AI-102 Practice Exam 61 — 20 Questions

Question 1

You are the AI engineer at a global e-commerce company that allows users to upload product images and descriptions. You use Azure Content Moderator to automatically moderate images for adult and racy content, and text for profanity and personal data. Recently, you noticed that some product descriptions containing profanity in French are not being flagged. Your Content Moderator text moderation API call includes the language parameter set to 'eng'. The profanity list appears to be English-only. You have a requirement to support French and Spanish in addition to English. You also need to ensure that false positives for legitimate product descriptions are minimized. You cannot use a custom term list because the profanity terms are dynamic. What should you do?

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

Accepted Answer

Set the language parameter to 'auto-detect' in the text moderation API request.. Setting the language parameter to 'auto-detect' allows the Azure Content Moderator text moderation API to automatically identify the language of the input text and apply the corresponding built-in profanity list (including French and Spanish). This meets the requirement to support multiple languages without using a custom term list, and it minimizes false positives by using the appropriate language-specific moderation model rather than a generic English-only list.

Answer

Disable the language parameter so that the API defaults to all languages.

Answer

Create separate API calls for each language and specify the language code in the request.

Answer

Add French and Spanish profanity terms to a custom term list and use the list in the API call.

Question 2

You are implementing a solution that uses Azure AI Vision to analyze images in a batch processing pipeline. The pipeline processes thousands of images daily. You need to minimize cost while ensuring that all images are processed within 24 hours. Which service tier should you choose?

Accepted Answer

Standard S1 tier. The Standard S1 tier is the correct choice because it provides the necessary throughput (10 transactions per second) and capacity to process thousands of images within 24 hours at the lowest cost among the paid tiers. The Free F0 tier is limited to 20 transactions per minute and 5,000 transactions per month, which is insufficient for batch processing at scale. The S0 and S2 tiers are either deprecated or offer higher throughput at a higher cost, making S1 the most cost-effective option for this workload.

Answer

Free tier F0

Answer

Standard S0 tier

Answer

Standard S2 tier

Question 3

You are deploying a Custom Vision object detection model to an Azure Container Instance for real-time inference. The model must respond within 500 ms. The default container runs on CPU. What should you do to meet the latency requirement?

Accepted Answer

Export the model as a Dockerfile with GPU support and deploy to a GPU-enabled ACI.. The default Custom Vision container runs on CPU, which is insufficient for real-time object detection inference within 500 ms. Exporting the model as a Dockerfile with GPU support and deploying to a GPU-enabled Azure Container Instance (ACI) leverages NVIDIA CUDA-accelerated inference, dramatically reducing latency to meet the sub-500 ms requirement.

Answer

Increase the number of CPU cores in the container instance.

Answer

Deploy the model to Azure Functions with a Premium plan.

Answer

Use the Cognitive Services Computer Vision container instead.

Question 4

Which TWO Azure AI services can perform optical character recognition (OCR)?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) includes a prebuilt read model that extracts printed and handwritten text from documents, making it a valid OCR service. The Read API, part of Azure AI Vision, is specifically designed for OCR and can extract text from images and PDFs. Both services provide optical character recognition capabilities, though they target different use cases: Document Intelligence focuses on structured document analysis, while Read API is a general-purpose OCR engine.

Answer

Custom Vision

Answer

Face API

Answer

Video Indexer

Question 5

You are building an agentic solution using Microsoft Semantic Kernel. The agent needs to orchestrate multiple plugins. One plugin returns a large dataset that exceeds the model's context window. What is the best way to handle this?

Accepted Answer

Use a summarization plugin to condense the data before passing it to the model. Semantic Kernel agents interact with LLMs that have fixed context windows. When a plugin returns data that exceeds this limit, the best practice is to use a summarization plugin (e.g., a built-in Semantic Kernel text summarizer or a custom one) to condense the data into a concise representation that fits within the model's token budget. This preserves the essential information without losing context or requiring manual truncation, which could discard critical details.

Answer

Split the data into multiple smaller API calls and combine results

Answer

Truncate the data to fit the context window

Answer

Configure the plugin to return only a subset of the data and mark the rest as sensitive

Question 6

A company uses Azure Content Moderator to moderate text in a chat application. They want to automatically reject messages that contain profanity or personal data. Which API should they use?

Accepted Answer

Text Moderation API. The Text Moderation API (D) is the correct choice because it is specifically designed to scan text content for profanity, personally identifiable information (PII), and other unwanted text patterns. This API returns a moderation score and a list of detected terms, enabling automated rejection of messages that violate the defined policies.

Answer

Review API

Answer

Video Moderation API

Answer

Image Moderation API

Question 7

A company is building a custom question-answering solution using Azure AI Language. They need to ensure that the model can provide answers from a set of internal documents, but only to authenticated users from the company's Azure Active Directory tenant. The solution should minimize latency and cost. Which deployment option should the team choose?

Accepted Answer

Deploy a custom question-answering model using a dedicated Azure AI Language resource with a private endpoint and managed identity.. Deploying a custom question-answering model with a dedicated Azure AI Language resource, a private endpoint, and managed identity ensures that only authenticated users from the company's Azure AD tenant can access the solution via private network connectivity, minimizing latency by avoiding public internet routing and reducing cost by using a dedicated (not serverless) resource that can be right-sized.

Answer

Use the public endpoint of Azure AI Language with Azure AD token-based authentication.

Answer

Deploy a serverless endpoint with Azure Functions and Azure Cognitive Search.

Answer

Use the prebuilt question-answering model from Azure AI Language with a custom answer list.

Question 8

Which TWO actions should you take to optimize the performance of an Azure AI Search solution that indexes large volumes of data?

Accepted Answer

Use the appropriate search tier (S1, S2, etc.) based on document size. Choosing the appropriate search tier (S1, S2, etc.) ensures that the service has sufficient resources (CPU, memory, disk I/O) to handle the indexing load and document size. Higher tiers provide better indexing throughput and storage capacity, which is critical for large volumes of data. Using an undersized tier can lead to throttling, timeouts, or failed indexing operations.

Answer

Use the free tier for production workloads

Answer

Increase the replica count for better indexing throughput

Answer

Disable scoring profiles to speed up indexing

Question 9

A company wants to build a customer support agent using Microsoft Copilot Studio. The agent needs to understand natural language and handle complex queries beyond simple keyword matching. The agent should be able to escalate to a human agent when it cannot resolve the issue. Which feature should the agent use to understand natural language?

Accepted Answer

Enable generative answers and configure a knowledge source.. Generative answers in Microsoft Copilot Studio use large language models (LLMs) to interpret natural language queries and generate responses based on configured knowledge sources (e.g., SharePoint, websites, or custom data). This enables the agent to handle complex, conversational queries beyond simple keyword matching, and it can escalate to a human agent when confidence is low or the issue cannot be resolved.

Answer

Create a Power Automate flow to process queries.

Answer

Create topics with trigger phrases.

Answer

Use entities to extract key information.

Question 10

Match each Azure Cognitive Services endpoint to its purpose.

Accepted Answer

Computer Vision: Analyze images to extract visual features, descriptions, and objects.. The correct matches are: Computer Vision -> image analysis, Text Analytics -> sentiment analysis, Form Recognizer -> form data extraction, Face API -> face detection and attributes. Common confusions include mixing up Computer Vision with Translator, and Text Analytics with Content Moderator.

Answer

Computer Vision: Translate text between different languages.

Answer

Text Analytics: Moderate text content for offensive language.

Question 11

You are using Azure AI Language to analyze customer reviews. You need to determine whether each review expresses a positive, negative, or neutral sentiment. Which API should you call?

Accepted Answer

Sentiment analysis API.. The Sentiment Analysis API is specifically designed to evaluate text and return sentiment labels (positive, negative, neutral) along with confidence scores. This directly matches the requirement to determine whether each customer review expresses positive, negative, or neutral sentiment.

Answer

Language detection API.

Answer

Entity recognition API.

Answer

Key phrase extraction API.

Question 12

You are testing an Azure OpenAI Service chat completion with function calling. The assistant returned null content and a tool call. What does this indicate?

Accepted Answer

The model is using a tool to answer the question, so content is null. In Azure OpenAI Service function calling, when the model determines that it needs to invoke a tool (e.g., an external API or database query) to fulfill the user's request, it returns a `tool_calls` object with `content` set to `null`. This is the expected behavior—the model is signaling that it is delegating the response to the tool, not that it has failed or is blocked.

Answer

The model is unable to answer the question and returned an error

Answer

The system message is preventing the model from responding

Answer

The function call syntax is invalid and the model skipped it

Question 13

You are deploying a chat application using Azure OpenAI. The application should only answer questions based on a specific set of internal documents. Which THREE features should you use?

Accepted Answer

Azure AI Search index with the internal documents. Azure AI Search indexes allow you to ingest internal documents and perform vector or hybrid search over them. When integrated with Azure OpenAI, the search results are used as grounding context for the model, ensuring responses are based solely on your data.

Answer

Content filters to block out-of-domain questions

Answer

Fine-tuning the model on the internal documents

Question 14

You are troubleshooting an Azure AI Vision application that calls the Analyze Image API. The application suddenly returns HTTP 403 errors. The API key and endpoint have not changed. What is the most likely cause?

Accepted Answer

The API key has been regenerated or the resource is in a different region.. HTTP 403 errors indicate the server understood the request but refused to authorize it. Since the API key and endpoint have not changed from the client's perspective, the most likely cause is that the key was regenerated in the Azure portal or the resource was moved to a different region, invalidating the previously valid key. This is a common authorization failure distinct from quota or throttling issues.

Answer

The image file size exceeds the maximum limit.

Answer

The service is throttling requests due to high volume.

Answer

The API call quota has been exceeded.

Question 15

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.

Answer

Sentiment Analysis: Speech Service

Answer

Optical Character Recognition: Language Service

Question 16

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.

Answer

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.

Answer

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 17

You are developing a chatbot that uses Azure AI Language to understand user intents. The chatbot must handle multiple languages and direct users to the appropriate support team based on the detected intent. Which Azure AI Language feature should you use?

Accepted Answer

Conversational language understanding (CLU). Conversational language understanding (CLU) is the correct feature because it is specifically designed to extract intents and entities from user utterances in a multi-language conversational context. CLU enables the chatbot to detect the user's intent (e.g., 'billing', 'technical support') and route them to the appropriate support team, while also supporting multiple languages through its language-agnostic model training and per-language project configurations.

Answer

Text Analytics

Answer

QnA Maker

Answer

Translator

Question 18

You are building a generative AI application that uses Azure OpenAI Service. The application must handle sensitive user data and comply with GDPR. You need to ensure that the service does not store or log user prompts and completions. What should you configure?

Accepted Answer

Disable data logging in the Azure OpenAI Service resource. Azure OpenAI Service provides a data logging toggle that, when disabled, ensures Microsoft does not store or process your prompts and completions for monitoring or improvement purposes. This is essential for GDPR compliance when handling sensitive user data, as it prevents any retention of personal data in the service's logs.

Answer

Deploy the resource in a GDPR-compliant region

Answer

Configure content filtering to block sensitive data

Answer

Use a custom endpoint that anonymizes data

Question 19

You are using Azure AI Language to analyze sentiment in customer feedback. The analysis returns a sentiment label of 'mixed' for a review that contains both positive and negative statements. The overall sentiment score is 0.75 (positive). What does this indicate?

Accepted Answer

The text contains both positive and negative sentiments.. The 'mixed' sentiment label indicates that the text contains both positive and negative statements. The overall sentiment score of 0.75 (positive) reflects the aggregate confidence that the text is positive, but the label 'mixed' is assigned when the model detects significant conflicting sentiments, meaning the text is not uniformly positive or negative. This is a key behavior of Azure AI Language's sentiment analysis, which provides both a label and a score for the overall document.

Answer

The text is negative overall.

Answer

The text is neutral overall.

Answer

The analysis is inconclusive.

Question 20

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.

Answer

The model is using a cached version of the prompt

Answer

The data source is configured to sync only daily

Answer

The Azure CDN is caching the responses