AI-102 Practice Exam 2 — 20 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The model version is outdated.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.

Question 11

You are building an agent for a legal firm that uses Azure OpenAI to analyze contracts. The agent must extract key clauses, identify risks, and summarize the contract. The agent uses a RAG pattern with Azure Cognitive Search as the vector database. After deployment, the agent sometimes returns irrelevant information or fails to find relevant clauses. You suspect the issue is with the chunking strategy. The contracts are large, typically 50-100 pages. Currently, you are chunking by page (each page is one chunk). You want to improve retrieval accuracy. Which action should you take?

Accepted Answer

Change chunking to use semantic boundaries: split at clause or section headings.. Option D is correct because splitting contracts at semantic boundaries (clause or section headings) preserves the natural meaning and context of each chunk, which is critical for legal document analysis. Page-level chunking often splits a clause across two pages, causing the vector search to retrieve incomplete or irrelevant information. By aligning chunks with the document's logical structure, the RAG pattern retrieves more coherent and relevant passages for the Azure OpenAI agent to process.

Answer

Keep page-level chunking but add 50% overlap between chunks.

Answer

Use a different embedding model, such as text-embedding-3-large.

Answer

Increase the chunk size to 5 pages per chunk and reduce overlap.

Question 12

A company is building an agent that uses Azure OpenAI to answer questions from a large document library. The agent must use a Retrieval Augmented Generation (RAG) pattern. Which TWO actions should the team take to implement RAG effectively?

Accepted Answer

Index the documents into a vector database like Azure Cognitive Search.. Option C is correct because indexing documents into a vector database like Azure Cognitive Search enables efficient similarity search over embeddings, which is the retrieval foundation of RAG. This allows the system to quickly find the most relevant document chunks based on semantic meaning, rather than relying on the model to memorize or be fine-tuned on the entire library.

Answer

Ensure the model is large enough to memorize the entire document library.

Answer

Fine-tune the Azure OpenAI model on the document library.

Answer

Train a custom language model from scratch.

Question 13

An agent uses Azure AI Language to perform sentiment analysis on customer feedback. The team notices that the sentiment scores are sometimes inaccurate for negative feedback. Which TWO improvements should the team consider?

Accepted Answer

Pre-process the text to handle negations and sarcasm.. Option A is correct because Azure AI Language's pre-built sentiment analysis models can struggle with linguistic nuances like negations (e.g., 'not good') and sarcasm (e.g., 'Great, another delay'). Pre-processing the text to explicitly handle these patterns—such as by expanding contractions or using negation detection—can improve the accuracy of the sentiment scores before they are passed to the model.

Answer

Switch to a different language model without fine-tuning.

Answer

Increase the number of decimal places in the sentiment score.

Answer

Increase the confidence threshold for positive sentiment.

Question 14

An e-commerce company wants to build an agent that helps users track orders, initiate returns, and answer FAQs. The agent should be available on the company's website and mobile app. Which Azure service should the team use to deploy the agent?

Accepted Answer

Azure Bot Service. Azure Bot Service is the correct choice because it provides a managed environment for building, deploying, and scaling conversational AI agents that can be integrated with multiple channels, including websites and mobile apps. It supports the Bot Framework SDK, which enables the agent to handle order tracking, returns, and FAQs through natural language understanding (NLU) with LUIS or the newer CLU service.

Answer

Azure Logic Apps

Answer

Azure API Management

Answer

Azure Functions

Question 15

A company is developing an agent that uses Azure AI Language to extract entities and intents from user queries. The agent receives a query: 'Book a flight to Paris on Friday.' The agent should extract the intent as 'BookFlight' and entities as 'Paris' (destination) and 'Friday' (date). The team uses a custom entity extraction model. After testing, the model extracts 'Paris' as location but fails to extract 'Friday' as date. What should the team do to fix this?

Accepted Answer

Train the entity extraction model with more examples of dates.. Option C is correct because the custom entity extraction model in Azure AI Language requires sufficient labeled examples for each custom entity type to learn patterns. Since the model extracts 'Paris' (location) but fails on 'Friday' (date), the issue is specifically with the date entity's training data, not the location. Adding more diverse examples of date expressions (e.g., 'next Monday', 'tomorrow', 'March 5th') will improve the model's ability to recognize 'Friday' as a date entity.

Answer

Increase the training data for location entities.

Answer

Add a prebuilt entity component for date.

Answer

Use a different intent classification model.

Question 16

A company is deploying an Azure AI solution that uses Azure Cognitive Services. The solution must comply with data residency requirements that mandate all customer data be stored within a specific geographic region. Which action should the company take when creating the Cognitive Services resource?

Accepted Answer

Select the appropriate region during resource creation.. Data residency requirements are satisfied by physically storing customer data within a specific geographic boundary. When creating an Azure Cognitive Services resource, selecting the appropriate region (e.g., 'West Europe' or 'East US') during the provisioning process ensures that all data processed and stored by that service instance remains within that Azure datacenter region. This is the fundamental and only guaranteed method to enforce data residency at the resource level.

Answer

Apply a resource tag that specifies the region.

Answer

Configure the endpoint URL to point to a regional endpoint.

Answer

Set the SKU to a tier that supports regional restrictions.

Question 17

A developer is building a chatbot using Azure Bot Service and Language Understanding (LUIS). The bot needs to handle multiple intents, including 'BookFlight', 'CancelFlight', and 'CheckWeather'. During testing, the bot frequently confuses 'BookFlight' and 'CancelFlight' intents. What is the most effective way to improve intent classification accuracy?

Accepted Answer

Add more varied training utterances for 'BookFlight' and 'CancelFlight' intents.. Adding more varied training utterances for the 'BookFlight' and 'CancelFlight' intents directly addresses the root cause of confusion: insufficient or overlapping training data. LUIS relies on diverse utterance patterns to distinguish between semantically similar intents; increasing the quantity and variety of labeled examples improves the model's ability to learn discriminative features, thereby boosting classification accuracy.

Answer

Reduce the number of intents by merging similar ones.

Answer

Increase the confidence threshold for intent predictions.

Answer

Add more entities to the utterances.

Question 18

A company is using Azure Form Recognizer to extract data from invoices. The prebuilt model does not correctly extract a custom field that is specific to the company's invoices. What is the most appropriate action to improve extraction accuracy for this field?

Accepted Answer

Train a custom model using labeled invoices that include the custom field.. The prebuilt Form Recognizer model is designed for common invoice layouts and may not recognize company-specific fields. Training a custom model with labeled invoices that include the custom field allows the model to learn the field's location and semantics, significantly improving extraction accuracy for that specific field.

Answer

Use the prebuilt model with a custom field mapping.

Answer

Adjust the confidence threshold for the prebuilt model.

Answer

Retrain the prebuilt model with additional invoices.

Question 19

A company plans to use Azure Cognitive Search to index a large number of PDF documents stored in Azure Blob Storage. The documents contain sensitive personally identifiable information (PII). The search results should only be accessible to authorized users based on their Azure Active Directory (Azure AD) group membership. Which combination of features should the company implement?

Accepted Answer

Index the documents and use security filters with Azure AD group-based access control.. Option B is correct because Azure Cognitive Search supports security trimming via OAuth 2.0 and Azure AD group-based access control. By storing group identifiers in a search index field and using a security filter, only users whose Azure AD group membership matches the stored group IDs can see the corresponding search results. This ensures that sensitive PII in PDF documents is only returned to authorized users without exposing the documents themselves.

Answer

Generate shared access signatures (SAS) for each document and include them in the search index.

Answer

Use Azure RBAC to assign permissions to users for the search service and configure field-level security.

Answer

Store Azure AD group membership in a field in the search index and configure an indexer to map the field.

Question 20

A company uses Azure Content Moderator to review user-generated images in a social media app. Recently, the team noticed that images containing subtle adult content are not being flagged. What should they do to improve detection without increasing false positives?

Accepted Answer

Configure a human review team using the Review tool to manually inspect flagged content.. Option C is correct because Azure Content Moderator is designed to work with human review teams via the Review tool to handle edge cases where automated detection fails. By configuring a human review team, flagged images can be manually inspected to catch subtle adult content that the machine learning model misses, without lowering thresholds that would increase false positives. This approach leverages human judgment to improve detection accuracy while maintaining the existing automated moderation settings.

Answer

Increase the moderation thresholds for adult content.

Answer

Disable the adult classification tier to allow all images to pass through.

Answer

Retrain the Content Moderator model with additional labeled images of adult content.