AI-102 Practice Exam 58 — 20 Questions

Question 1

Which TWO options are valid ways to index content from Azure SQL Database into Azure AI Search? (Select TWO.)

Accepted Answer

Use the Push API to send data directly to the search index.. Azure AI Search supports indexing from SQL Database using either a SQL Server indexer or a push API. Data Factory and Event Hubs are not direct indexers for SQL.

Answer

Use Azure Data Factory to copy data to Blob Storage, then index from Blob.

Answer

Use Azure AI Document Intelligence to extract data and push to index.

Answer

Use Azure Event Hubs to stream data into the search index.

Question 2

You are extracting text from scanned documents that are in French. Which capability of Azure AI Document Intelligence should you use?

Accepted Answer

Read API. The Read API is the correct choice because it is specifically designed for extracting printed and handwritten text from scanned documents, including support for multiple languages like French. It performs optical character recognition (OCR) to digitize text without requiring any additional training or customization, making it ideal for general text extraction from scanned documents.

Answer

Custom model

Answer

Layout model

Answer

Prebuilt invoice model

Question 3

You are developing a knowledge mining solution that extracts insights from customer feedback. Which TWO Azure AI services can be used to analyze the sentiment of the feedback and categorize it into topics?

Accepted Answer

Azure AI Language. Azure AI Language provides pre-built sentiment analysis and key phrase extraction capabilities that can determine the sentiment (positive, negative, neutral) of customer feedback. Additionally, its named entity recognition and topic modeling features allow you to categorize feedback into topics, making it a direct fit for this requirement.

Answer

Azure AI Personalizer

Answer

Azure AI Translator

Answer

Azure AI Custom Vision

Question 4

You have a computer vision solution that analyzes security camera feeds to detect people and vehicles. The solution uses Azure AI Vision Spatial Analysis. You need to ensure compliance with privacy regulations by blurring detected faces. Which feature should you enable?

Accepted Answer

Enable face blurring in the Spatial Analysis configuration. Azure AI Vision Spatial Analysis includes a built-in face blurring feature that can be enabled directly in the Spatial Analysis configuration. This allows you to automatically blur detected faces in the video feed at the edge or in the cloud, ensuring compliance with privacy regulations without requiring additional services or post-processing steps.

Answer

Use Azure AI Content Safety to filter faces

Answer

Post-process frames with Azure AI Face client SDK

Answer

Enable face detection and redact faces using Azure AI Video Indexer

Question 5

Your Azure AI Search indexer is failing to index a large number of PDFs from Azure Blob Storage. The error log shows 'Document extraction timeout' for many documents. You need to resolve this issue without losing data. What should you do?

Accepted Answer

Increase the indexer execution timeout in the indexer definition. The 'Document extraction timeout' error indicates that the indexer is taking longer than the default 24-hour timeout to process certain PDFs. Increasing the indexer execution timeout in the indexer definition allows the indexer to continue processing these large documents without losing data, as it extends the maximum time the indexer can run for a single execution.

Answer

Change the parsing mode of the indexer to 'text'

Answer

Split large PDFs into smaller files before uploading

Answer

Enable incremental enrichment on the skillset

Question 6

You are a data engineer at a multinational corporation. The company has thousands of research reports in PDF format stored in Azure Blob Storage. The reports contain text, tables, charts, and handwritten annotations. Your team needs to build a knowledge mining solution using Azure AI Search that allows researchers to query the reports using natural language. The solution must extract text, table structures, and handwritten annotations. Additionally, the solution must handle multiple languages (English, Spanish, and French) and ensure that the index is updated daily as new reports are added. The search should prioritize the most recent reports. You have an Azure AI Search service in the S2 tier. Which combination of actions should you take to meet these requirements?

Accepted Answer

Use Azure AI Document Intelligence layout model with OCR, add a custom translation skill, and configure a scoring profile with freshness boosting. Using Azure AI Document Intelligence's layout and OCR capabilities extracts text, tables, and handwriting. The enrichment pipeline with a custom skill using the translation service handles multilingual content, and a scoring profile with freshness boosting prioritizes recent reports. Option A is incorrect because Azure AI Vision OCR alone does not extract table structure. Option C is incorrect because the Language service does not handle document layout. Option D is incorrect because scheduling the indexer once a week does not meet the daily update requirement.

Answer

Use Azure AI Vision OCR skill for text extraction, add a translation skill, and use a simple search query

Answer

Use Azure AI Document Intelligence prebuilt-read model, add a custom skill for language detection, and schedule the indexer weekly

Answer

Use Azure AI Language text extraction, a custom entity recognition skill, and enable semantic ranking

Question 7

Your team is using Azure AI Search to index a large collection of technical manuals. Users report that searches for 'disk failure' do not return relevant results because the manuals use terms like 'hard drive crash'. Which feature should you implement to improve recall?

Accepted Answer

Add a synonym map to the index. A synonym map in Azure AI Search allows you to define equivalent terms (e.g., 'disk failure' = 'hard drive crash') so that queries automatically expand to include synonyms. This directly addresses the vocabulary mismatch between user queries and indexed content, improving recall without requiring changes to the documents or queries.

Answer

Apply a filter

Answer

Configure a scoring profile

Answer

Enable semantic search

Question 8

Which TWO of the following are best practices for managing Azure AI services costs?

Accepted Answer

Use the S0 pricing tier for production workloads. The S0 pricing tier is the standard paid tier for Azure AI services, such as Cognitive Services, and is designed for production workloads. It provides higher throughput, reliability, and support compared to lower tiers, making it a cost-effective choice for production scenarios where performance and availability are critical.

Answer

Always use the Free tier to avoid charges

Answer

Scale up partitions to improve performance

Answer

Increase batch size to reduce number of API calls

Question 9

Your knowledge mining solution uses Azure AI Search with a custom skill that calls an Azure Function to perform complex data validation. The custom skill returns an error for some documents, but the indexer continues without raising an error. What is the most likely cause?

Accepted Answer

The indexer is configured with 'allowSkillsetToExecuteIfError' set to true.. By default, indexers continue on error. To stop on skill errors, set 'allowSkillsetToExecuteIfError' to false. The indexer treats skill errors as warnings by default.

Answer

The indexer is configured with 'allowSkillsetToExecuteIfError' set to false.

Answer

The Azure Function returns HTTP 200 with an error message in the body.

Answer

The custom skill's output field mapping is incorrect.

Question 10

Which TWO are valid ways to manage cost when using Azure OpenAI Service in a production application?

Accepted Answer

Use a smaller model like GPT-3.5-turbo instead of GPT-4 for simpler tasks. Using a smaller model like GPT-3.5-turbo for simpler tasks directly reduces the per-token cost compared to GPT-4, which is significantly more expensive. Azure OpenAI Service charges based on model tier and token usage, so selecting the appropriate model for the task complexity is a primary cost management strategy.

Answer

Fine-tune the model to reduce the number of examples needed in prompts

Answer

Increase the temperature parameter to 1.0

Answer

Provision more PTUs to get a lower rate per token

Question 11

Which TWO Azure AI services can be used to extract text from images?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) includes the Read OCR engine that extracts printed and handwritten text from images and documents. Azure AI Computer Vision provides the OCR API (optical character recognition) which can extract text from images, including both printed and handwritten text, and supports multiple languages. Both services are designed specifically for text extraction from visual content.

Answer

Azure AI Face

Answer

Azure AI Video Indexer

Answer

Azure AI Custom Vision

Question 12

You are designing a solution to detect personally identifiable information (PII) in documents using Azure AI Language. The solution must also handle documents in multiple languages. Which THREE features should you use?

Accepted Answer

The language detection feature of Azure AI Language.. Azure AI Language's language detection feature is necessary to identify the language of each document before applying PII detection, ensuring the correct language-specific model is used for accurate entity extraction. This is a prerequisite step in a multi-language PII detection pipeline, as the PII detection API requires the language parameter to be set appropriately.

Answer

The conversation summarization API.

Answer

The Text Analytics for health feature.

Question 13

You are a solution architect at a legal firm. The firm wants to build a copilot using Microsoft Foundry that answers questions about case law documents stored in Azure Blob Storage. The copilot should use the Retrieval Augmented Generation (RAG) pattern with Azure AI Search as the vector store. The documents are in PDF format and include complex tables and footnotes. The solution must ensure that the answers are grounded in the documents and that the copilot can handle follow-up questions. You need to design the ingestion pipeline. Which approach should you take?

Accepted Answer

Use Azure AI Document Intelligence to extract content, then chunk by headings and paragraphs, generate embeddings using Azure OpenAI, and index in Azure AI Search with vector search. It uses Azure AI Document Intelligence to accurately extract content from PDFs (including complex tables and footnotes), then chunks by headings and paragraphs to preserve document structure, generates embeddings via Azure OpenAI for semantic understanding, and indexes in Azure AI Search with vector search to enable RAG-based, grounded answers with follow-up support.

Answer

Use Azure AI Vision OCR to extract text, split by page, and use Azure AI Search keyword search

Answer

Use Azure AI Document Intelligence prebuilt-read model, chunk by character count, and use Azure AI Search with semantic ranking

Answer

Use Azure AI Language to extract key phrases, create a non-vector index, and use simple search

Question 14

You deploy an agent using the ARM template shown. Users report that the agent cannot answer questions about uploaded documents. What is the most likely cause?

Accepted Answer

The file_search tool is disabled in the agent configuration. The ARM template shown in the question likely configures the agent with the `file_search` tool set to `disabled` or omitted, which prevents the agent from indexing and querying uploaded documents. Without this tool enabled, the agent cannot perform retrieval-augmented generation (RAG) on file content, even if files are uploaded. Option C directly identifies this missing capability as the root cause.

Answer

The function tool is missing required parameters

Answer

The model specified is not supported for file operations

Answer

The code_interpreter tool is enabled, which conflicts with file_search

Question 15

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.

Answer

The model is not being batch-processed for inference.

Answer

The model is overfitting to the training data.

Answer

The Computer Vision API version is outdated.

Question 16

You are a senior AI engineer at a financial services company. You are building a generative AI solution to assist financial advisors with client portfolio recommendations. The solution must use Azure OpenAI Service. The following requirements must be met:

1. Responses must be based on the latest market data and client profiles stored in Azure SQL Database.
2. The solution must not generate investment advice that is not backed by the data.
3. The solution must be cost-effective and minimize API calls.
4. The system must provide citations for the data used in the response.

You design a RAG pattern with Azure AI Search indexing the portfolio data. You also implement a system message instructing the model to only use provided context. However, the model occasionally generates advice that contradicts the data or invents new facts. You need to modify the solution to ensure responses are strictly grounded in the retrieved data. What should you do?

Accepted Answer

Set temperature to 0 in the Azure OpenAI completion request.. Setting the temperature to 0 makes the model deterministic, reducing its tendency to generate creative or ungrounded responses. This enforces adherence to the provided context (retrieved data) and minimizes hallucination. Option B is incorrect: reducing top_k limits the number of retrieved documents but does not force the model to rely solely on them; the model can still invent facts if the temperature is high. Option C is incorrect: increasing chunk size provides more context per document but does not prevent the model from ignoring or contradicting that context. Option D is incorrect: Azure OpenAI Service on your own data still uses a language model that can hallucinate if not properly constrained (e.g., with low temperature); it does not directly query SQL databases in real time.

Answer

Reduce the top_k parameter in the search query to retrieve fewer documents.

Answer

Increase the chunk size in the index to provide more context per document.

Answer

Use Azure OpenAI Service on your own data integration to directly query the SQL database.

Question 17

Refer to the exhibit. You are using the Azure AI Face API to detect faces in an image. You need to ensure that the response includes the unique face ID for each detected face. However, the response does not contain face IDs. What is the most likely cause?

Accepted Answer

The 'detectionModel' parameter is not set; the default detection model does not return face IDs.. The Face API's default detection model (detection_01) does not return face IDs. To obtain face IDs, you must explicitly set the 'detectionModel' parameter to 'detection_03' or 'detection_04', which support face ID generation. Without this parameter, the API omits the face ID field in the response.

Answer

The 'returnFaceLandmarks' parameter must be set to true.

Answer

The 'returnFaceId' parameter is set to false.

Answer

The API version is incorrect; use '2023-06-01-preview' instead.

Question 18

You are a developer at a large retail company. The company receives thousands of product reviews daily. You need to build a solution that automatically categorizes reviews into positive, negative, and neutral sentiments, and also extracts key product features mentioned (e.g., battery life, screen quality) along with their associated sentiments. The solution must be scalable and cost-effective. You have access to Azure AI Language. You decide to use the built-in sentiment analysis and opinion mining features. However, after initial testing, you find that the opinion mining feature does not always correctly associate sentiments with the correct product features. For example, in the review 'The battery life is great but the screen is terrible', opinion mining might incorrectly associate 'terrible' with 'battery life'. You need to improve the accuracy of feature-sentiment association. What should you do?

Accepted Answer

Create a custom NER project in Azure AI Language to extract product features, then use the opinion mining results and post-process to associate sentiments with the extracted features.. It combines Azure AI Language's built-in opinion mining with a custom NER model to extract product features, then uses post-processing logic to correctly associate sentiments with those features. This approach addresses the core limitation of opinion mining, which can misalign sentiments when multiple features with contrasting sentiments appear in the same sentence. By first extracting features via custom NER, you can then map each sentiment phrase to the nearest or most relevant extracted entity, improving accuracy without sacrificing scalability or cost-effectiveness.

Answer

Use the PII recognition feature to identify product features as entities.

Answer

Use Conversational Language Understanding (CLU) to define intents for each product feature and train a model with labeled utterances.

Answer

Use the standard sentiment analysis API without opinion mining, and then use key phrase extraction to identify features and assign overall sentiment.

Question 19

You are building an agentic solution using Microsoft Semantic Kernel. The agent uses a planner to orchestrate multiple functions. You want to improve the planner's ability to handle complex user requests that involve multiple steps. Which THREE strategies should you implement?

Accepted Answer

Enable the planner to ask the user for clarification when the request is ambiguous. Enabling the planner to ask for clarification when a request is ambiguous allows the agent to resolve underspecified intents or missing parameters, which is critical for complex multi-step workflows. In Semantic Kernel, the planner can be configured with a 'user interaction' step that prompts for additional context, improving plan accuracy and reducing the risk of incorrect function chaining.

Answer

Limit the number of available functions to reduce planning overhead

Answer

Use a simple, generic prompt to avoid overfitting

Question 20

A healthcare provider uses Azure Computer Vision to analyze medical images. They need to ensure patient data is not stored outside the Azure region. What should you configure?

Accepted Answer

Configure a private endpoint for the Computer Vision resource.. Configuring a private endpoint for the Computer Vision resource ensures that all traffic to the service traverses a private IP address within your virtual network, using Azure Private Link. This prevents data from being routed through the public internet and allows you to enforce data residency by keeping all data processing within the designated Azure region, as the private endpoint is deployed in the same region as your VNet.

Answer

Use the Free tier for Computer Vision.

Answer

Enable customer-managed keys (CMK) for the Computer Vision resource.

Answer

Deploy Computer Vision in multiple regions.