strict content filtering and set the model to only respond based on retrieved documents.. Option C is correct because the 'use your own data' feature in Azure OpenAI Service allows you to restrict the model to answer only from the retrieved documents, ensuring responses are grounded in the knowledge base. Strict content filtering further prevents the model from generating unverified information, and the explicit setting to respond based solely on retrieved documents directly addresses the requirement to state when it does not know the answer.

Answer

Use a custom model fine-tuned on the knowledge base and disable content filtering.

Answer

Use a system message that says 'If you don't know, say you don't know' and rely on the model's training.

Answer

Use prompt engineering with a system message that instructs the model to only answer from the knowledge base, with no additional filtering.