AI-900 Describe features of Natural Language Processing workloads on Azure Practice Test 10 — 15 Questions

Question 1

What is 'abstractive summarization' vs. 'extractive summarization' in Azure AI Language, and which produces summaries in new words?

Accepted Answer

Abstractive generates new sentences; extractive selects existing sentences from the source. Abstractive summarization generates new sentences that rephrase the core meaning of the source text, similar to how a human would summarize. Extractive summarization, in contrast, selects and copies key sentences directly from the original document without rewording them. Option B correctly identifies that abstractive produces new sentences while extractive selects existing ones.

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Extractive produces new words; abstractive copies sentences

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They produce identical output through different computational paths

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Abstractive works only for legal documents; extractive for general text

Question 2

What is Azure Cognitive Search (Azure AI Search) and what role does it play in AI applications?

Accepted Answer

An enterprise search and retrieval service that powers RAG by indexing documents for semantic search. Azure Cognitive Search (now Azure AI Search) is a cloud-based enterprise search service that provides full-text search, vector search, and hybrid search capabilities. In AI applications, it plays a critical role in Retrieval Augmented Generation (RAG) by indexing documents and enabling semantic search, which allows AI models to retrieve relevant information from private data sources to ground their responses.

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A service for searching Azure subscription costs and billing records

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A web crawler that indexes public internet content like Bing

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A service for searching through Azure ML model training logs

Question 3

What is the difference between entities and intents in conversational language understanding?

Accepted Answer

Intents represent the user's goal; entities are the specific pieces of information within the utterance. In conversational language understanding (CLU), intents represent the user's overall goal or desired action (e.g., 'BookFlight'), while entities are specific data points extracted from the utterance that provide context for that intent (e.g., 'New York' as a destination). This distinction is fundamental to natural language processing (NLP) on Azure, where intents map to actions and entities provide the parameters needed to fulfill those actions.

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Intents are for text; entities are for speech recognition

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Intents are predefined answers; entities are user questions

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They are the same concept with different names for clarity

Question 4

What is a 'multi-turn conversation' in the context of Azure Bot Service and conversational AI?

Accepted Answer

A conversation where context is maintained across multiple exchanges to enable natural dialogue. In Azure Bot Service and conversational AI, a multi-turn conversation refers to the ability of a bot to maintain context across multiple exchanges, allowing for natural, coherent dialogue. This is achieved through state management (e.g., using Bot Framework's turn context and storage layers) to track user intent and conversation history, enabling follow-up questions and clarifications without losing context.

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A conversation with multiple human agents taking turns responding

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A bot that can respond in multiple languages within one conversation

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A conversation with multiple topics handled simultaneously in parallel

Question 5

What does Azure AI Language's 'language detection' feature return?

Accepted Answer

The language name, ISO code, and a confidence score. Azure AI Language's language detection feature returns the language name, its ISO 639-1 code, and a confidence score between 0 and 1. This allows applications to identify the language of input text with a quantifiable level of certainty, enabling downstream processing like routing to the correct language-specific model.

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Only the language name in English

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A translated version of the text in English

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Only a binary yes/no for whether the text is English

Question 6

What is the Azure AI Translator's 'document translation' capability?

Accepted Answer

Asynchronously translating complete documents while preserving layout and formatting. Azure AI Translator's document translation capability is designed for asynchronous batch translation of entire documents (e.g., Word, PDF, HTML) while preserving the original layout, structure, and formatting. This is achieved through the Document Translation API, which processes files in their entirety rather than translating individual text snippets in real time.

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Translating short text snippets from a chat interface

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Creating translated copies of database records

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Converting documents from one file format to another in a different language

Question 7

What is Azure AI Language's 'custom summarization' capability?

Accepted Answer

Fine-tuning the summarization model on domain-specific documents for improved specialized summaries. Azure AI Language's custom summarization allows you to fine-tune a pre-trained summarization model using your own domain-specific documents. This enables the model to generate more accurate and relevant summaries for specialized fields like legal, medical, or financial texts, rather than relying solely on generic training data.

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Generating summaries with custom fonts and formatting styles

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Setting a custom character limit for all generated summaries

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Automating summary creation for all documents in an Azure storage account

Question 8

What is the purpose of Azure AI Language's 'entity linking' feature?

Accepted Answer

Identifying entities in text and connecting them to a knowledge base to disambiguate meaning. Entity linking in Azure AI Language identifies named entities in text and disambiguates them by linking to a corresponding entry in a knowledge base, such as Wikipedia or Microsoft's internal knowledge graph. This resolves cases where the same name could refer to multiple real-world entities (e.g., 'Washington' could be a state, a person, or a city), ensuring the correct meaning is assigned.

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Creating hyperlinks in documents to connect related sections

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Linking multiple Azure AI Language projects together

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Connecting extracted entities to a CRM database for business intelligence

Question 9

What is the purpose of 'entities' in conversational language understanding (CLU) models?

Accepted Answer

To extract specific parameter values from user utterances needed to fulfill an intent. Entities in CLU models are designed to extract specific pieces of information (parameter values) from user utterances, such as dates, locations, or product names, which are necessary to fulfill the user's intent. For example, in the utterance 'Book a flight to Seattle on June 5th,' the intent is 'BookFlight,' and entities extract 'Seattle' (destination) and 'June 5th' (date). This directly supports the intent by providing the required parameters for downstream actions.

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To determine the overall topic or domain of the conversation

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To classify how confident the model is in its intent prediction

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To define the fallback response when no intent is recognized

Question 10

What is 'speech synthesis markup language' (SSML) used for in Azure AI Speech?

Accepted Answer

An XML markup language for controlling TTS voice characteristics like pitch, rate, pauses, and pronunciation. SSML is an XML-based markup language that allows you to fine-tune text-to-speech (TTS) output by controlling prosodic elements such as pitch, speaking rate, volume, and pronunciation. It also supports inserting pauses, specifying phonetic pronunciations, and adjusting emphasis, making it essential for generating natural-sounding speech in Azure AI Speech.

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A programming language for writing speech recognition algorithms

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A system for transcribing speech in real time to a database

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A security protocol for encrypting speech API calls

Question 11

What is Azure AI Translator's 'custom translator' service?

Accepted Answer

A service for training custom machine translation models on domain-specific parallel texts. Azure AI Translator's 'custom translator' service allows users to build and train custom machine translation models using parallel documents (source-target sentence pairs) specific to a domain, such as legal or medical terminology. This improves translation accuracy for specialized content beyond what the general-purpose neural machine translation engine provides.

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A service that hires human translators to manually translate documents

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A feature that lets users customize the output language font and formatting

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A tool for automatically detecting which language content should be translated into

Question 12

What is 'named entity recognition' (NER) in Azure AI Language?

Accepted Answer

Identifying and classifying real-world entities (people, organisations, locations) mentioned in text. Named entity recognition (NER) is a feature of Azure AI Language that identifies and categorizes real-world entities such as people, organizations, locations, dates, and quantities within unstructured text. It uses pre-trained machine learning models to extract these entities, enabling downstream tasks like information retrieval and content summarization. Option B correctly describes this core functionality.

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Renaming database fields to follow a consistent naming convention

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Recognising the named author of a document for copyright purposes

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Detecting when a user provides their name in a chatbot conversation

Question 13

What is 'conversation history' and why is it important in multi-turn chatbot interactions?

Accepted Answer

The sequence of prior messages included in the prompt so the model can maintain context across turns. Conversation history is the sequence of prior messages included in the prompt to a language model, allowing it to maintain context across multiple turns in a dialogue. This is critical because the model itself has no inherent memory; without the history, each turn would be treated as an isolated query, breaking the flow of a multi-turn interaction. By appending previous user inputs and assistant responses to the prompt, the model can reference earlier statements and provide coherent, context-aware replies.

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A database log of all chatbot conversations for compliance auditing

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A summary of frequently asked questions generated from past user interactions

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The total number of messages a user has sent to a chatbot over their lifetime

Question 14

What is 'text analytics' in Azure AI Language?

Accepted Answer

AI-powered extraction of insights (sentiment, key phrases, entities) from unstructured text. Text analytics in Azure AI Language is an AI-powered service that extracts structured insights from unstructured text. It uses pre-built models to identify sentiment (positive/negative/neutral), key phrases, named entities (people, places, organizations), and language detection, enabling automated analysis of large volumes of text data.

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A tool for counting the number of words and characters in a document

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A spreadsheet function for analysing numerical data in text cells

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Encrypting sensitive text data before storing it in the cloud

Question 15

What is 'custom text classification' in Azure AI Language?

Accepted Answer

Training a model on labelled examples to classify documents into custom business-specific categories. Custom text classification in Azure AI Language allows you to train a model using your own labeled data to classify documents into categories that are specific to your business needs, such as contract types, customer feedback themes, or support ticket priorities. This is a supervised learning capability where you provide examples of text and their corresponding categories, and the service learns to predict the category for new, unseen text. It is not a pre-built or universal classifier, but rather a tailored solution for domain-specific classification tasks.

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Automatically applying CSS classes to text displayed on a web page

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Classifying text files by their file type (PDF, Word, TXT)

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A pre-built classifier that categorises all text into 10 universal topics