AI-102 Implement natural language processing solutions Practice Test 4 — 15 Questions

Question 1

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

Accepted Answer

Extractive summarization.. Extractive summarization is the correct feature because it specifically identifies and extracts the most important sentences from a document to create a concise summary. Azure AI Language's extractive summarization uses a ranking model to score sentences based on relevance and informativeness, directly addressing the requirement to automatically summarize long documents.

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Sentiment analysis.

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Key phrase extraction.

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Entity recognition.

Question 2

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.. The bot's authentication settings in Azure Bot Service require Microsoft Entra ID tokens for user authentication, but the bot is not passing the token correctly. When users are not signed into Microsoft Entra ID, the bot fails to respond because it cannot validate the user's identity via the missing or malformed token. The CLU service itself does not require tokens for prediction; the issue lies in the bot's authentication flow, not the CLU endpoint configuration.

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

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

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The bot is not registered in Microsoft Entra ID.

Question 3

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.. 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.

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Reduce the number of categories to simplify the classification.

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

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

Question 4

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.. The Sentiment Analysis API is specifically designed to evaluate text and return sentiment labels (positive, negative, neutral) along with confidence scores. This directly matches the requirement to determine whether each customer review expresses positive, negative, or neutral sentiment.

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Language detection API.

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Entity recognition API.

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Key phrase extraction API.

Question 5

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.. A balanced dataset ensures the model learns to recognize all entity types equally, preventing bias toward overrepresented entities. In Azure AI Language custom NER, the model's performance depends on the quality and distribution of labeled data; an imbalanced dataset can lead to poor recall for minority entities.

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Increase the training time of the model.

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Reduce the number of entity types to simplify the model.

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Use only prebuilt entity types to avoid training from scratch.

Question 6

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.. Azure Bot Service can integrate a CLU model as a skill, enabling the bot to route user intents to the CLU service for natural language understanding. This is done by configuring the bot's skill endpoint to point to the CLU project's prediction URL, allowing the bot to leverage the trained model for intent classification and entity extraction.

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A QnA Maker resource for FAQs.

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A Translator resource for multilingual support.

Question 7

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

Accepted Answer

Confidence scores for each extracted sentence.. Azure AI Language's extractive summarization returns a confidence score for each extracted sentence, indicating the model's certainty that the sentence is important. Option D is correct because the feature outputs a ranked list of sentences extracted from the source document, ordered by their relevance scores.

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Identified named entities from the document.

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Sentiment scores for each extracted sentence.

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Generated abstractive summary.

Question 8

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.. The Azure AI Language API for extractive summarization returns the most relevant sentences from the original document in the order they appear, not reordered by score. The API extracts sentences based on a ranker, but the output preserves the original sentence sequence to maintain readability and context. Option D is correct because the default behavior is to return the extracted sentences in their original order.

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Only the first sentence because the document is short.

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An abstractive summary generated from the text.

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The three sentences ranked by confidence score.

Question 9

Refer to the exhibit. You are calling the Azure AI Language API for conversational language understanding (CLU). The CLU project 'SupportBot' has an intent 'CancelOrder' with an entity 'OrderNumber' of type 'Number'. The deployment 'production' is active. What is the expected output?

Accepted Answer

The response will contain the top intent 'CancelOrder' and the entity 'OrderNumber' with value '12345'.. D is correct because the CLU project 'SupportBot' has a defined intent 'CancelOrder' with an entity 'OrderNumber' of type 'Number'. The 'Number' entity type in Azure AI Language automatically extracts numeric values from the utterance, stripping non-numeric characters like the hash (#). Since the deployment is active, the response returns the top intent and the entity with the numeric value '12345'.

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The response will contain an error because the deployment is not active.

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The response will contain the entity 'OrderNumber' with value '#12345' because the hash is part of the entity.

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The response will contain only the top intent, but no entities because the entity type is not recognized.

Question 10

Refer to the exhibit. You are calling the Azure AI Language API for entity linking. What is the primary purpose of this request?

Accepted Answer

To identify entities in the text and link them to a knowledge base.. The request is configured for entity linking, which is a specific capability of the Azure AI Language API that identifies named entities in the text and resolves them to a unique identifier in a knowledge base (such as Wikipedia or a custom knowledge graph). This goes beyond simple named entity recognition (NER) by providing a canonical link, enabling disambiguation of entities with the same name (e.g., 'Washington' as a state vs. a person). The response includes both the entity name and a URL to the knowledge base entry, confirming the primary purpose is linking to a knowledge base.

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To extract named entities from the text without linking.

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To extract key phrases from the text.

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To analyze the sentiment of the text.

Question 11

A company is building a chatbot that must handle user queries in multiple languages. The chatbot uses Azure AI Language Service. Which feature should be used to detect the language of incoming messages before routing them to the appropriate language model?

Accepted Answer

Language Detection. Language Detection is the correct feature because it is specifically designed to identify the language of text input, returning a language name and a confidence score. In a multi-language chatbot, this detection step is essential to route the query to the appropriate language-specific model or handler. Azure AI Language Service provides a dedicated pre-built capability for language detection, which can be called via the REST API or SDK.

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Sentiment Analysis

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Key Phrase Extraction

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Entity Recognition

Question 12

A company uses Azure AI Language Service to analyze customer feedback. They notice that the sentiment scores for negative reviews are often incorrectly labeled as neutral. Which configuration should be adjusted to improve accuracy?

Accepted Answer

Create a custom sentiment analysis model using Custom Text Classification. The Azure AI Language Service's pre-built sentiment analysis model may not capture domain-specific nuances in customer feedback, leading to misclassification of negative reviews as neutral. By creating a custom sentiment analysis model using Custom Text Classification (option C), you can train the model on your labeled data to improve accuracy for your specific use case.

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Deploy the Language service in a different Azure region

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Increase the confidence threshold for sentiment classification

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Enable Key Phrase Extraction to preprocess the text

Question 13

A healthcare organization deploys an Azure AI Language Service custom entity recognition model to extract medical conditions from clinical notes. During testing, the model fails to recognize rare diseases mentioned in the training data. What is the most likely cause?

Accepted Answer

The model requires more labeled examples of rare diseases. The custom entity recognition model in Azure AI Language Service requires sufficient labeled examples for each entity type to learn patterns effectively. Rare diseases with few training examples will have lower recall because the model lacks the statistical evidence to generalize those entities. This is a classic data sparsity issue in supervised NLP models.

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The entity length exceeds the maximum allowed for the model

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The model is overfitting to common diseases

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The training data is imbalanced with too many common diseases

Question 14

A company is developing a conversational AI solution using Microsoft Copilot Studio. They want the copilot to answer questions based on a knowledge base of technical documents. Which data source integration should they use?

Accepted Answer

Azure AI Search. Azure AI Search is the correct data source because it provides a search index that can be queried by Copilot Studio using the 'Azure AI Search' connector. This allows the copilot to perform semantic or keyword-based retrieval over indexed technical documents, enabling accurate question-answering from a knowledge base. Copilot Studio natively supports Azure AI Search as a data source for generative answers, making it the optimal choice for this scenario.

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Azure Blob Storage

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Azure SQL Database

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Microsoft Lists

Question 15

A company uses Azure AI Language Service for custom text classification. The model is trained to classify support tickets into categories. After deployment, the model performs well on the test set but poorly on new incoming tickets. Which action should be taken to improve generalization?

Accepted Answer

Add more labeled data from actual production tickets. Adding more labeled data from actual production tickets helps the model learn the true distribution of real-world inputs, reducing overfitting to the test set. The model's poor performance on new tickets indicates it memorized patterns specific to the training data rather than generalizing. Incorporating production data directly addresses the distribution shift between the test set and live traffic.

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Switch to a prebuilt text classification model

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Increase the number of training epochs

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Reduce the confidence threshold for classification