AI-102 Implement natural language processing solutions Practice Test 7 — 15 Questions

Question 1

You need to analyze customer call transcripts to identify positive and negative sentiment. 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. For customer call transcripts, this feature analyzes each sentence or document and returns a sentiment label and confidence scores, directly addressing the requirement to identify positive and negative sentiment.

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Language Detection

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Named Entity Recognition

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Key Phrase Extraction

Question 2

You are designing a solution that must extract personally identifiable information (PII) from medical records stored in Azure Blob Storage. The solution must redact the PII before storing the results. Which combination of Azure services should you use?

Accepted Answer

Use Azure AI Language's PII detection feature and a custom Azure Function to redact.. Azure AI Language's PII detection feature is specifically designed to identify and categorize PII entities in text, and combining it with a custom Azure Function allows you to programmatically redact those entities before storing the results in Blob Storage. This provides a serverless, scalable pipeline that meets the requirement of extracting and redacting PII from medical records without manual intervention.

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Use Azure AI Search with cognitive skills for PII detection.

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Use Azure OpenAI to detect and redact PII.

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Use Text Analytics for Health and then manually redact.

Question 3

Which TWO actions can you take to improve the performance of a Conversational Language Understanding model?

Accepted Answer

Add more varied utterances to each intent.. Adding more varied utterances to each intent directly improves the model's ability to generalize and correctly classify user input by exposing it to a wider range of phrasing, synonyms, and sentence structures. This reduces overfitting to specific word patterns and increases the likelihood of accurate predictions on unseen data.

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Reduce the number of intents.

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Change the Azure region of the resource.

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Disable active learning.

Question 4

Which THREE factors should you consider when selecting a region for an Azure AI Language resource?

Accepted Answer

Data residency requirements.. Data residency requirements (Option C) are critical when selecting an Azure region for an AI Language resource because Azure AI Language processes and stores customer data within the region where the resource is provisioned. Many organizations have compliance obligations (e.g., GDPR, HIPAA, or local data sovereignty laws) that mandate data remain within specific geographic boundaries, so choosing a region that aligns with these requirements is essential.

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Pricing differences.

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Availability of the Free tier SKU.

Question 5

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

Accepted Answer

Custom text classification.. Custom text classification is a core capability of the Azure AI Language service, enabling users to build and deploy custom models that classify text into user-defined categories. This feature is part of the service's suite of custom natural language processing (NLP) capabilities, distinct from pre-built features like sentiment analysis or key phrase extraction.

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Text translation.

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Speech-to-text conversion.

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Image captioning.

Question 6

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 7

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 8

You submit the request shown in the exhibit to the Azure AI Language service. What will the response contain?

Accepted Answer

Both key phrases and sentiment scores.. The exhibit shows a request to the Azure AI Language service with the `showStats` parameter set to `true` and the `opinionMining` parameter set to `true`. When `opinionMining` is enabled, the service returns both key phrases and sentiment scores (including aspect-based sentiment) for the analyzed text. Therefore, the response contains both key phrases and sentiment scores, making option C correct.

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Only extracted key phrases.

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Entity recognition results and sentiment scores.

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Only sentiment scores.

Question 9

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 10

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 11

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 12

Which ONE Azure AI service can be used to perform sentiment analysis on text?

Accepted Answer

Azure AI Language. Azure AI Language provides built-in sentiment analysis capabilities as part of its natural language processing (NLP) features, allowing you to assess the sentiment (positive, negative, neutral, or mixed) of text at the document and sentence level. Azure AI Translator does not include sentiment analysis; it only provides text translation. Therefore, only Azure AI Language is correct.

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Azure AI Search

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Azure AI Bot Service

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Azure AI Translator

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Azure AI Content Safety

Question 13

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

Accepted Answer

A project in Azure AI Language. A custom question answering project requires a project in Azure AI Language, a knowledge base containing Q&A pairs, and an endpoint for inference. A LUIS app is for language understanding, not QnA. An Azure AI Bot Service is optional for deploying a bot.

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A Language Understanding (LUIS) app

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An Azure AI Bot Service resource

Question 14

Your organization uses Azure AI Language to analyze customer support tickets. You need to ensure that personally identifiable information (PII) is detected and redacted before further processing. Which TWO features should you use?

Accepted Answer

Redaction. Redaction (Option B) is correct because it is the process of masking or removing detected PII from text, which directly meets the requirement to ensure PII is redacted before further processing. Azure AI Language provides a dedicated PII detection and redaction feature that identifies categories such as names, phone numbers, and credit card numbers, and then replaces them with placeholders like '********' or entity type labels.

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Entity linking

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Conversation summarization

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Key phrase extraction

Question 15

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