AI-102 Implement natural language processing solutions Practice Test 11 — 15 Questions

Question 1

Which TWO capabilities are provided by the Azure AI Language service?

Accepted Answer

Custom text classification.. Option C (Custom text classification) is correct because Azure AI Language includes a custom text classification feature that lets you train models to categorize documents or text into user-defined classes. Option E (Key phrase extraction) is correct because Azure AI Language provides prebuilt key phrase extraction to identify the main talking points in unstructured text. Option A (Text translation) is not part of Azure AI Language; translation is handled by Azure AI Translator. Option B (Speech-to-text conversion) belongs to Azure AI Speech, not Azure AI Language. Option D (Image captioning) is a computer vision capability in Azure AI Vision, not Azure AI Language.

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Text translation.

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Speech-to-text conversion.

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Image captioning.

Question 2

You are testing a Conversational Language Understanding application. You send the JSON request shown in the exhibit. What is the purpose of this request?

Accepted Answer

Analyze the utterance for intent and entities.. The JSON request sends a user utterance to a Conversational Language Understanding (CLU) endpoint, which is designed to analyze natural language input. The response will include the predicted intent (e.g., 'GetWeather') and extracted entities (e.g., 'location: Seattle'), fulfilling the core function of CLU: intent and entity recognition. This is not a generative or translation task; it is a classification and extraction operation.

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Translate the text to another language.

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Generate a response to the user.

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Summarize the conversation.

Question 3

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.

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Remove the customSubDomainName property.

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Set the defaultAction to Allow.

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Change the SKU to F0 to allow public access.

Question 4

Your company uses Azure AI Language to analyze customer feedback. You need to extract key phrases from reviews in multiple languages. Which feature should you use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct feature because it is specifically designed to identify and extract the most important points or topics from text, regardless of the language. Azure AI Language's key phrase extraction supports multiple languages and returns a list of key phrases that represent the main subjects discussed in the customer feedback, which directly meets the requirement.

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Named entity recognition

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Language detection

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Sentiment analysis

Question 5

You are building a chatbot using Azure AI Language and need to handle user intents that are not covered by the predefined intents. What should you implement?

Accepted Answer

A 'None' intent in a conversational language understanding project. In a Conversational Language Understanding (CLU) project, the 'None' intent is specifically designed to capture utterances that do not match any of the defined intents. This intent acts as a catch-all for unrecognized user inputs, ensuring the chatbot can gracefully handle out-of-scope or ambiguous queries without misclassifying them into a predefined intent.

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Custom entities to capture unknown phrases

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A fallback intent in the QnA Maker knowledge base

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A prebuilt intent from the LUIS catalog

Question 6

Your organization uses Azure AI Language for custom text classification. You have deployed a model to a dedicated endpoint. After updating the training data, you retrain and redeploy the model. Users report that the endpoint still returns predictions from the old model. What is the most likely cause?

Accepted Answer

The new model is not yet deployed; you must deploy it to the endpoint. In Azure AI Language, retraining a custom text classification model does not automatically update the deployed endpoint. After training, you must explicitly deploy the new model to the endpoint using the 'Deploy model' action. Until that step is completed, the endpoint continues to serve predictions from the previously deployed model.

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The training data changes are not saved

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The project needs to be rebuilt from scratch

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The endpoint has a caching issue

Question 7

Which THREE components are required to build a custom question answering solution using Azure AI Language?

Accepted Answer

A project in Azure AI Language. Option B is correct because a custom question answering solution in Azure AI Language is created as a project (formerly a knowledge base) within the Azure AI Language resource, which holds the Q&A data and configuration. Option E is correct because the project must contain a knowledge base populated with question-and-answer pairs (or imported sources such as FAQs and documents) that the service indexes and searches. Option C is correct because after the knowledge base is built and deployed, a query endpoint is required so client applications can send questions and receive answers via the REST API or SDK. Option A is not needed because LUIS is a separate language understanding service for intent and entity extraction, not for custom question answering. Option D is not required because Azure AI Bot Service is only an optional client that can consume the endpoint; the question answering solution itself does not depend on it.

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A Language Understanding (LUIS) app

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An Azure AI Bot Service resource

Question 8

You deploy an Azure AI Services resource using the ARM template shown in the exhibit. You need to test the Language service API from your local machine. What should you do first?

Accepted Answer

Add your public IP address to the ipRules array in the networkAcls. The ARM template in the exhibit sets `defaultAction` to `Deny`, which blocks all traffic not explicitly allowed by the `ipRules` array. To test the Language service API from your local machine, you must add your public IP address to the `ipRules` array so that the resource's network firewall permits inbound requests from your IP. Without this step, all API calls from your local machine will be rejected with a 403 Forbidden error.

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Configure a managed identity for the resource

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Change the defaultAction to Allow

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Use Azure CLI to enable the resource

Question 9

You are designing a conversational AI solution using Microsoft Copilot Studio. The exhibit shows part of a topic configuration. What is the purpose of the 'triggers' section?

Accepted Answer

To define the conditions that activate the topic. In Microsoft Copilot Studio, the 'triggers' section defines the conditions (such as specific phrases, intents, or keywords) that activate a topic. This is the entry point for the conversation flow, ensuring the bot responds appropriately when a user's input matches the defined trigger phrases. Option C correctly identifies this purpose.

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To define the response the bot sends

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To define the authentication method

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To define the entities to extract

Question 10

Your company uses Azure AI Document Intelligence to process invoices. You need to extract the invoice date and total amount. Which model should you use?

Accepted Answer

Prebuilt invoice model. The Prebuilt invoice model is specifically trained on thousands of invoice documents to extract key fields like invoice date, total amount, vendor details, and line items. It uses deep learning models optimized for invoice layouts, providing higher accuracy and structured output for these fields compared to general models.

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Read model

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Layout model

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General document model

Question 11

You are building a custom named entity recognition (NER) model using Azure AI Language. After labeling 200 documents, you train the model and achieve 85% precision but only 60% recall. Which action is most likely to improve recall?

Accepted Answer

Increase the number of labeled documents, especially those containing the target entities. Low recall in a custom NER model typically indicates that the model is failing to identify many instances of the target entities. Increasing the number of labeled documents, especially those containing the target entities, provides more positive examples for the model to learn from, directly improving its ability to recognize those entities and thus boosting recall.

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Lower the confidence threshold

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Increase the training hours

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Switch to a different Azure AI Language feature

Question 12

Which TWO components are required to create a custom text classification model in Azure AI Language?

Accepted Answer

A set of labeled documents. Option A (a set of labeled documents) is correct because custom text classification in Azure AI Language is a supervised learning feature that requires training data in the form of documents tagged with the custom categories (labels) you want the model to learn. Option C (a project in Azure AI Language) is correct because you must create a custom text classification project in Azure AI Language (via Language Studio or the REST API) to hold your dataset, labels, training configuration, and deployed model. Option B (a QnA Maker knowledge base) is incorrect because QnA Maker is for building question-and-answer bots, not for training custom classification models. Option D (a Language Understanding (LUIS) app) is incorrect because LUIS is a separate conversational language understanding service for intents and entities, not custom text classification. Option E (an Azure Functions app) is incorrect because Azure Functions is a serverless compute service and is not a required component for creating or training a custom text classification model.

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A QnA Maker knowledge base

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A Language Understanding (LUIS) app

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An Azure Functions app

Question 13

Your organization needs to analyze customer call transcripts to extract key insights, including sentiment, issues, and resolution. Which THREE Azure AI Language features should you use?

Accepted Answer

Custom named entity recognition. Sentiment analysis (B) is correct because Azure AI Language's sentiment analysis feature evaluates text and returns sentiment labels and confidence scores, which directly satisfies the requirement to extract sentiment from customer call transcripts. Conversation summarization (C) is correct because it is designed to summarize conversations and extract key information such as issues and resolutions from call transcripts, matching the need to identify issues and resolution outcomes. Custom named entity recognition (A) is correct because it lets you train a model to extract domain-specific entities (for example, product names, issue categories, or resolution codes) from transcripts, which supports extracting key insights beyond generic entities. Key phrase extraction (D) is not marked correct because, while it surfaces main talking points, it does not specifically deliver sentiment, issue, or resolution extraction as required. PII detection (E) is not marked correct because it only identifies and redacts personally identifiable information and does not provide sentiment, issue, or resolution insights.

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Key phrase extraction

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PII detection

Question 14

You need to analyze the sentiment of social media posts in real time using Azure AI Language. Which approach should you use?

Accepted Answer

Call the sentiment analysis REST API for each post. The sentiment analysis REST API in Azure AI Language is designed for real-time, per-document analysis. By calling the API for each social media post as it arrives, you achieve the lowest latency and can process posts individually without batching or streaming overhead, which is essential for real-time sentiment analysis.

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Use Azure AI Search with cognitive skills

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Use the batch processing feature in Azure AI Language

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Send posts to an Event Hub and use Stream Analytics

Question 15

You are building a chatbot that must understand user intents from free-text input. You have a small set of labeled examples. Which Azure AI Language feature should you use to classify intents with minimal effort?

Accepted Answer

Conversational Language Understanding (CLU). Conversational Language Understanding (CLU) is the correct choice because it is specifically designed to extract intents and entities from free-text input in a conversational context, and it can be trained with a small set of labeled examples to classify user intents with minimal effort. CLU provides a pre-built pipeline for intent recognition and entity extraction, making it the most efficient option for building a chatbot that understands user intents.

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Entity Linking

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Custom Text Classification

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Language Detection