AI-102 Plan and manage an Azure AI solution Practice Test 7 — 15 Questions

Question 1

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.. Option A is correct because when processing multilingual content, the ability to train and deploy custom translation models (e.g., via Azure Custom Translator) lets you tailor terminology and domain-specific vocabulary for each language pair, which is a key selection factor for translation quality. Option B is correct because each Azure AI service supports a specific set of languages (for example, Translator supports 100+ languages while some Speech or Language services support fewer), so you must verify that every source and target language in your multilingual content is supported. Option C is correct because multilingual workloads often involve high volumes of text or speech across many languages, so throughput limits (such as transactions per second) and scaling options (such as autoscale or provisioned capacity) directly determine whether the service can meet demand. Option D is not a primary selection factor here because geographic region mainly affects data residency, latency, and compliance rather than the service's ability to process multilingual content. Option E is not a primary factor because pricing per transaction is a cost consideration, not a capability requirement for handling multiple languages.

Answer

Geographic region of the resource.

Answer

Pricing per transaction.

Question 2

Your organization deploys an Azure AI Foundry solution for a customer service chatbot. The chatbot uses a large language model (LLM) hosted on Azure OpenAI Service with a GPT-4 model. Requirements: (1) The chatbot must only use information from the company's internal knowledge base, not general internet knowledge. (2) Responses must include citations from the knowledge base. (3) The solution must filter out any toxic or harmful content. (4) The chatbot must be deployed in a secure environment with network isolation. You have an Azure AI Foundry project with a connected Azure OpenAI resource. The knowledge base is stored in Azure AI Search. You need to configure the solution. What should you do?

Accepted Answer

Use Azure OpenAI on your data with Azure AI Search as the data source, enable content filtering, and configure the model to use the search index with strict grounding.. It uses Azure OpenAI on your data with Azure AI Search as the data source, which ensures the model only retrieves and generates responses from the indexed knowledge base, meeting the requirement to avoid general internet knowledge. Enabling content filtering satisfies the toxicity requirement, and configuring strict grounding ensures responses include citations from the search index. The secure environment with network isolation is achieved through Azure AI Foundry's managed network capabilities, which are compatible with this configuration.

Answer

Use Azure OpenAI with function calling to retrieve knowledge base documents, and enable content filtering.

Answer

Use prompt engineering with system messages to restrict knowledge, and enable content filtering.

Answer

Fine-tune the GPT-4 model on the knowledge base and deploy with content filtering.

Question 3

You are designing an Azure AI solution for a global e-commerce company. The solution must: (1) Translate product descriptions into 12 languages in real-time. (2) Detect sentiment in customer reviews for each language. (3) Extract key product attributes (e.g., color, size) from unstructured review text. (4) Store results in a centralized database for analytics. The solution must minimize latency and cost. You plan to use Azure AI services. Which combination of services should you use?

Accepted Answer

Azure AI Translator for translation, Azure AI Language for sentiment and entity extraction, and Azure AI Search to index the results.. Azure AI Translator provides real-time translation into 12 languages, Azure AI Language handles both sentiment analysis and entity extraction (for attributes like color and size) from unstructured text, and Azure AI Search indexes the results for low-latency analytics. This combination minimizes latency by using dedicated services for each task and avoids unnecessary overhead from services like Speech or Computer Vision that are not required for text-only processing.

Answer

Azure AI Language for translation and sentiment, and Azure AI Computer Vision for attribute extraction.

Answer

Azure AI Speech for translation, Azure AI Language for sentiment, and Azure AI Search for storage.

Answer

Azure AI Translator for translation, Azure AI QnA Maker for attribute extraction, and Azure SQL Database for storage.

Question 4

A financial services company uses Azure AI Document Intelligence to process loan applications. The solution extracts data from uploaded PDFs and stores it in Azure Cosmos DB. Recently, extraction errors increased due to poor-quality scans. The company needs to improve accuracy without manual intervention. The solution must also handle varying document layouts. You need to recommend a plan. What should you do?

Accepted Answer

Train a custom document extraction model using labeled samples of poor-quality scans and different layouts.. Training a custom document extraction model using labeled samples of poor-quality scans and varying layouts directly addresses the root cause of extraction errors. Azure AI Document Intelligence's custom model capability allows you to train on specific document types and quality issues, improving accuracy without manual intervention. This approach adapts to the company's need to handle diverse layouts and degraded image quality, which prebuilt models cannot reliably manage.

Answer

Increase the throughput (TPS) of the Document Intelligence resource.

Answer

Enable OCR enhancement in Document Intelligence.

Answer

Switch to Azure AI Language for entity extraction.

Question 5

You are designing an Azure AI solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must handle up to 1000 invoices per hour with a maximum latency of 5 seconds per invoice. You need to choose the appropriate pricing tier and resource configuration. What should you do?

Accepted Answer

Use the S0 tier and configure autoscaling if needed. The S0 tier (Standard) for Azure AI Document Intelligence supports up to 15 transactions per second (TPS), which equates to 54,000 invoices per hour—far exceeding the 1,000 per hour requirement. With a maximum latency of 5 seconds per invoice, the S0 tier's default throughput is sufficient without needing autoscaling, though autoscaling can be configured for burst scenarios. The Free tier (F0) is limited to 20 transactions per minute (1,200 per hour) and is not designed for production workloads, making S0 the correct choice.

Answer

Create a Cognitive Services multi-service account to increase throughput

Answer

Use the Free tier and implement a retry policy

Answer

Deploy the Document Intelligence resource in multiple regions

Question 6

Your company uses Azure AI Language to analyze customer feedback. The solution currently uses the default endpoint and key. Security policy requires that all API calls be authenticated using Microsoft Entra ID and that network access be restricted to a specific virtual network. You need to reconfigure the resource. What should you do?

Accepted Answer

Disable local authentication, enable managed identity, and configure the virtual network. It addresses both security requirements: disabling local authentication ensures that API keys cannot be used, forcing all calls to authenticate via Microsoft Entra ID; enabling a managed identity provides a secure identity for the resource to authenticate with Entra ID; and configuring the virtual network restricts network access to the specified VNet, meeting the network restriction policy.

Answer

Regenerate the API keys and update the application

Answer

Create a private endpoint for the resource

Answer

Configure the resource to use Microsoft Entra ID authentication only

Question 7

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?

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

Answer

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

Answer

Use Azure AI Search to route queries based on language

Question 8

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. Option D is correct because GDPR data residency requirements mandate that personal data be stored and processed within the geographic region where it originates, so deploying the AI resource in the data's origin region ensures compliance with localization obligations. Option E is correct because GDPR grants data subjects rights to erasure and storage limitation, so configuring data retention and deletion policies ensures customer data is not kept longer than necessary and can be properly purged on request. Option A is not required here because customer-managed keys address encryption key control and sovereignty, not data residency or GDPR retention obligations directly. Option B is not specific to this scenario because Microsoft Entra ID authentication is a general identity best practice but does not by itself satisfy GDPR or data residency requirements. Option C is also not the targeted answer because private endpoints improve network isolation and reduce exposure, but they do not guarantee that data remains in the originating region or that retention rules are enforced.

Answer

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

Answer

Use Microsoft Entra ID for authentication

Answer

Use a private endpoint to connect to the AI resource

Question 9

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 10

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

Answer

Set enableMultiLanguage to true and deploy

Answer

Use Azure AI Translator to translate English phrases to Spanish

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

Answer

Manually review the model performance weekly and retrain if needed

Answer

Increase the number of replicas in the AKS cluster

Question 12

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.. Option A is correct because a data change detection policy (e.g., a high-water-mark or SQL integrated change tracking policy) on the data source lets the indexer identify which documents have changed, so after a failure it can resume and reprocess only the affected or new documents rather than starting over. Option C is correct because setting the indexer property 'failOnUnprocessedDocuments' to false allows the indexer to continue running and complete the indexing session even when some documents cannot be processed, which supports resuming from failures instead of aborting the whole run. Options B, D, and E are not required: separate data sources per language are unnecessary since Azure AI Search indexers and custom skills can handle multilingual content within a single pipeline, 'maxFailedItems' only controls how many failures are tolerated before the indexer stops and does not by itself ensure resumability, and enabling Azure Monitor logs is useful for diagnostics but is not a mechanism that makes the indexer resume from failures.

Answer

Configure separate indexer data sources for each language.

Answer

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

Answer

Enable Azure Monitor logs for the indexer to track failures.

Question 13

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.. Option A is correct because Copilot Studio needs a SharePoint Online knowledge source (created as a data source) so the agent can index and query the approved policy documents for grounding. Option C is correct because the requirement to support authentication via Microsoft Entra ID is met by configuring the agent's authentication settings to require Entra ID sign-in, which secures access and enables per-user permissions. Option E is correct because generative answers require a topic containing a knowledge source node that references the SharePoint source, which is what produces grounded, generative responses restricted to the approved documents. Option B is not needed because Copilot Studio's native SharePoint knowledge source handles retrieval directly, so a custom Power Automate flow is unnecessary. Option D is not needed because AI Builder classification is unrelated to grounding generative answers in SharePoint content.

Answer

Create a Power Automate flow to retrieve documents from SharePoint.

Answer

Train an AI Builder model to classify policy documents.

Question 14

Refer to the exhibit. You have an Azure AI Search skillset with the custom skill shown. When you run the indexer, you notice that many documents fail with a timeout error. What is the most likely cause of the timeouts?

Accepted Answer

The degreeOfParallelism is set too high, overwhelming the Azure Function.. The custom skill in Azure AI Search is configured with a `degreeOfParallelism` of 10, meaning up to 10 concurrent requests are sent to the Azure Function. If the function cannot handle this level of concurrency (e.g., due to limited resources or cold starts), requests will queue up and eventually time out. Reducing the `degreeOfParallelism` would throttle the load and prevent the function from being overwhelmed.

Answer

The HTTP method should be GET instead of POST.

Answer

The timeout value is too short for the function to complete.

Answer

The batch size is too large, causing each request to process too many documents.

Question 15

Your company is deploying an Azure AI Document Intelligence solution to process invoices. The solution must: - Extract key fields (invoice number, date, total amount). - Handle invoices in both PDF and image formats. - Use a prebuilt model to reduce development effort. - Process high volumes (up to 10,000 invoices per day). - Store extracted data in Azure Cosmos DB. You need to design the processing pipeline. What should you do?

Accepted Answer

Use the prebuilt invoice model with the async API. Submit all invoices for analysis. Use an Azure Function to poll for results and write to Cosmos DB.. The prebuilt invoice model's asynchronous API is designed for high-volume batch processing, allowing you to submit up to 10,000 invoices per day without timeout or rate-limit issues. The async API returns operation locations that you can poll via an Azure Function, and once results are ready, you write the extracted fields (invoice number, date, total amount) to Azure Cosmos DB. This decouples submission from retrieval, ensuring scalability and reliability for both PDF and image formats.

Answer

Use the synchronous API of the prebuilt invoice model. For each invoice, call the API and write the result to Cosmos DB.

Answer

Use the prebuilt receipt model to process invoices. Store results in Cosmos DB.

Answer

Use the layout model to extract text from invoices. Then use Azure AI Language to extract entities.