AI-102 Practice Test 1 — 30 Questions

Question 1

You are building a chatbot for a retail company using Azure OpenAI Service. The chatbot must provide product recommendations based on customer preferences. To ensure the chatbot does not generate harmful or inappropriate responses, you need to implement a content filtering solution. What should you use?

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

Accepted Answer

Azure OpenAI Service content filtering. Azure OpenAI Service content filtering is the correct choice because it provides built-in, configurable filters that block harmful or inappropriate content at the model level, directly within the Azure OpenAI endpoint. This ensures that the chatbot's product recommendations remain safe without requiring external services, as the filtering is applied to both input prompts and output completions based on severity levels for categories like hate, violence, and self-harm.

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Azure AI Content Safety

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Azure AI Search

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Microsoft Purview Information Protection

Question 2

You are developing a chat application that uses Azure OpenAI Service to answer customer queries. You need to ensure that the model does not generate responses containing internal company policies or confidential information. Which approach should you use?

Accepted Answer

Set the system message to instruct the model not to include confidential information.. Setting the system message in Azure OpenAI Service allows you to define high-level behavioral instructions for the model, such as prohibiting the disclosure of internal policies or confidential information. This approach leverages the model's instruction-following capability without requiring retraining or external filtering, making it a direct and effective guardrail for content generation.

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Use Azure AI Search with index filtering to exclude documents with confidential info.

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Fine-tune the model on a dataset that excludes confidential information.

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Configure Azure AI Content Safety with custom categories for confidential terms.

Question 3

You are developing a solution that uses Azure AI Language to analyze customer feedback. You need to determine whether the sentiment of a given sentence is positive, negative, or neutral. Which Azure AI Language feature should you use?

Accepted Answer

Sentiment Analysis. Sentiment Analysis is the correct Azure AI Language feature because it is specifically designed to evaluate text and determine whether the sentiment expressed is positive, negative, or neutral. This feature uses machine learning classifiers trained on large datasets to assign a sentiment label and confidence scores at the sentence and document level, directly matching the requirement to analyze customer feedback for sentiment polarity.

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Entity Recognition

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Language Detection

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Key Phrase Extraction

Question 4

A company is building a knowledge mining solution using Azure AI Search. They need to extract key phrases from a large set of documents in multiple languages. Which skill should they add to the skillset?

Accepted Answer

Key Phrase Extraction skill. The Key Phrase Extraction skill is the correct choice because it is specifically designed to identify and extract the most important phrases from text, which directly supports the requirement to extract key phrases from documents. Azure AI Search's built-in Key Phrase Extraction skill leverages natural language processing to analyze text and return a list of key phrases, making it the appropriate skill for this knowledge mining solution.

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Sentiment Analysis skill

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Language Detection skill

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Entity Recognition skill

Question 5

Refer to the exhibit. You are configuring an agent in Azure AI Foundry. The agent fails to start because the specified model is not available in the current Azure OpenAI resource. What should you do to resolve the issue?

Accepted Answer

Deploy the gpt-4-0613 model in the Azure OpenAI resource. The agent fails to start because the specified model (likely gpt-4-0613) is not deployed in the Azure OpenAI resource. In Azure AI Foundry, agents require an existing model deployment to invoke; you cannot use a model that hasn't been deployed. Option B correctly resolves this by deploying the required model in the Azure OpenAI resource.

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Modify the system_prompt to include the model version

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Change the connection_type to 'Weak'

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Change the provider to 'AzureAI'

Question 6

You manage an Azure AI Search solution that indexes documents from Azure Blob Storage. The index must support real-time updates when documents are added or modified. Which approach should you use?

Accepted Answer

Configure an indexer with a short schedule and change tracking.. Azure AI Search indexers can be configured with a short schedule (e.g., every 5 minutes) and change tracking (using high-water mark or integrated change detection on Azure Blob Storage) to automatically index new or modified documents without manual intervention. This provides near-real-time updates while leveraging the indexer's built-in change detection capabilities.

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Use the push API to manually upload documents.

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Manually reset and rerun the indexer after each change.

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Add a cognitive skillset to process new documents.

Question 7

A development team is using Azure Cognitive Service for Language to extract key phrases from customer reviews. They notice that some reviews are not being processed, and the API returns a 400 error code. What is the most likely cause?

Accepted Answer

One of the reviews exceeds the maximum character limit for a single document.. The Azure Cognitive Service for Language key phrase extraction API enforces a maximum document size of 5,120 characters per document. When a single review exceeds this limit, the API returns a 400 Bad Request error because the request payload violates the service's input constraints. This is the most common cause of 400 errors in batch text analysis operations.

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The reviews contain characters that are not valid UTF-8.

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The request contains more than 5 documents.

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The reviews are written in a language not supported by the service.

Question 8

You are developing a custom text classification model using Azure AI Language. The model must classify customer support tickets into 15 categories. You have 10,000 labeled examples. After training, the model shows 95% accuracy on the test set but only 60% on a small sample of new tickets. What is the most likely cause?

Accepted Answer

The model is overfitting to the training data.. The model's 95% accuracy on the test set versus 60% on new tickets is a classic symptom of overfitting. In Azure AI Language custom text classification, overfitting occurs when the model learns noise and idiosyncrasies of the training data rather than generalizable patterns, causing poor performance on unseen data. The high accuracy on the test set but sharp drop on new tickets indicates the model memorized the training distribution and fails to generalize.

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The model's confidence threshold is set too low.

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The training data is not representative of the new tickets.

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There is data leakage between the training and test sets.

Question 9

You need to build a chatbot that answers questions based on your company's internal knowledge base. The knowledge base consists of Word documents and PDFs. Which service should you use to create a conversational interface that retrieves answers from these documents?

Accepted Answer

Azure AI Search with Azure AI Bot Service. Azure AI Search indexes the content from Word documents and PDFs, enabling full-text and vector search over the knowledge base. Azure AI Bot Service provides the conversational interface that queries the search index and returns answers to users. Together, they form a retrieval-augmented generation (RAG) pipeline that answers questions from unstructured documents.

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Azure AI Language Service - Custom Question Answering

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Azure AI Computer Vision

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Azure AI Document Intelligence

Question 10

You are using Azure AI Language Service to extract key phrases from customer reviews. You notice that for reviews containing the word 'not good', the service sometimes extracts 'good' as a key phrase. What is the most likely reason?

Accepted Answer

Key phrase extraction does not consider negation. Key phrase extraction in Azure AI Language Service uses a statistical model that identifies significant terms based on frequency and context, but it does not inherently understand negation. When the phrase 'not good' appears, the model may still extract 'good' as a key phrase because it recognizes 'good' as a high-value term, ignoring the negation. This is a known limitation of the feature, as it focuses on noun phrases and important terms rather than sentiment or negated constructs.

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The language detection model misidentified the language

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You need to set a confidence threshold to exclude negative phrases

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The service is not trained on your specific domain

Question 11

You are building an Azure AI Search solution to index a collection of technical manuals. Users need to find documents by searching for specific terms and also have the ability to filter by document category. Which feature should you configure in the index to support filtering?

Accepted Answer

Set the 'filterable' property to true on the category field. Setting the 'filterable' property to true on the category field enables Azure AI Search to apply OData filter expressions (e.g., `$filter=category eq 'Networking'`) during query execution. This allows users to narrow search results by document category without requiring the field to be full-text searchable, which is essential for efficient filtering in a technical manuals index.

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Set the 'facetable' property to true on the category field

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Set the 'searchable' property to true on the category field

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Set the 'sortable' property to true on the category field

Question 12

You are building a custom question answering solution using Azure AI Language. Which TWO actions are required to deploy the solution?

Accepted Answer

Create a Custom Question Answering project in Azure AI Language.. A Custom Question Answering project is the container for your question-answer pairs, synonyms, and active learning settings within Azure AI Language. You must create this project to define the knowledge base that the solution will query against.

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Train a custom model using the Azure AI Language training API.

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Create a QnA Maker service in the Azure portal.

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Publish the project to a web app bot.

Question 13

You are reviewing a skillset definition for an Azure AI Search indexer. The indexer is configured to index 1000 PDF documents. After running the indexer, you notice that only 500 documents have sentiment scores. What is the most likely cause?

Accepted Answer

The SplitSkill outputs are not correctly mapped to the SentimentSkill input; the skill runs only on the first page of each document. The SplitSkill divides each PDF into pages, and by default the SentimentSkill runs in the context of each page (e.g., /document/pages/*). If the SplitSkill outputs are not correctly mapped to the SentimentSkill input, the sentiment skill may only process the first page or fail to receive the split content, resulting in sentiment scores for only a subset of documents. Correct mapping ensures that each page's content is passed to the SentimentSkill for analysis.

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The skills are defined in the wrong order; sentiment should run before split

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The SentimentSkill is not supported in this region

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The context of the SentimentSkill should be "/document" instead of "/document/pages/*"

Question 14

Refer to the exhibit. You receive this error when calling an Azure Cognitive Services API. What is the most likely cause?

Accepted Answer

The subscription key is not valid for this Cognitive Services resource.. The error indicates that the subscription key provided is not associated with the Cognitive Services resource being accessed. In Azure Cognitive Services, each resource has a unique set of keys, and using a key from a different resource (even in the same region) will result in a 401 Unauthorized error with this specific message. Option D is correct because the key must match the resource's endpoint exactly.

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The API endpoint is incorrect.

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The subscription key has expired.

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The subscription key is from a different region.

Question 15

You need to summarize a large document using Azure AI Language. Which feature should you use?

Accepted Answer

Document summarization. Document summarization is the correct feature because it is specifically designed to generate concise summaries of large documents, extracting the most important information. Azure AI Language's document summarization uses extractive or abstractive techniques to produce a summary, directly addressing the requirement to summarize a large document.

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Key phrase extraction

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Entity recognition

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Sentiment analysis

Question 16

You are designing an agentic solution that uses Microsoft Copilot Studio and Azure AI Search. The agent needs to answer questions based on confidential documents. Which security measure should you implement to ensure the agent only accesses documents the user has permission to read?

Accepted Answer

Implement document-level security using security filters in the search index.. Azure AI Search supports document-level security through security filters, which allow you to restrict search results based on the user's identity. By storing security identifiers (e.g., group memberships or user IDs) as a field in the index and applying an OData filter at query time, the agent can ensure users only see documents they are permitted to read. This is the standard approach for implementing row-level security in Azure AI Search.

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Disable public network access on the Azure AI Search service.

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Use a managed identity for the agent to access the search index.

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Require multi-factor authentication for all users.

Question 17

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.

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

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Sentiment Analysis: Detects the dominant language of a document.

Question 18

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.

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Building a conversational chatbot: Azure Machine Learning

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Analyzing images for content moderation: Azure Bot Service

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Classifying text by sentiment: Azure Cognitive Search

Question 19

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.

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OCR (legacy)

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Facial detection

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Image tagging

Question 20

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.

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API key authentication

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IP whitelist

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Azure Firewall