AI-900 Describe features of Natural Language Processing workloads on Azure — Timed 25 Questions

Question 1

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?

25 minute time limit — choose an answer to begin.

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.

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Entity recognition and Language detection

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Text summarization and Key phrase extraction

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Sentiment analysis and Entity recognition

Question 2

A hotel chain wants to analyze thousands of guest reviews to understand the overall tone of feedback (positive or negative) and to extract the most commonly mentioned features (e.g., 'room cleanliness', 'staff friendliness', 'breakfast'). Which two Azure AI Language features should they combine?

Accepted Answer

Sentiment analysis and key phrase extraction. The scenario requires two objectives: understanding the overall tone (positive/negative) and extracting commonly mentioned features. Sentiment analysis directly provides the tone. Key phrase extraction identifies important phrases like 'room cleanliness'. Therefore, the correct combination is sentiment analysis and key phrase extraction (Option A). Option C (key phrase extraction and entity linking) lacks sentiment analysis, so it does not fully address the requirement. Entity linking helps disambiguate entities but does not capture tone, making Option C incomplete.

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Entity recognition and language detection

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Key phrase extraction and entity linking

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Text summarization and sentiment analysis

Question 3

A customer support team receives emails in multiple languages. They want to automatically determine the language of each email and then extract key phrases to summarize the issue. Which two Azure AI Language features should they use in sequence?

Accepted Answer

Language detection and key phrase extraction. The scenario requires first identifying the language of each email (using Language Detection) to enable accurate processing of multilingual content. Then, to summarize the issue, Key Phrase Extraction directly extracts key phrases from the text. The stem explicitly asks for 'extract key phrases to summarize the issue,' so only Language Detection followed by Key Phrase Extraction (Option C) achieves this. Option B uses Entity Extraction, which identifies entities like names or dates but does not extract key phrases for summarization. Options A and D are incorrect because Sentiment Analysis is not used for summarization, and starting with Entity Extraction without language detection is less effective.

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Sentiment analysis and key phrase extraction

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Language detection and entity extraction

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Entity extraction and sentiment analysis

Question 4

A customer service team wants to analyze chat transcripts to understand customer sentiment and identify the most frequently discussed topics. Which two Azure AI Language features should they combine to achieve this?

Accepted Answer

Sentiment analysis and key phrase extraction. To understand customer sentiment and identify frequently discussed topics, the required features are sentiment analysis (to detect emotional tone) and key phrase extraction (to surface important terms/topics). Option A provides this pair. Option B (language detection and entity extraction) does not include sentiment analysis, so it cannot assess customer sentiment. Options C and D are also unsuitable because they lack sentiment analysis and do not directly address both requirements.

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Language detection and entity extraction

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Text summarization and question answering

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Named entity recognition and translation

Question 5

A customer support team wants to automatically analyze incoming emails to (1) determine the overall emotional tone (e.g., frustrated, satisfied) and (2) identify specific key phrases that indicate the reason for contact (e.g., 'return item', 'refund policy'). Which two Azure AI Language features should they use? (Choose two.)

Accepted Answer

Sentiment analysis. Sentiment analysis is correct because it evaluates text to determine the overall emotional tone, such as frustration or satisfaction, by assigning a sentiment score (positive, negative, neutral, or mixed) at the document and sentence level. Key phrase extraction is correct because it identifies the most relevant phrases in the text, such as 'return item' or 'refund policy', which directly map to the customer's reason for contact. Entity recognition identifies predefined entities like people or locations, and language detection determines the language, neither of which addresses the requirements.

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Entity recognition

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Language detection

Question 6

What is 'semantic role labelling' (SRL) and why is it important for language understanding?

Accepted Answer

Identifying who did what to whom, when, and where by assigning roles to sentence participants. Semantic role labeling (SRL) is a natural language processing technique that identifies the predicate-argument structure in a sentence, assigning roles such as agent, patient, instrument, location, and time to sentence constituents. It is important for language understanding because it enables a machine to answer 'who did what to whom, when, and where,' which is foundational for tasks like information extraction, question answering, and event detection. Note that Azure AI services do not expose SRL as a primary API capability; it is a more specialized NLP technique.

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Labelling training data with semantic tags for custom NLP model training

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Classifying the semantic category (positive, negative, neutral) of each sentence role

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Assigning roles and responsibilities to team members in an AI project

Question 7

What is 'abstractive question answering' vs 'extractive question answering' in Azure AI Language?

Accepted Answer

Extractive returns a quoted text span; abstractive generates a new synthesised answer. Extractive question answering (QA) identifies and returns a verbatim text span from the source document as the answer, while abstractive QA generates a new, synthesized answer in natural language that may not appear verbatim in the source. Azure AI Language's custom question answering is extractive; abstractive QA is typically performed by generative AI models.

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Extractive works on short texts; abstractive handles long documents

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Abstractive QA is faster because it doesn't need to search the full document

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Extractive is pre-built; abstractive always requires custom model training

Question 8

What is 'Azure AI Bot Service' and how does it relate to Azure AI Language?

Accepted Answer

The conversation infrastructure platform that uses Azure AI Language for NLP understanding. Azure AI Bot Service is a comprehensive platform for building, deploying, and managing conversational bots. It relies on Azure AI Language (specifically the Language Understanding (LUIS) service) to perform natural language processing (NLP) — interpreting user intents and extracting entities from utterances. This integration allows the bot to understand and respond to natural language input, making option B correct.

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A hosting service for training NLP models on conversational data

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A monitoring service that tracks bot conversation quality metrics

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Azure's billing service for tracking bot API call usage and costs

Question 9

A healthcare organization needs to extract specific data elements (such as patient names, medication dosages, and dates) from unstructured doctors' notes. Which Azure Cognitive Service is best suited for this task?

Accepted Answer

Text Analytics. Text Analytics (now part of Azure AI Language) is the correct service because it provides pre-built entity extraction capabilities specifically designed to identify and extract named entities like people (patient names), quantities (medication dosages), and dates from unstructured text. This aligns directly with the requirement to extract specific data elements from doctors' notes without needing custom model training.

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Language Understanding (LUIS)

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Translator Text

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Speech

Question 10

A hospital wants to create a system that can transcribe doctor-patient conversations in real time and also extract medical conditions, medications, and dosages from the transcribed text. Which combination of Azure AI services should they use?

Accepted Answer

Speech to Text and Text Analytics for Health. The scenario requires real-time transcription of doctor-patient conversations, which is handled by Azure Speech to Text, and then extraction of medical entities like conditions, medications, and dosages from the transcribed text, which is specifically provided by Azure Text Analytics for Health. Text Analytics for Health is a specialized container or API within Azure Cognitive Services that uses medical ontologies (e.g., UMLS, SNOMED CT) to extract clinical entities, unlike the standard Text Analytics API which only extracts general entities like names or locations.

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Speech to Text and Text Analytics API (standard)

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Translator Text and Language Understanding (LUIS)

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Speaker Recognition and Question Answering

Question 11

A customer service team wants to build an Azure AI-powered bot that can understand the intent behind customer messages. For example, the bot should recognize that 'I want to return my shoes' maps to a 'ReturnItem' intent, and 'Where is my order?' maps to 'TrackOrder'. Which Azure service provides pre-built models specifically for intent recognition?

Accepted Answer

Language Understanding (LUIS). Language Understanding (LUIS) is the correct Azure service because it provides pre-built models and custom capabilities specifically designed for intent recognition and entity extraction from natural language utterances. The scenario requires mapping customer messages like 'I want to return my shoes' to a 'ReturnItem' intent, which is exactly the core function of LUIS—it analyzes user input to identify the user's goal (intent) and any relevant details (entities).

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Text Analytics

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Translator Text

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Speech-to-text

Question 12

An online news platform receives thousands of articles daily. The editors want to automatically identify the most important topics discussed in each article to help with content categorization. Which Azure Text Analytics capability should they use?

Accepted Answer

Key Phrase Extraction. Key Phrase Extraction (B) is the correct Azure Text Analytics capability because it identifies the most important topics and main points discussed in a document by returning a list of key phrases that summarize the core content. For an online news platform needing to automatically detect topics for categorization, this directly extracts the salient subjects from each article, unlike other capabilities that focus on sentiment, named entities, or language identification.

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Sentiment Analysis

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Named Entity Recognition

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Language Detection

Question 13

A company's HR department wants to create a self-service bot that can answer employee questions about company policies. They have a collection of policy documents in PDF format. Which Azure AI Language feature should they use to ingest these documents and enable the bot to provide answers based on them?

Accepted Answer

Custom Question Answering. Custom Question Answering (CQA) is the correct choice because it is specifically designed to ingest documents (including PDFs) and build a knowledge base of question-answer pairs. The bot can then query this knowledge base to provide answers based on the policy documents, using the underlying Azure Cognitive Search and language models to match user questions to the most relevant content.

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Sentiment Analysis

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Key Phrase Extraction

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Language Detection

Question 14

A retail company collects thousands of customer reviews. They want to automatically extract frequently mentioned aspects (e.g., 'battery life', 'customer service', 'price') to understand common topics. Which Azure AI Language capability should they use?

Accepted Answer

Key phrase extraction. Key phrase extraction is the correct Azure AI Language capability because it is specifically designed to identify and extract the main talking points or topics from unstructured text, such as 'battery life', 'customer service', and 'price' from customer reviews. This directly matches the requirement to automatically extract frequently mentioned aspects without needing predefined categories.

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Sentiment analysis

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Named entity recognition

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Language detection

Question 15

A law firm receives hundreds of legal documents daily. They want to use Azure AI Language to automatically assign each document to exactly one predefined category, such as 'Contract', 'Trademark', or 'Litigation'. Which Azure AI Language feature is specifically designed for this task?

Accepted Answer

Custom text classification (single-label). Custom text classification (single-label) is the correct feature because it allows you to train a model to assign each document to exactly one predefined category (e.g., 'Contract', 'Trademark', 'Litigation') based on your own labeled data. This is distinct from prebuilt features like NER or key phrase extraction, which do not perform document-level categorization into custom classes.

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Named Entity Recognition (NER)

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Key phrase extraction

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Conversational Language Understanding (CLU)

Question 16

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?

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 17

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 18

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 19

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 20

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