AI-102 Practice Questions

Question 1

You are a solution architect at a media company. The company uses Azure AI Speech to generate subtitles for videos. The current solution uses the batch transcription API and takes several hours to process a 1-hour video. The business requires near-real-time subtitles for live streaming events. You need to design a new solution that provides low-latency transcription. You have the following options:

Option A: Use the batch transcription API with a higher priority queue.

Option B: Use the Speech-to-text REST API for real-time streaming with the Speech SDK.

Option C: Use the Azure AI Language API to transcribe audio from a file.

Option D: Use Azure AI Video Indexer to generate subtitles.

Accepted Answer

Option B. The Speech-to-text REST API with the Speech SDK supports real-time streaming transcription, which provides low-latency results suitable for live streaming events. Unlike the batch transcription API, which processes audio asynchronously and can take hours, the streaming API processes audio chunks in near-real-time, returning partial and final results with sub-second latency.

Answer

Option C

Answer

Option D

Answer

Option A

Question 2

You need to generate realistic synthetic data for training a machine learning model while ensuring the data does not contain personally identifiable information (PII). Which Azure service should you use?

Accepted Answer

Azure OpenAI Service. Azure OpenAI Service provides access to powerful generative AI models (e.g., GPT-4) that can create realistic synthetic data by learning patterns from training data. Crucially, these models can be configured to avoid memorizing or reproducing PII, and you can apply content filters and data masking to ensure the generated output is free of personally identifiable information.

Answer

Azure AI Search

Answer

Azure AI Document Intelligence

Answer

Azure AI Language

Question 3

A company uses Azure Face API to verify employee identities for building access. They need to ensure that only live faces are used, not photos or videos. Which feature should they enable?

Accepted Answer

Enable liveness detection using session-based verification.. Azure Face API's liveness detection with session-based verification is specifically designed to prevent spoofing attacks using photos, videos, or masks. It analyzes subtle cues such as micro-movements, texture, and depth to confirm the presence of a live person, ensuring that only live faces are accepted for identity verification.

Answer

Set a high confidence threshold for face matching.

Answer

Face identification with a large person group.

Answer

Face detection with attributes such as age and emotion.

Question 4

You are building a multilingual chatbot using Azure AI Language. The chatbot must handle English, Spanish, and French. You need to configure the LUIS (Language Understanding) model to support multiple languages efficiently. What is the best practice?

Accepted Answer

Create a separate LUIS app for each language and use the same intents and entities structure.. LUIS does not support a single multilingual model; each LUIS app is designed for a single primary language. To handle multiple languages efficiently, you must create a separate LUIS app per language, each with the same intent and entity schema, and route user queries to the appropriate app based on the detected language. This ensures optimal language-specific model training and accuracy.

Answer

Use the prebuilt multilingual LUIS model and fine-tune it with your intents.

Answer

Create a single LUIS app and add utterances in all three languages.

Answer

Create one LUIS app with language set to 'Multilingual' and add utterances in all languages.

Question 5

You are a data scientist for Contoso Pharmaceuticals. The company has thousands of research documents in PDF format stored in Azure Blob Storage. You need to build an Azure Cognitive Search solution that enables researchers to search for documents based on chemical compound names, disease mentions, and experimental results. The solution must extract these entities using a custom AI model built in Azure AI Language. Additionally, the solution must support semantic search for natural language queries. The search index must be updated daily with new documents. You have an existing Azure AI Language custom entity extraction model that recognizes chemical compounds and diseases. The model is deployed as an endpoint. You need to configure the enrichment pipeline. What should you do?

Accepted Answer

Create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP.. To integrate a custom AI model from Azure AI Language into an Azure Cognitive Search enrichment pipeline, you need to create a custom skill in the skillset that calls the custom entity extraction endpoint via HTTP. The built-in Entity Recognition skill only supports prebuilt models and cannot be configured to use a custom model endpoint. Deploying the model to Azure AI Document Intelligence and using a Document Intelligence skill is not appropriate because the model is already deployed in Azure AI Language as a custom entity extraction model. Field mappings in the indexer are for direct field-to-field mappings from the data source to the index, not for calling external AI services. Therefore, Option A is the correct approach.

Answer

Deploy the custom model to Azure AI Document Intelligence and use a Document Intelligence skill.

Answer

Add the custom entity extraction as a field mapping in the indexer.

Answer

Use the built-in Entity Recognition skill and configure it to use your custom model endpoint.

Question 6

A company uses Azure AI Search to index a large collection of scanned invoices stored in Azure Blob Storage. They have a skillset that includes an OCR skill to extract text from the invoices. The indexer is configured to run every night. They notice that the indexer takes a long time to complete and sometimes times out. They want to optimize the indexer performance without reducing the quality of the extracted text. What should they do?

Accepted Answer

Enable incremental enrichment and cache the enriched data to avoid reprocessing unchanged documents.. Enabling incremental enrichment and caching is the best approach to optimize indexer performance for recurring runs. It avoids reprocessing documents that have not changed, thus reducing the workload on the OCR skill and other enrichments. This maintains the quality of extracted text because only new or modified documents are processed, while unchanged documents reuse cached enrichments. Other options either reduce quality or do not address the performance bottleneck.

Answer

Switch from the OCR skill to the Text Merge skill to combine text from multiple pages.

Answer

Increase the indexer's batch size and reduce the number of parallel indexers.

Answer

Reduce the OCR skill's image resolution by setting the 'imageAction' to 'none' and relying on the document's embedded text.

Question 7

You need to implement a solution that searches through a collection of scanned invoices and extracts invoice numbers, dates, and total amounts. The solution must run on a schedule without manual intervention. Which Azure service should you use?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) provides prebuilt and custom models specifically designed to extract structured fields like invoice numbers, dates, and totals from scanned documents, including the prebuilt invoice model. It can be invoked programmatically on a schedule via Azure Functions, Logic Apps, or Data Factory, satisfying the no-manual-intervention requirement. This is the canonical Azure service for OCR-plus-field-extraction from forms and invoices.

Answer

Azure Bot Service

Answer

Azure AI Search with built-in skills

Answer

Azure AI Foundry model catalog

Question 8

You are designing a multilingual chatbot using Azure AI Language. The chatbot must support English, Spanish, and French. You need to minimize development effort and ensure consistent intent recognition across languages. What should you do?

Accepted Answer

Use a single CLU multilingual project that supports all three languages.. A single CLU multilingual project in Azure AI Language natively supports multiple languages within one project, allowing you to define intents and entities once and provide utterances in English, Spanish, and French. The model learns shared semantic representations across languages, so intent recognition remains consistent without building separate models or translation pipelines. This directly minimizes development effort because you maintain one project, one deployment, and one set of intent definitions.

Answer

Use the Azure AI Translator to translate all utterances to English before sending to a single English-only CLU project.

Answer

Create a separate CLU project for each language and combine them with a routing mechanism.

Answer

Use the Language Understanding (LUIS) service with a single app that includes language-specific utterances.

Question 9

Which THREE practices should be followed to secure an Azure AI solution that uses Azure OpenAI Service and Azure AI Search?

Accepted Answer

Use managed identities to authenticate between Azure OpenAI and Azure AI Search.. Option B is correct because managed identities let Azure OpenAI and Azure AI Search authenticate to each other through Microsoft Entra ID without embedding secrets or connection strings in code, eliminating credential leakage and rotation overhead. Option D is correct because requiring HTTPS with TLS 1.2 or higher protects data in transit between client applications and the AI services, preventing interception or downgrade attacks on prompts, responses, and search queries. Option E is correct because enabling firewall rules and private endpoints on the AI service endpoints removes public internet exposure and restricts traffic to approved virtual networks, which is a core network-isolation control for Azure AI workloads. Option A is not appropriate because using API keys directly in application code exposes secrets in source control, logs, and memory even if the keys are originally stored in Azure Key Vault. Option C is not appropriate because placing AI services in a DMZ subnet with public IP addresses increases the attack surface rather than securing the solution; private endpoints and restricted access are preferred.

Answer

Store API keys in Azure Key Vault but use them directly in application code.

Answer

Place all AI services in a DMZ subnet with public IP addresses.

Question 10

You manage an Azure AI Search service that indexes legal documents. The search latency is high, and you need to improve query performance without reducing index size. Which action should you take?

Accepted Answer

Increase the number of replicas. Increasing the number of replicas distributes query load across multiple copies of the index, which directly improves query throughput and reduces latency. Replicas are designed for scaling query operations without changing the index size or storage capacity.

Answer

Upgrade to a higher pricing tier

Answer

Increase the number of partitions

Answer

Reduce the number of searchable fields

Question 11

You deploy the ARM template shown in the exhibit. After deployment, you need to allow access to the Language service from your on-premises application. What should you do?

Accepted Answer

Add an IP rule with your on-premises public IP address.. The ARM template deploys an Azure Cognitive Services Language service with a network ACL that defaults to denying all traffic (defaultAction: Deny). To allow your on-premises application to access the service, you must add an IP rule that permits traffic from your on-premises public IP address. This is because the network ACL evaluates IP rules before the default action, and adding a rule with your public IP overrides the default deny for that specific source.

Answer

Remove the customSubDomainName property.

Answer

Set the defaultAction to Allow.

Answer

Change the SKU to F0 to allow public access.

Question 12

Match each Azure Cognitive Search skill to its capability.

Accepted Answer

Entity Recognition: Identifies and categorizes entities in text as people, places, organizations, etc.. Common Azure Cognitive Search skills include Entity Recognition, Key Phrase Extraction, OCR (for image text extraction), and Sentiment Analysis. The correct matches are A and B. Options C and D are mismatched definitions.

Answer

OCR: Detects language and translates text to a target language.

Answer

Sentiment Analysis: Detects the dominant language of a document.

Question 13

Match each Azure AI scenario to the appropriate service.

Accepted Answer

Building a conversational chatbot: Azure Bot Service. Correct matches: Chatbot -> Bot Service, Image moderation -> Computer Vision, Sentiment -> Text Analytics. Common confusions include associating Bot Service with image tasks or mistaking Cognitive Search for text analytics.

Answer

Building a conversational chatbot: Azure Machine Learning

Answer

Analyzing images for content moderation: Azure Bot Service

Answer

Classifying text by sentiment: Azure Cognitive Search

Question 14

You are a cloud solution architect at a legal firm. The firm needs to automate the summarization of legal documents. They have a large corpus of past case summaries and legal documents stored in Azure Blob Storage. They want to use Azure OpenAI to generate summaries for new documents. The solution must ensure that the generated summaries are accurate and do not contain hallucinated legal facts. The firm also requires that the solution be serverless and minimize operational overhead. You need to design the solution.

Option A: Use Azure OpenAI with a system message that instructs the model to be accurate. Deploy the model as a web app on Azure App Service and call it from Azure Functions triggered by new blob uploads.

Option B: Use Azure OpenAI with Retrieval-Augmented Generation (RAG) by indexing the past case summaries in Azure AI Search. Use Azure Functions to process new documents, retrieve relevant cases, and pass them as context to the model. Store summaries in Azure Cosmos DB.

Option C: Fine-tune an Azure OpenAI model on the past case summaries and deploy it as a managed endpoint. Use Azure Logic Apps to trigger summarization when new blobs are added.

Option D: Use Azure OpenAI with the chat API and provide the entire document in the prompt. Use Azure Container Instances to run a service that calls the API and writes summaries back to Blob Storage.

Which option should you choose?

Accepted Answer

Option B. It uses Retrieval-Augmented Generation (RAG) with Azure AI Search to ground the model's output in verified past case summaries, directly addressing the requirement to avoid hallucinated legal facts. The serverless architecture is achieved via Azure Functions triggered by blob uploads, minimizing operational overhead, while storing summaries in Azure Cosmos DB provides a scalable, low-latency output store.

Answer

Option A

Answer

Option D

Answer

Option C

Question 15

You need to enforce that only users from your Microsoft Entra ID tenant can call your Azure AI Language API endpoint. Which security mechanism should you configure?

Accepted Answer

Microsoft Entra ID authentication. Microsoft Entra ID authentication (formerly Azure AD) allows you to enforce that only users and applications from your specific Entra ID tenant can call the Azure AI Language API. This is achieved by configuring a managed identity or service principal and assigning it the Cognitive Services User role, which ensures that tokens issued by your tenant are required for access. API keys and IP whitelists do not provide tenant-level identity enforcement, and Azure Firewall is a network-level control that does not authenticate individual users or applications.

Answer

API key authentication

Answer

IP whitelist

Answer

Azure Firewall

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