AI-102 Practice Test 34 — 15 Questions

Question 1

You are managing an Azure AI solution that uses Azure OpenAI and Azure AI Search. You need to ensure that the solution can be deployed to multiple environments with different configurations while minimizing manual steps. Which two actions should you take? (Choose two.)

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

Accepted Answer

Store environment-specific values in a parameter file for each environment.. To deploy the same solution to multiple environments with minimal manual steps, you should parameterize environment-specific values using parameter files and modularize the deployment using Bicep modules referenced by environment-specific main files. This combination ensures consistency, reduces duplication, and allows automated, repeatable deployments across development, test, and production.

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Manually create resources in the Azure portal for each environment.

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Use a single hard-coded connection string for all environments.

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Use Azure Policy to enforce naming conventions and tags after deployment.

Question 2

You are planning an Azure AI solution that uses Azure OpenAI. You need to ensure that the solution can be deployed to multiple regions for high availability. The solution must automatically route requests to the nearest available region and fail over if a region becomes unavailable. What should you use?

Accepted Answer

Azure Front Door with latency-based routing. Azure Front Door is the appropriate service because it provides global HTTP load balancing with latency-based routing and automatic failover. It can route requests to the nearest available Azure OpenAI endpoint across multiple regions and detect unhealthy endpoints to redirect traffic. The other options are either regional load balancers or do not support latency-based global routing.

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Azure Load Balancer with availability zones

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Azure Application Gateway with multi-site listeners

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Azure Traffic Manager with priority routing

Question 3

You are deploying an Azure AI solution that uses Azure AI Document Intelligence to extract data from invoices. The solution must process documents in near real-time and must be able to handle sudden spikes in volume. You need to design the architecture to meet these requirements while minimizing cost. What should you use?

Accepted Answer

Azure Functions with a blob trigger that calls the Document Intelligence API and uses a consumption plan.. For near real-time processing with sudden spikes and minimal cost, a serverless approach with Azure Functions on a consumption plan is best. The blob trigger ensures immediate processing when documents are uploaded, and the consumption plan scales automatically and charges only for execution time. Other options introduce latency, require infrastructure management, or do not scale to zero.

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Azure Logic Apps with a recurrence trigger that polls the blob container every minute.

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Azure Kubernetes Service (AKS) with a horizontal pod autoscaler that processes documents from a queue.

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Azure Batch with a pool of virtual machines that processes documents from a queue.

Question 4

Match each Azure AI feature to the service that provides it.

Accepted Answer

Sentiment Analysis: Language Service. Azure Cognitive Services offer specialized AI capabilities: Language Service for text analysis (e.g., sentiment), Speech Service for audio processing, Vision Service for image analysis (e.g., OCR), and Translator for translation. Common confusions include swapping features between services.

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Sentiment Analysis: Speech Service

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Optical Character Recognition: Language Service

Question 5

Drag and drop the steps to implement an Azure AI Bot Service with QnA Maker into the correct order.

Accepted Answer

Create and publish a QnA Maker knowledge base, then create an Azure Bot Service bot, then connect the bot to the QnA Maker knowledge base, then test the bot.. Start with QnA Maker, build the knowledge base, create the bot, connect it, and test.

Answer

Create an Azure Bot Service bot, then create and publish a QnA Maker knowledge base, then connect the bot to the QnA Maker knowledge base, then test the bot.

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Create and publish a QnA Maker knowledge base, then create an Azure Bot Service bot, then test the bot, then connect the bot to the QnA Maker knowledge base.

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Create an Azure Bot Service bot, then create and publish a QnA Maker knowledge base, then test the bot, then connect the bot to the QnA Maker knowledge base.

Question 6

Refer to the exhibit. You deployed a custom model for Language service. Which command should you run to check if the deployment is ready to accept inference requests?

Accepted Answer

az cognitiveservices account deployment show --resource-group myRG --name myLangService --deployment-name myDeployment. The `az cognitiveservices account deployment show` command retrieves the current state of a specific deployment, including its provisioning status (e.g., 'Succeeded'). Only when the status is 'Succeeded' can the deployment accept inference requests. This is the correct command to verify readiness before sending any prediction calls.

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az cognitiveservices account deployment list --resource-group myRG --name myLangService

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az cognitiveservices account deployment delete --resource-group myRG --name myLangService --deployment-name myDeployment

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az cognitiveservices account deployment create --resource-group myRG --name myLangService --deployment-name myDeployment

Question 7

Refer to the exhibit. You execute a search query on an Azure AI Search index and get these results. The query was 'brown fox'. Why is the first result scored higher than the second?

Accepted Answer

The first document has a higher term frequency and better term proximity for the query terms. The first document is scored higher because it has a higher term frequency and better term proximity for the query terms 'brown fox'. In Azure AI Search's default scoring profile, documents that contain the query terms more frequently and closer together receive higher relevance scores.

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The first document has a higher value in a scoring profile field

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The first document is more similar to the query in vector space

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The first document was boosted by a semantic ranking function

Question 8

Your organization is using Azure AI Search with semantic ranking. Users report that search results are not showing relevant documents at the top. You need to improve relevance. What should you configure?

Accepted Answer

Enable semantic search configuration on the index. Semantic search configuration is required to enable semantic ranking, which uses deep learning models to re-rank search results based on contextual relevance rather than just keyword matching. Without this configuration, the index cannot leverage semantic ranking even if the service tier supports it, so enabling it directly addresses the user's complaint about irrelevant documents appearing at the top.

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Add synonyms to the index

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Define a custom scoring profile

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Change the index analyzer to a different language analyzer

Question 9

You are building a conversational AI system using Azure OpenAI Service. The system must maintain context across multiple user turns. Which parameter determines how many previous messages are considered for the next response?

Accepted Answer

The length of the messages array in the API call. The Azure OpenAI Service API uses a `messages` array in the request body to represent the conversation history. Each entry in this array corresponds to a previous turn (with roles like 'user', 'assistant', or 'system'), and the length of this array directly determines how many prior messages are considered when generating the next response. By including more messages, you extend the context window; by truncating the array, you limit it.

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max_tokens

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temperature

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top_p

Question 10

You are deploying a custom Azure AI Language question answering project. The solution must only answer questions based on a specific set of internal FAQ documents. Which data source type should you use when creating the project?

Accepted Answer

URLs or files containing FAQ content. Azure AI Language custom question answering is designed to ingest structured FAQ content from URLs or files. When you create a custom project, selecting 'URLs or files containing FAQ content' as the data source type allows the service to automatically extract question-answer pairs from the provided documents, ensuring the solution only answers questions based on that specific set of internal FAQs.

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Prebuilt model from Azure AI Language

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Azure SQL Database with a QnA Maker schema

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Azure Cognitive Search index

Question 11

Your company uses Azure OpenAI Service to generate product descriptions. You need to ensure that the generated content does not include offensive language and adheres to responsible AI principles. What should you implement?

Accepted Answer

Configure content filters in Azure OpenAI. Content filters in Azure OpenAI allow you to define categories (e.g., hate, violence, self-harm) and severity levels (low, medium, high) to automatically block or flag offensive language in generated outputs. This directly enforces responsible AI principles by preventing harmful content from being surfaced to users, without requiring model retraining or encryption changes.

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Enable customer-managed key encryption

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Fine-tune the model with a curated dataset

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Set usage limits and throttling

Question 12

Your organization uses Azure OpenAI Service with a data source configured as 'Azure OpenAI on your data'. You notice that the responses include outdated information even though the underlying data source has been updated. What is the most likely cause?

Accepted Answer

The index in Azure Cognitive Search has not been refreshed. When using Azure OpenAI Service with 'Azure OpenAI on your data', the responses are generated by querying an Azure Cognitive Search index that contains your data. If the underlying data source has been updated but the responses still include outdated information, the most likely cause is that the index in Azure Cognitive Search has not been refreshed to reflect those updates. The model itself does not store or cache the data; it relies on the index at query time, so an outdated index directly leads to outdated responses.

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The model is using a cached version of the prompt

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The data source is configured to sync only daily

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The Azure CDN is caching the responses

Question 13

You are building a solution that must translate customer chat messages from Spanish to English in real-time. The solution must also detect the language of incoming messages to handle cases where users write in other languages. Which TWO Azure AI service features should you use?

Accepted Answer

Azure AI Translator - Language Detection. Azure AI Translator's Language Detection feature (option A) is correct because it identifies the language of incoming chat text, which is exactly what is needed to handle messages written in languages other than Spanish. Azure AI Translator's Translation feature (option B) is correct because it performs the actual text-to-text translation from Spanish to English in real time. Together, these two features satisfy both requirements: detecting the source language and translating the chat messages. Option C (Azure AI Speech - Speech Translation) is not appropriate because the scenario involves chat text, not spoken audio. Option D (LUIS) is a natural language understanding service for intent and entity extraction, not translation or language identification. Option E (Custom Question Answering) builds FAQ-style knowledge bases and does not provide translation or language detection.

Answer

Azure AI Speech - Speech Translation

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Azure AI Language Understanding (LUIS)

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Azure AI Custom Question Answering

Question 14

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.. 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.

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Use a different OCR API from Azure Cognitive Services.

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Train a custom OCR model using Azure Custom Vision.

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Increase the confidence threshold for text detection.

Question 15

Refer to the exhibit. You are creating an Azure Cognitive Services account using an ARM template snippet. What type of account is being created?

Accepted Answer

Azure AI Services multi-service account. The ARM template snippet uses the 'CognitiveServices' resource type and sets 'kind' to 'CognitiveServices', which provisions a multi-service account that provides access to multiple Azure AI services (e.g., Language, Computer Vision, Translator) under a single endpoint and key. This is distinct from single-service accounts, which use specific 'kind' values like 'TextAnalytics' or 'ComputerVision'.

Answer

Azure AI Language

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Azure AI Computer Vision

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Azure OpenAI Service