AI-102 Quick Quiz — 10 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