AI-900 Describe features of Natural Language Processing workloads on Azure Practice Test 2 — 15 Questions

Question 1

A legal research company needs to automatically extract specific case citation patterns (e.g., '123 U.S. 456') from thousands of legal documents. The standard named entity recognition in Azure AI Language does not recognize these custom citation formats. Which Azure AI Language feature should they use to create a model that extracts these custom entities?

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

Accepted Answer

Custom Named Entity Recognition. Custom Named Entity Recognition (NER) is the correct choice because it allows you to train a model to extract domain-specific entities, such as legal citation patterns like '123 U.S. 456', that are not recognized by the pre-built NER in Azure AI Language. Unlike standard NER, which only identifies common entity types (e.g., person, location, date), Custom NER lets you define custom entity labels and train the model with labeled examples to recognize these specific patterns in legal documents.

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Sentiment Analysis

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Key Phrase Extraction

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Conversational Language Understanding

Question 2

A market research firm wants to analyze thousands of customer reviews to automatically identify frequently mentioned product aspects and the sentiment associated with each aspect. For example, they want to know that 'battery life' is mentioned often with negative sentiment, while 'design' is mentioned with positive sentiment. Which Azure AI Language capability is best suited for this task?

Accepted Answer

Aspect-based Sentiment Analysis (Opinion Mining). Aspect-based Sentiment Analysis (Opinion Mining) is the correct choice because it goes beyond general sentiment to identify specific aspects (e.g., 'battery life', 'design') mentioned in text and the sentiment associated with each aspect. This directly matches the requirement to automatically detect frequently mentioned product aspects and their corresponding sentiments from customer reviews.

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Key Phrase Extraction

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Sentiment Analysis

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Named Entity Recognition (NER)

Question 3

A customer support team wants to analyze chat transcripts to identify the most common issues customers are reporting. They need to automatically extract meaningful phrases like 'slow internet connection' and 'billing error' from the conversations. Which Azure AI Language feature should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction is the correct Azure AI Language feature because it is specifically designed to automatically identify and extract the most important words and phrases from unstructured text, such as 'slow internet connection' and 'billing error' from chat transcripts. This allows the support team to surface common issues without manual review. Sentiment Analysis, NER, and Language Detection serve different purposes and do not extract meaningful multi-word phrases.

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Sentiment Analysis

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Named Entity Recognition (NER)

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Language Detection

Question 4

A global customer support team receives feedback messages in multiple languages. They want to build an automated pipeline that first identifies the language of each message, then translates it to English, and finally analyzes the sentiment of the translated text. Which combination of Azure AI services should they use?

Accepted Answer

Azure AI Translator and Azure AI Language (Text Analytics). The pipeline requires language detection, translation, and sentiment analysis. Azure AI Translator provides language detection and translation, while Azure AI Language (Text Analytics) provides sentiment analysis on the translated English text. This combination directly fulfills all three requirements.

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Azure AI Translator and Azure Anomaly Detector

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Azure AI Speech and Azure AI Language (Text Analytics)

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Azure AI Vision and Azure AI Language (Text Analytics)

Question 5

A medical research organization needs to process thousands of clinical trial documents to automatically extract specific medical entities such as diseases, symptoms, medications, and dosages. They want to use a prebuilt Azure AI Language capability that is already trained on medical domain data. Which Azure AI Language feature should they use?

Accepted Answer

Text Analytics for Health. Text Analytics for Health is a prebuilt Azure AI Language capability specifically trained on medical domain data, enabling extraction of entities like diseases, symptoms, medications, and dosages from clinical trial documents without requiring custom model training. It is designed for healthcare and life sciences use cases, making it the correct choice for processing thousands of clinical documents automatically.

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Key Phrase Extraction

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Named Entity Recognition (NER)

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Custom Text Classification

Question 6

A customer support team wants to automatically extract the most important words or short phrases from each customer service ticket to understand common issues. Which Azure AI Language feature should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language feature because it is specifically designed to identify and return the most important words and short phrases from a document, such as a customer service ticket. This allows the support team to automatically surface common issues by analyzing the extracted key phrases across many tickets. The other options serve different purposes: named entity recognition identifies specific entities like people or organizations, sentiment analysis detects emotional tone, and language detection identifies the language of the text.

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Named entity recognition

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Sentiment analysis

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Language detection

Question 7

A marketing team wants to automatically analyze thousands of product reviews to determine if each review expresses a positive, negative, or neutral opinion about the product. Which Azure AI Language feature should they use?

Accepted Answer

Sentiment Analysis. Sentiment Analysis is the correct Azure AI Language feature because it is specifically designed to classify text into positive, negative, or neutral sentiments. This directly matches the requirement to automatically determine the opinion expressed in each product review, making it the appropriate choice for this marketing team's task.

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Key Phrase Extraction

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Named Entity Recognition

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Language Detection

Question 8

A hospital receives patient notes in free text. They need to automatically identify entities like disease names, medications, and dosages from these notes without requiring any custom training. Which Azure AI Language feature is specifically designed for this medical entity extraction task?

Accepted Answer

Text Analytics for Health. Text Analytics for Health is a pre-built Azure AI Language feature specifically designed to extract medical entities such as diseases, medications, dosages, symptoms, and procedures from unstructured clinical text without requiring any custom training. It uses specialized medical ontologies (e.g., UMLS, SNOMED CT) and natural language processing models trained on healthcare data, making it the correct choice for this task.

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Custom Named Entity Recognition

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Key Phrase Extraction

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Sentiment Analysis

Question 9

A news agency wants to automatically extract the names of people and organizations mentioned in thousands of articles. They plan to use a prebuilt Azure AI Language feature that requires no custom training. Which feature should they use?

Accepted Answer

Named entity recognition. Named entity recognition (NER) is the correct feature because it is specifically designed to identify and categorize entities such as people, organizations, locations, and dates from unstructured text. The news agency's requirement to extract names of people and organizations from articles matches NER's prebuilt capability, which requires no custom training and is available in Azure AI Language.

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Sentiment analysis

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Key phrase extraction

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Language detection

Question 10

A customer support team wants to create a chatbot that can answer common questions about employee benefits. They have a PDF document containing a list of frequently asked questions with their answers. Which Azure AI Language feature should they use to build a solution that extracts answers directly from this content?

Accepted Answer

Custom Question Answering. Custom Question Answering (C) is the correct choice because it is specifically designed to ingest documents like PDFs and extract question-answer pairs from them, enabling a chatbot to respond directly with answers from the content. This feature uses a pre-built or custom knowledge base to match user queries to the most relevant answer, making it ideal for the described scenario.

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Sentiment Analysis

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Key Phrase Extraction

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Text Analytics for Health

Question 11

A law firm needs to automatically detect and redact sensitive information such as names, addresses, and social security numbers from legal documents. Which Azure AI Language feature can detect these entities without custom training?

Accepted Answer

PII Detection. PII Detection is the correct Azure AI Language feature because it is specifically designed to identify and redact sensitive personal information such as names, addresses, and social security numbers from text without requiring any custom training. This pre-built capability uses machine learning models to detect categories of personally identifiable information (PII) out of the box, making it ideal for compliance scenarios like legal document processing.

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Sentiment Analysis

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Key Phrase Extraction

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Language Detection

Question 12

A medical research team needs to analyze thousands of clinical trial reports to extract specific medical terms like disease names, symptoms, and medications. They want to use an Azure AI Language feature that is pre-trained on medical data and requires no custom training. Which feature should they use?

Accepted Answer

Text Analytics for Health (Healthcare NLP). Text Analytics for Health (Healthcare NLP) is a pre-trained Azure AI Language feature specifically designed to extract medical entities such as disease names, symptoms, medications, and treatment details from unstructured clinical text. It requires no custom training and is built on medical ontologies like UMLS, making it ideal for analyzing thousands of clinical trial reports without additional model development.

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Key Phrase Extraction

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Named Entity Recognition (NER)

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Sentiment Analysis

Question 13

A legal firm needs to automatically produce a short summary of each lengthy court ruling, highlighting the most important sentences. Which Azure AI Language feature should they use?

Accepted Answer

Extractive summarization. Extractive summarization (Option C) is the correct Azure AI Language feature because it identifies and extracts the most important sentences from a document to produce a concise summary. This directly matches the legal firm's requirement to automatically generate a short summary of lengthy court rulings by highlighting key sentences, without generating new text.

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Key phrase extraction

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Named entity recognition

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Sentiment analysis

Question 14

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?

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.

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Key phrase extraction and sentiment analysis

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Named Entity Recognition (NER) and language detection

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Entity linking and conversational language understanding

Question 15

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.

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Conversational Language Understanding (CLU)

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Text Analytics for health

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Translator