AI-102 Plan and manage an Azure AI solution Practice Test 14 — 15 Questions

Question 1

You are deploying a chatbot using Azure AI Bot Service integrated with Azure AI Language for natural language understanding. The bot must be able to handle multiple languages and route queries to the appropriate language model. What should you configure?

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

Accepted Answer

Use Azure AI Language with a multi-language project and enable language detection. Azure AI Language supports multi-language projects that allow you to train a single model to understand multiple languages. By enabling language detection, the bot can automatically identify the input language and route the query to the appropriate language-specific model or intent recognition within the same project, eliminating the need for separate deployments or translation steps.

Answer

Deploy Azure AI Translator to translate all input to English before processing

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Deploy the bot in multiple regions, each with a different language model

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Use Azure AI Search to route queries based on language

Question 2

You are designing a solution that uses Azure AI Vision to analyze images for moderation. The solution must detect adult content and identify text in images. You need to minimize latency and cost. Which approach should you recommend?

Accepted Answer

Call the Analyze Image API with the 'adult' and 'read' visual features. The Analyze Image API in Azure AI Vision supports multiple visual features in a single call, including 'adult' for adult content detection and 'read' for OCR (text extraction). This minimizes latency by avoiding multiple API calls and reduces cost since you are billed per API call, not per feature.

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Call the Analyze Image API twice: once for adult content and once for OCR

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Use the Computer Vision 3.2 API with the 'adult' and 'OCR' parameters

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Use the Read API for text and the Content Moderator API for adult content

Question 3

Your team is building a custom question-answering solution using Azure AI Language. The solution must be able to answer questions based on a set of PDF documents. You need to import the documents and create a knowledge base. What should you do first?

Accepted Answer

Use the Azure AI Language service with the custom question answering feature and import the documents. The custom question answering feature of Azure AI Language is specifically designed to ingest documents (including PDFs) and build a knowledge base that can be used for question-answering. This feature provides a built-in pipeline to extract question-answer pairs from documents, create a knowledge base, and deploy it as a service without needing additional search indexing or bot orchestration.

Answer

Use Azure AI Foundry to create a project and upload the documents

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Create an index in Azure AI Search and upload the documents

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Deploy an Azure AI Bot Service and connect it to the documents

Question 4

You are planning to use Azure AI Document Intelligence to process a large volume of mixed document types (invoices, receipts, and purchase orders). The solution must automatically classify each document type and extract relevant fields. What should you configure?

Accepted Answer

Create a custom classification model to identify document types, then use extraction models. Azure AI Document Intelligence (formerly Form Recognizer) requires a two-step process for mixed document types: first, a custom classification model identifies each document type (invoice, receipt, purchase order), then separate extraction models (custom or prebuilt) extract the relevant fields from each classified type. This approach ensures accurate routing and field extraction without relying on filenames or brittle regex patterns.

Answer

Use the Form Recognizer service with neural models

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Use prebuilt models for each document type and route based on filename

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Use the Read model to extract all text and then use regular expressions to classify

Question 5

You need to design an Azure AI solution that processes sensitive customer data. The solution must comply with GDPR and data residency requirements. Which TWO actions should you take?

Accepted Answer

Deploy the AI resource in the region where the data originates. Deploying the AI resource in the region where the data originates ensures compliance with GDPR data residency requirements, which mandate that personal data must not leave the geographic region of the data subject. This directly addresses the principle of data localization, a core GDPR obligation for controllers and processors.

Answer

Enable customer-managed keys (CMK) for the AI resource

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Use Microsoft Entra ID for authentication

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Use a private endpoint to connect to the AI resource

Question 6

You are architecting an Azure AI solution that uses Azure AI Language to analyze text for sentiment and key phrases. The solution must handle bursts of up to 500 requests per second but average only 50 requests per second. You need to ensure cost efficiency while meeting performance requirements. Which THREE actions should you take?

Accepted Answer

Use Azure Queue Storage to buffer requests during spikes. Azure Queue Storage can decouple the ingestion of requests from processing, allowing the solution to buffer bursts of up to 500 requests per second while the Azure AI Language service processes at a lower sustained rate. This prevents request throttling and enables cost-efficient scaling by using a lower-cost queue to absorb spikes rather than over-provisioning the AI service tier.

Answer

Select the Free tier and implement queuing

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Deploy the service in multiple regions

Question 7

You are developing an Azure AI solution that uses pre-built models from Azure AI Vision to analyze images. The solution must be able to detect objects and read printed text. Which TWO capabilities should you use?

Accepted Answer

Object detection. Azure AI Vision's Object Detection capability identifies and locates objects within an image, returning bounding box coordinates and labels. This directly meets the requirement to 'detect objects' in the solution.

Answer

OCR (legacy)

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Facial detection

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Image tagging

Question 8

Refer to the exhibit. You are implementing an Azure AI Search index for semantic search with vector support. The index includes a field 'descriptionVector' of type Collection(Edm.Single) with 1536 dimensions. When you run a vector search query, you notice that results are not sorted by relevance. What is the most likely cause?

Accepted Answer

The index does not have a vector configuration with a similarity metric. Azure AI Search requires a vector configuration with a similarity metric (e.g., cosine, dotProduct, euclidean) to compute relevance scores for vector search results. Without this configuration, the search engine cannot sort results by relevance, leading to unsorted or default ordering. The similarity metric is defined in the index's vector configuration and is essential for ranking vector query results.

Answer

The 'descriptionVector' field is not searchable

Answer

The vector dimensions do not match the embedding model output

Answer

The 'descriptionVector' field is set as retrievable: false

Question 9

Refer to the exhibit. You are configuring a custom conversational language understanding project in Azure AI Language. The project currently has English training phrases. You need to add support for Spanish. The exhibit shows the current settings. What should you do?

Accepted Answer

Set enableMultiLanguage to true and add Spanish training phrases. In Azure AI Language, a custom conversational language understanding (CLU) project can support multiple languages by setting the `enableMultiLanguage` property to `true` and then adding training phrases in the target language (Spanish) to the same project. This allows the model to learn intents and entities across languages without creating separate projects, leveraging the multilingual capabilities of the underlying LUIS-based engine.

Answer

Create a new project for Spanish

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Set enableMultiLanguage to true and deploy

Answer

Use Azure AI Translator to translate English phrases to Spanish

Question 10

You are a lead AI engineer for a global retail company. The company is building an AI-powered customer support chatbot using Microsoft Foundry. The chatbot must answer product questions, process returns, and escalate to human agents when needed. The solution uses Azure AI Language for intent recognition and Azure AI Bot Service for bot orchestration. During testing, the chatbot fails to understand customer queries about return policies. The intents 'ProductInquiry' and 'ReturnRequest' are defined, but the model often confuses them. You need to improve intent classification accuracy. The development team has already collected 500 sample utterances for each intent. You have a budget to collect additional data. What should you do?

Accepted Answer

Collect additional utterances that are similar to the confusing ones and retrain. Collecting additional utterances that are similar to the confusing ones directly addresses the ambiguity between the 'ProductInquiry' and 'ReturnRequest' intents. By providing more representative examples of edge cases where the intents overlap, the Azure AI Language model can better learn the subtle linguistic patterns that distinguish them, thereby improving classification accuracy without requiring a complete retraining from scratch.

Answer

Use Azure AI Translator to translate utterances into multiple languages

Answer

Increase the confidence threshold in the bot configuration

Answer

Create a new custom language project and retrain from scratch

Question 11

Your company deploys an Azure AI Vision solution to detect defects on a manufacturing assembly line. The solution uses a custom object detection model trained on images of products. The model is deployed as a real-time endpoint on an Azure Kubernetes Service (AKS) cluster. Recently, the defect detection accuracy dropped significantly. You suspect data drift because the lighting conditions on the assembly line changed after maintenance. You need to monitor and retrain the model to maintain accuracy. The solution must use Azure AI Foundry's model monitoring capabilities. You also need to automate retraining when drift is detected. What should you do?

Accepted Answer

Enable model monitoring in Azure AI Foundry, set up drift detection alerts, and create an automated retraining pipeline. Azure AI Foundry's model monitoring provides built-in drift detection capabilities that can automatically monitor input data distributions and trigger alerts when drift is detected. By combining this with an automated retraining pipeline (e.g., using Azure Machine Learning pipelines or Azure DevOps), you can retrain the custom object detection model on new data reflecting the changed lighting conditions without manual intervention, ensuring sustained accuracy.

Answer

Collect more training data from the new lighting conditions and retrain once

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Manually review the model performance weekly and retrain if needed

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Increase the number of replicas in the AKS cluster

Question 12

You are a security engineer for a financial services company. The company uses Azure AI Language to analyze customer communications for compliance. The solution processes sensitive personal data. You need to ensure that all data transmitted to the Azure AI Language service is encrypted in transit and that the service endpoint is not accessible from the public internet. Additionally, you must use Microsoft Entra ID for authentication. The current implementation uses API keys and the public endpoint. You need to reconfigure the solution. What should you do?

Accepted Answer

Disable the public network access, configure a private endpoint, enable managed identity, and enforce HTTPS. It addresses all three requirements: disabling public network access removes internet exposure, configuring a private endpoint ensures traffic stays within the Azure backbone and your virtual network, enabling managed identity allows Microsoft Entra ID authentication without API keys, and enforcing HTTPS guarantees encryption in transit via TLS. This combination fully secures the Azure AI Language service for sensitive personal data.

Answer

Configure a private endpoint and continue using the public endpoint for redundancy

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Disable the public network access without configuring a private endpoint

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Enable Microsoft Entra ID authentication but keep the public endpoint and API keys

Question 13

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

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Use a prebuilt model and map fields manually

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Use the Read model and write custom logic to extract fields

Question 14

You are planning an Azure AI solution that uses Azure AI Search with semantic ranker and a custom skill. The solution must meet the following requirements: - Process documents in multiple languages (English, Spanish, French). - Use a custom skill to extract entities via an Azure Function. - Ensure that the indexer can resume from failures. Which TWO actions should you include in your plan?

Accepted Answer

Generate a data change detection policy on the data source.. A data change detection policy (e.g., HighWaterMarkChangeDetectionPolicy or SqlIntegratedChangeTrackingPolicy) enables the indexer to track which documents have been modified or added. This allows the indexer to resume from failures by reprocessing only the changed or unprocessed documents, rather than starting over from scratch. Without this policy, a failure would require a full reindex, which is inefficient and violates the requirement to resume from failures.

Answer

Configure separate indexer data sources for each language.

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Set the indexer property 'maxFailedItems' to a high value.

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Enable Azure Monitor logs for the indexer to track failures.

Question 15

You are designing a conversational AI solution using Microsoft Copilot Studio. The solution must: - Allow users to ask questions about company policies stored in SharePoint Online. - Use generative answers with grounding data. - Ensure that responses are based only on approved documents. - Support authentication via Microsoft Entra ID. Which THREE components must be configured as part of the solution?

Accepted Answer

Create a new data source in Copilot Studio pointing to SharePoint Online.. Copilot Studio allows you to create a new data source that connects directly to SharePoint Online, enabling the generative answers feature to retrieve and ground responses on approved policy documents stored there. This ensures that only content from the specified SharePoint site is used, meeting the requirement for grounding data from approved documents.

Answer

Create a Power Automate flow to retrieve documents from SharePoint.

Answer

Train an AI Builder model to classify policy documents.