AI-102 Implement natural language processing solutions Practice Test 3 — 15 Questions

Question 1

Refer to the exhibit. You are defining a custom entity recognition model in Azure AI Language. The exhibit shows a partial configuration. What is the relationship between 'Laptop' and 'Electronics'?

Accepted Answer

Laptop is a type of Electronics.. Option A is correct. The relation type 'InstanceOf' indicates that Laptop is an instance of (i.e., a type of) Electronics. Option B is wrong because Laptop is not a part of Electronics in the model. Option C is wrong because the relation is not 'HasPart'. Option D is wrong because the relation is defined.

Answer

There is no defined relationship.

Answer

Electronics is a type of Laptop.

Answer

Laptop is a part of Electronics.

Question 2

Refer to the exhibit. You submit this request to Azure AI Language's conversational language understanding (CLU) for the 'FlightBooking' project. The model correctly identifies the intent as 'BookFlight' and extracts entities: 'Seattle' as FromCity, 'New York' as ToCity, and 'June 15th' as Date. What is the next step for the application?

Accepted Answer

Call a separate booking API with the extracted entities to complete the reservation.. Option B is correct. After CLU extracts intent and entities, the application must call a booking API to complete the action. Option A is wrong because the CLU model does not perform actions. Option C is wrong because the response already contains the entities. Option D is wrong because the intent is already identified.

Answer

Use the CLU response to directly book the flight via the Azure AI Language service.

Answer

Prompt the user to rephrase the request because the intent is ambiguous.

Answer

Send another request to CLU to confirm the booking details.

Question 3

A company deploys a custom question answering project in Azure AI Language. Users report that the bot sometimes returns irrelevant answers. The knowledge base contains hundreds of QnA pairs. You need to improve answer relevance without retraining the model. What should you do?

Accepted Answer

Add alternate phrases to existing QnA pairs for common user queries.. Option B is correct because adding alternate questions to existing QnA pairs improves the model's ability to match user queries without retraining. Option A is wrong because increasing the confidence score threshold may cause many legitimate queries to go unanswered. Option C is wrong because enabling active learning requires user feedback and does not immediately improve relevance. Option D is wrong because reducing the number of QnA pairs may remove valid content.

Answer

Increase the confidence score threshold in the project settings.

Answer

Reduce the number of QnA pairs to decrease ambiguity.

Answer

Enable active learning to let the bot suggest new questions based on user queries.

Question 4

You are building a chatbot that uses Azure AI Language to extract intents and entities from user utterances. The bot must recognize custom entities like product names that are not in the default model. Which feature should you use?

Accepted Answer

Custom named entity recognition (NER) component.. Option C is correct because a custom named entity recognition (NER) component allows you to train a model to extract custom entities. Option A is wrong because the prebuilt entity recognition only recognizes common entities. Option B is wrong because a list entity in a conversational language understanding (CLU) project is for matching exact terms, not for training a model. Option D is wrong because key phrase extraction does not extract entities.

Answer

Prebuilt entity recognition component.

Answer

Key phrase extraction.

Answer

List entity in a conversational language understanding (CLU) project.

Question 5

A company uses Azure AI Language for sentiment analysis on customer feedback. They notice that the sentiment scores for mixed reviews are often neutral when they should be slightly positive. They need to improve the accuracy for these mixed reviews without labeling new data. Which approach should you recommend?

Accepted Answer

Create and train a custom sentiment analysis model using labeled feedback data.. Option D is correct because using a custom sentiment analysis model allows the company to train on their specific data, improving accuracy for mixed reviews. Option A is wrong because increasing the sentiment threshold may misclassify some reviews. Option B is wrong because using opinion mining provides more detail but does not change the base sentiment model. Option C is wrong because the prebuilt model is general and may not perform well on specific domain data.

Answer

Adjust the sentiment threshold to require a higher confidence for neutral.

Answer

Enable opinion mining to capture aspects and opinions separately.

Answer

Use a prebuilt sentiment analysis model with a different language.

Question 6

A developer is building a multilingual chatbot using Azure AI Language. The bot must detect the user's language automatically and route the query to the appropriate language-specific model. Which Azure AI Language feature should the developer use?

Accepted Answer

Language detection API.. Option A is correct because language detection identifies the language of the input text, allowing routing to the correct model. Option B is wrong because translation changes the text but does not detect language. Option C is wrong because the multilingual CLU model handles multiple languages in one project but does not detect language. Option D is wrong because the custom text classification model is for classification, not language detection.

Answer

Translator API.

Answer

Conversational language understanding (CLU) with multilingual project.

Answer

Custom text classification model.

Question 7

A company uses Azure AI Language to analyze customer call transcripts. They need to identify specific entities such as product names and issue types. The prebuilt entity recognition does not cover their custom entities. Which approach should they take to extract both standard and custom entities from the transcripts?

Accepted Answer

Use a custom named entity recognition (NER) model for custom entities and call the prebuilt entity recognition API separately for standard entities.. Option B is correct because using a custom NER model allows extraction of custom entities, and it can be combined with prebuilt entity extraction via the API. Option A is wrong because a single CLU project is for intent and entity extraction but may not support both prebuilt and custom entities seamlessly. Option C is wrong because using only prebuilt entities misses custom entities. Option D is wrong because a custom text classification model classifies text but does not extract entities.

Answer

Use the prebuilt entity recognition API only and map standard entities to custom categories.

Answer

Use a single conversational language understanding (CLU) project with entities defined for both standard and custom entities.

Answer

Use a custom text classification model to classify the transcript and then extract entities from the classified output.

Question 8

A team is developing a solution to automatically summarize long documents using Azure AI Language. Which feature should they use?

Accepted Answer

Extractive summarization.. Option A is correct because the extractive summarization feature in Azure AI Language extracts key sentences from documents. Option B is wrong because key phrase extraction extracts phrases, not a summary. Option C is wrong because sentiment analysis determines sentiment, not summary. Option D is wrong because entity recognition identifies entities.

Answer

Sentiment analysis.

Answer

Key phrase extraction.

Answer

Entity recognition.

Question 9

A company uses Azure AI Language's conversational language understanding (CLU) to build a customer support bot. They want to integrate the bot with Microsoft Teams and need to ensure that user authentication is handled by Microsoft Entra ID. However, users report that the bot sometimes fails to respond when they are not signed into Microsoft Entra ID. What is the most likely cause?

Accepted Answer

The bot's authentication settings in Azure Bot Service require Microsoft Entra ID, but the bot is not passing the token correctly.. Option C is correct because the bot's authentication settings may require Microsoft Entra ID, but if the bot is configured to expect tokens from Microsoft Entra ID and users are not signed in, the bot cannot authenticate requests. Option A is wrong because the CLU project endpoint does not require authentication for each query; authentication is handled at the bot level. Option B is wrong because the CLU model does not require a Microsoft Entra ID token for prediction. Option D is wrong because the bot framework itself does not require Microsoft Entra ID; it can use other authentication methods.

Answer

The CLU model requires a Microsoft Entra ID token for prediction.

Answer

The CLU project endpoint is not configured to accept anonymous requests.

Answer

The bot is not registered in Microsoft Entra ID.

Question 10

A company uses Azure AI Language's custom text classification to categorize support tickets. The model was trained with 5000 labeled examples and achieves 90% accuracy. However, for a specific category (e.g., 'billing'), the model frequently misclassifies tickets that contain both billing and technical issues. Which action should you take to improve classification for this category?

Accepted Answer

Add more labeled examples for the 'billing' category, especially those that are mixed with other categories.. Option B is correct because adding more labeled examples for the 'billing' category, especially those that are mixed with other categories, will help the model learn to distinguish them better. Option A is wrong because reducing the number of categories may not address the specific confusion. Option C is wrong because increasing the training epochs may lead to overfitting. Option D is wrong because using a different algorithm is not an option in Azure AI Language's custom text classification.

Answer

Reduce the number of categories to simplify the classification.

Answer

Increase the number of training epochs to further train the model.

Answer

Use a different classification algorithm, such as a neural network.

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.. Option B is correct because the sentiment analysis API in Azure AI Language returns sentiment labels (positive, negative, neutral) and confidence scores. Option A is wrong because the language detection API detects language. Option C is wrong because the entity recognition API extracts entities. Option D is wrong because the key phrase extraction API extracts key phrases.

Answer

Language detection API.

Answer

Entity recognition API.

Answer

Key phrase extraction API.

Question 12

Which TWO actions should you take to improve the performance of a custom named entity recognition (NER) model in Azure AI Language?

Accepted Answer

Use a balanced dataset with similar numbers of examples for each entity.. Options A and C are correct because labeling more examples with entity annotations provides more training data, and using a balanced dataset helps the model learn all entities equally. Option B is wrong because increasing the training time is not a user-controlled parameter in Azure AI Language. Option D is wrong because using only prebuilt entities does not improve a custom model. Option E is wrong because reducing the number of entities may lose needed information.

Answer

Increase the training time of the model.

Answer

Reduce the number of entity types to simplify the model.

Answer

Use only prebuilt entity types to avoid training from scratch.

Question 13

Which THREE components are required to deploy a bot using Azure AI Language's conversational language understanding (CLU) and Azure Bot Service?

Accepted Answer

An Azure Bot Service bot with the CLU model as a skill.. Options A, C, and E are correct because a CLU project provides the language model, an Azure Bot Service bot handles the bot logic and channels, and a Microsoft Entra ID app registration is needed for authentication. Option B is wrong because a Translator resource is not required for CLU. Option D is wrong because a QnA Maker resource is not required (CLU handles both intents and entities).

Answer

A QnA Maker resource for FAQs.

Answer

A Translator resource for multilingual support.

Question 14

Which TWO features are available in Azure AI Language's extractive summarization?

Accepted Answer

Confidence scores for each extracted sentence.. Options A and E are correct because extractive summarization returns a ranked list of sentences and a confidence score for each sentence. Option B is wrong because it does not generate new sentences. Option C is wrong because it does not categorize sentences by sentiment. Option D is wrong because it does not identify named entities; that's entity recognition.

Answer

Identified named entities from the document.

Answer

Sentiment scores for each extracted sentence.

Answer

Generated abstractive summary.

Question 15

Refer to the exhibit. You are calling the Azure AI Language API for extractive summarization. What will be the output of this request?

Accepted Answer

The three sentences in the order they appear in the document.. Option A is correct because the request specifies 'sortBy': 'Offset', which means sentences are returned in the order they appear in the original text, not by rank. Option B is wrong because sentences are not ranked by confidence when sortBy is Offset. Option C is wrong because the request requests 3 sentences. Option D is wrong because the summary is not abstractive.

Answer

Only the first sentence because the document is short.

Answer

An abstractive summary generated from the text.

Answer

The three sentences ranked by confidence score.

AI-102 Implement natural language processing solutions Practice Test 3 — 15 Questions

Exhibit

AI-102 Implement natural language processing solutions Practice Test 3 — 15 Questions

Exhibit