AI-900 Describe features of Natural Language Processing workloads on Azure Practice Test 3 — 15 Questions

Question 1

A healthcare organization needs to automatically redact personally identifiable information (PII) such as patient names and addresses from unstructured medical notes, while keeping clinical terms like disease names and medications. They want to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

PII detection and Text Analytics for Health. PII detection identifies and redacts personally identifiable information like patient names and addresses, while Text Analytics for Health extracts clinical entities such as diseases and medications from unstructured medical notes. Both are prebuilt Azure AI Language features that require no custom training, making them ideal for this healthcare redaction scenario.

Answer

Key phrase extraction and sentiment analysis

Answer

Named Entity Recognition (NER) and language detection

Answer

Entity linking and conversational language understanding

Question 2

A travel agency wants to build a chatbot that can automatically answer customer questions about flight status by extracting answers from a PDF document containing FAQs. Which Azure AI Language feature should they use to directly query this content?

Accepted Answer

Question Answering. Question Answering, is correct because it is specifically designed to extract answers directly from a provided document (such as a PDF FAQ) by using a pre-built or custom knowledge base. The travel agency can upload the PDF, and the service will return precise answers to user queries without requiring intent classification or entity extraction, which is exactly what is needed for querying flight status from a static FAQ document.

Answer

Conversational Language Understanding (CLU)

Answer

Text Analytics for health

Answer

Translator

Question 3

A customer service team wants to analyze thousands of call transcripts to identify common complaints and understand whether customer sentiment is positive, negative, or neutral. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

Accepted Answer

Key phrase extraction and sentiment analysis. The customer service team needs to identify common complaints (which requires extracting key phrases from the call transcripts) and understand sentiment polarity (positive, negative, or neutral). Azure AI Language's prebuilt key phrase extraction returns the main talking points and recurring terms, while sentiment analysis assigns a sentiment score and labels per sentence or document. Both features are available out-of-the-box without any custom training, directly meeting the stated requirements.

Answer

Entity recognition and text translation

Answer

Language detection and summarization

Answer

PII detection and conversation analysis

Question 4

A law firm needs to automatically extract specific information from legal contracts, such as the names of the parties involved, effective dates, and governing law clauses. The firm has a small set of contracts that have been manually annotated with these specific fields. Which Azure AI Language feature should they use to build a custom extraction solution?

Accepted Answer

Custom Named Entity Recognition (NER). Custom Named Entity Recognition (NER) is the correct choice because the law firm needs to extract specific, custom fields (party names, effective dates, governing law clauses) from legal contracts, which are not covered by prebuilt entity categories. Custom NER allows you to train a model using a small set of manually annotated contracts to recognize these domain-specific entities, enabling tailored extraction for the firm's unique requirements.

Answer

Prebuilt Named Entity Recognition (NER)

Answer

Key Phrase Extraction

Answer

Text Summarization

Question 5

A legal firm needs to automatically process thousands of court documents. The system must identify and redact sensitive personal information such as names, addresses, and social security numbers. Additionally, it must extract legal-specific entities like case numbers, judge names, and statute references. The firm has a small set of manually annotated documents with these legal entities. Which combination of Azure AI Language features should they use?

Accepted Answer

PII detection and custom named entity recognition (NER). The firm needs both PII detection to redact sensitive personal information and custom NER to extract legal-specific entities like case numbers and judge names from a small set of annotated documents. Azure AI Language provides a prebuilt PII detection feature for common sensitive data and a custom NER capability that can be trained on the firm's annotated documents to recognize domain-specific entities.

Answer

Prebuilt entity recognition and key phrase extraction

Answer

Sentiment analysis and language detection

Answer

PII detection only

Question 6

A retail company wants to automatically analyze thousands of product reviews to identify the most frequently mentioned aspects, such as 'battery life', 'screen quality', and 'customer service'. They plan to use a prebuilt Azure AI Language feature without any custom training. Which feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it is specifically designed to identify and extract the most important words or phrases from unstructured text, such as product reviews. This prebuilt Azure AI Language feature requires no custom training and directly surfaces frequently mentioned aspects like 'battery life' or 'screen quality' by analyzing term frequency and relevance.

Answer

Text Analytics for Health

Answer

Entity linking

Answer

Sentiment analysis

Question 7

A customer support team wants to analyze chat logs to automatically identify the most common reasons for customer complaints and track how customer sentiment changes throughout a conversation. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?

Accepted Answer

Key phrase extraction and sentiment analysis. Key phrase extraction identifies the most common reasons for complaints by pulling out important terms from the chat logs, while sentiment analysis tracks how customer sentiment changes throughout a conversation by assigning positive, negative, or neutral scores per utterance. Both are prebuilt Azure AI Language features that require no custom training, making them the correct combination for this scenario.

Answer

Entity recognition and language detection

Answer

Text summarization and question answering

Answer

Conversational language understanding and personal identification

Question 8

A customer insights team at a retail company has collected thousands of product reviews. They want to automatically extract the most frequently mentioned topics or aspects from these reviews, such as 'price', 'durability', and 'customer service', without any custom training. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct choice because it is a prebuilt Azure AI Language feature designed to automatically identify and extract the main points or topics from unstructured text, such as product reviews, without requiring any custom training or labeled data. It surfaces frequently mentioned aspects like 'price', 'durability', and 'customer service' by analyzing linguistic patterns and term frequency, making it ideal for this summarization task.

Answer

Text Analytics for Health

Answer

Conversational Language Understanding

Answer

Entity Linking

Question 9

A global news organization receives articles in multiple languages. They need to first identify the language of each article, then translate it into English. Which combination of prebuilt Azure AI services should they use?

Accepted Answer

Language Detection (Azure AI Language) and Translator (Azure AI Translator). The scenario requires two distinct steps: first, identifying the language of each article, which is performed by the Language Detection API in Azure AI Language; second, translating the article into English, which is handled by the Azure AI Translator service. These two prebuilt services are designed to work together seamlessly for multilingual content processing.

Answer

Key Phrase Extraction and Sentiment Analysis

Answer

Entity Recognition and Translator

Answer

Language Detection and Text Analytics for Health

Question 10

A marketing team wants to automatically analyze thousands of customer reviews to identify the most commonly discussed aspects, such as 'price', 'durability', or 'customer service'. They do not have any labeled data for custom training. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct choice because it automatically identifies the most important points or topics (like 'price', 'durability', 'customer service') from unstructured text without requiring any labeled training data. This prebuilt Azure AI Language feature is designed specifically to surface commonly discussed aspects from large volumes of text, making it ideal for analyzing thousands of customer reviews.

Answer

Sentiment analysis

Answer

Entity recognition

Answer

Language detection

Question 11

A language teacher uses Azure AI Language to automatically analyze hundreds of student essays. The teacher wants to identify the main topics discussed in each essay and also understand the overall sentiment (positive, negative, or neutral) expressed. Which two prebuilt Azure AI Language features should the teacher use together to accomplish this goal?

Accepted Answer

Key phrase extraction and Sentiment analysis. The teacher needs two features: one to identify main topics and one to analyze sentiment. Key phrase extraction extracts key phrases that represent the main topics, and sentiment analysis provides the overall sentiment. Option A is the only correct combination. Option D includes entity recognition, which identifies named entities like people or places, not the main topics discussed. Options B and C do not correctly pair a topic-identifying feature with sentiment analysis.

Answer

Entity recognition and Language detection

Answer

Text summarization and Key phrase extraction

Answer

Sentiment analysis and Entity recognition

Question 12

A customer support team wants to analyze chat transcripts to automatically extract the most frequently mentioned product issues and also determine whether each chat represents a positive, neutral, or negative customer experience. Which prebuilt Azure AI Language feature should they use?

Accepted Answer

A. Text Analytics (prebuilt). The Text Analytics (prebuilt) feature in Azure AI Language provides pre-built capabilities for key phrase extraction (to identify frequently mentioned product issues) and sentiment analysis (to classify chats as positive, neutral, or negative). This matches the customer support team's requirements exactly without needing custom training or complex configuration.

Answer

B. Custom Text Classification

Answer

C. Conversational Language Understanding

Answer

D. Question Answering

Question 13

A customer service team wants to analyze thousands of support tickets to automatically categorize them into predefined topics like 'billing', 'technical issue', and 'account management'. They have a small set of labeled tickets for each category. Which Azure AI Language feature should they use?

Accepted Answer

Custom Text Classification. The Custom Text Classification feature of Azure AI Language allows you to train a model using a small set of labeled tickets to automatically categorize text into predefined topics like 'billing', 'technical issue', and 'account management'. This feature is specifically designed for scenarios where you have labeled data and need to classify documents into custom categories, making it ideal for analyzing support tickets.

Answer

Pre-built Text Classification

Answer

Sentiment Analysis

Answer

Key Phrase Extraction

Question 14

A customer support team wants to use Azure AI Language to automatically analyze incoming support emails. They need to extract the product name mentioned in each email and determine whether the customer's sentiment is positive, negative, or neutral. They have no labeled data for custom training. Which two prebuilt Azure AI Language features should they use together?

Accepted Answer

Named entity recognition (NER) and sentiment analysis. Named entity recognition (NER) extracts specific entities like product names from text, while sentiment analysis determines the emotional tone (positive, negative, neutral). Both are prebuilt, no-code features in Azure AI Language that require no labeled data for custom training, making them the correct pair for this use case.

Answer

Key phrase extraction and language detection

Answer

Conversational language understanding (CLU) and translation

Answer

Text summarization and personal identifying information (PII) detection

Question 15

A company wants to build a chatbot that can answer customer questions about their product return policy, shipping times, and warranty information. They have a structured document with these questions and answers. Which Azure AI Language feature should they use to create this chatbot without writing custom code?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to create a chatbot that answers questions based on a structured document (e.g., FAQ, product manual) without writing custom code. It uses a predefined knowledge base of question-answer pairs and provides a built-in orchestration for bot integration, making it ideal for this scenario.

Answer

Conversational Language Understanding (CLU)

Answer

Custom Text Classification

Answer

Language Detection