AI-900 Describe features of Natural Language Processing workloads on Azure Practice Test 8 — 15 Questions

Question 1

Which Azure AI service can identify and extract named entities (people, organizations, locations, dates) from text?

Accepted Answer

Azure AI Language (Named Entity Recognition). Azure AI Language's Named Entity Recognition (NER) capability is specifically designed to identify and categorize named entities such as people, organizations, locations, and dates from unstructured text. This is a core feature of the Natural Language Processing (NLP) workload within Azure AI Language, making option B the correct choice.

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Azure AI Vision

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Azure AI Translator

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Azure AI Speech

Question 2

Which Azure AI service extracts key information (like invoice numbers, dates, and amounts) from structured documents like forms and invoices?

Accepted Answer

Azure AI Document Intelligence. Azure AI Document Intelligence (formerly Form Recognizer) is the correct service because it is specifically designed to extract structured data such as invoice numbers, dates, and amounts from forms and invoices. It uses prebuilt models for invoices and receipts, along with custom model training, to perform optical character recognition (OCR) and key-value pair extraction from structured documents.

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Azure AI Language

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Azure AI Vision

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Azure AI Translator

Question 3

What does Azure AI Speech service's text-to-speech (TTS) feature do?

Accepted Answer

Converts written text into natural-sounding spoken audio. Azure AI Speech service's text-to-speech (TTS) feature converts written text into natural-sounding spoken audio using neural voice models. It synthesizes speech from input text, enabling applications like voice assistants, audiobooks, and accessibility tools. This is the core function of TTS, distinct from speech-to-text or translation capabilities.

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Converts spoken audio into written text

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Identifies the language of spoken audio

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Translates spoken words from one language to another

Question 4

What is the purpose of Azure AI Language Studio?

Accepted Answer

A web-based UI for exploring and building NLP solutions without code. Azure AI Language Studio is a web-based user interface that allows users to explore, build, and manage natural language processing (NLP) solutions without writing code. It provides pre-built and customizable features such as sentiment analysis, key phrase extraction, language detection, and conversational language understanding, enabling rapid prototyping and integration of NLP capabilities into applications.

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A tool for training custom computer vision models

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A platform for managing Azure AI service billing

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A code editor for writing Python ML scripts

Question 5

What is the purpose of Azure AI Translator?

Accepted Answer

To translate text between 100+ languages using neural machine translation. Azure AI Translator is a cloud-based neural machine translation service that translates text between over 100 languages and dialects. It uses deep learning models to produce high-quality, context-aware translations, making option B the correct answer.

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To convert spoken audio from one language to written text in another language

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To detect the sentiment of multilingual text

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To extract structured data from multilingual documents

Question 6

What is the purpose of key phrase extraction in Azure AI Language?

Accepted Answer

To identify the main talking points and important concepts in text. Key phrase extraction in Azure AI Language is designed to identify the main talking points and important concepts within a given text. It analyzes the text structure and returns a list of key phrases that summarize the core topics, enabling quick understanding of the document's primary themes without reading the entire content.

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To translate key phrases between languages

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To classify text into positive or negative sentiment

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To generate new text based on key topics

Question 7

What is conversational language understanding (CLU) in Azure AI Language?

Accepted Answer

A feature that trains models to understand user intent and extract entities from natural language. Conversational Language Understanding (CLU) is a feature within Azure AI Language that enables you to build custom models for extracting intents (what the user wants to do) and entities (key pieces of information) from natural language utterances. Unlike pre-built or translation services, CLU is specifically designed for training and deploying a natural language understanding model tailored to your application's domain.

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A service that translates chatbot responses into multiple languages

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A service that converts speech to text for voice assistants

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A pre-built AI for answering FAQ questions automatically

Question 8

What is the question answering feature in Azure AI Language used for?

Accepted Answer

Building knowledge bases that automatically answer questions from FAQ content. The question answering feature in Azure AI Language is designed to extract answers from structured content like FAQs, manuals, or support documents. It builds a knowledge base that can automatically respond to user queries in natural language, making it ideal for customer support or self-service portals.

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Training custom language models for complex conversations

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Generating new questions from a given text

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Testing the quality of chatbot responses

Question 9

Which Azure service enables building a question-answering chatbot from existing FAQ documents with minimal code?

Accepted Answer

Azure AI Language question answering + Azure Bot Service. Azure AI Language question answering (formerly QnA Maker) is specifically designed to extract question-answer pairs from semi-structured content like FAQ documents, PDFs, and URLs, and expose them via a REST API. Azure Bot Service provides the conversational interface and channel integration (e.g., Teams, Web Chat) with minimal code. Together, they enable a fully functional Q&A chatbot without custom NLP model training.

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Azure Machine Learning with custom NLP models

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Azure AI Speech with custom commands

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Azure Cognitive Search alone

Question 10

What is Azure AI Speech's real-time speech recognition feature used for?

Accepted Answer

Converting live spoken audio into text with low latency. Azure AI Speech's real-time speech recognition feature is designed to convert live spoken audio into text with low latency, enabling applications like live captioning, voice commands, and transcription during meetings. It uses streaming APIs (e.g., the Speech SDK's RecognizeOnceAsync or StartContinuousRecognitionAsync) to process audio chunks as they arrive, returning partial and final results with minimal delay.

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Generating spoken audio from written text in real time

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Translating real-time audio between languages

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Identifying who is speaking from their voice

Question 11

What is opinion mining (also called aspect-based sentiment analysis) in Azure AI Language?

Accepted Answer

Identifying sentiment toward specific aspects or topics mentioned in text. Opinion mining, also known as aspect-based sentiment analysis, in Azure AI Language goes beyond general sentiment to identify sentiment (positive, negative, neutral, or mixed) toward specific aspects or topics mentioned in the text. For example, in a product review like 'The battery life is great but the screen is too dim,' it would detect positive sentiment toward 'battery life' and negative sentiment toward 'screen.' Option B correctly captures this core functionality.

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Identifying who expressed an opinion in a text

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Translating opinions from one language to another

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Detecting politically biased content in news articles

Question 12

What is tokenization in the context of natural language processing?

Accepted Answer

Breaking text into smaller units (tokens) for processing by language models. Tokenization is the process of breaking text into smaller units called tokens, which can be words, subwords, or characters. This is a fundamental preprocessing step in natural language processing because language models like GPT or BERT operate on discrete tokens rather than raw text. Each token is mapped to an integer ID from a vocabulary, enabling the model to process and generate language mathematically.

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Encrypting text for secure transmission to AI services

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Assigning security access tokens to API calls

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Converting text into a numerical score for sentiment analysis

Question 13

What is the purpose of Azure AI Speech's speaker recognition feature?

Accepted Answer

To identify who is speaking based on their unique voice characteristics. Azure AI Speech's speaker recognition feature is designed to identify and verify individuals based on their unique vocal characteristics, such as pitch, tone, and speech patterns. This is achieved through voice biometrics, where the service creates a unique voiceprint for each speaker and matches it against enrolled profiles. Option B correctly captures this purpose, distinguishing it from transcription or audio processing tasks.

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To transcribe spoken audio into text

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To detect whether audio contains speech or background noise

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To improve audio quality by removing background noise

Question 14

What is custom text classification in Azure AI Language?

Accepted Answer

Training a text classification model on your own labeled data for custom categories. Custom text classification in Azure AI Language allows you to train a machine learning model on your own labeled dataset to classify text into custom categories that are specific to your business needs. This is a supervised learning approach where you provide examples of text and their corresponding labels, and the service learns to predict the correct category for new, unseen text. It is distinct from pre-built classification models because it adapts to your unique taxonomy.

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Translating text into a custom language invented by the user

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Automatically detecting and removing custom offensive terms from text

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Formatting text with custom styles and fonts

Question 15

What is Azure AI Language's text summarization capability used for?

Accepted Answer

Condensing long text into shorter summaries capturing the key information. Azure AI Language's text summarization capability is designed to condense long documents into shorter summaries that capture the key information. It uses extractive or abstractive summarization techniques to identify and present the most important sentences or generate new concise text, making it ideal for quickly digesting large volumes of content.

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Translating long documents into multiple languages

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Generating new creative text based on document themes

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Classifying documents into predefined business categories