Microsoft - AI-900: Microsoft Certified: Azure AI Fundamentals

Sample Questions

Explanation:

Conversational AI solutions are based on interactions between human users and AI agents called bots. Bots in general are examples of conversational AI workload.

Automatically generating captions for pre-recorded videos is an example of Natural Language Processing which uses the Speech service.

References:

Create conversational AI solutions

Captioning with speech to text

Explanation:

Document analysis models in Azure AI Document Intelligence enable text extraction from forms and documents and return structured business-ready content ready for your organization's action, use, or development.

Prebuilt models enable you to add intelligent document processing to your apps and flows without having to train and build your own models.

Custom models are trained using your labeled datasets to extract distinct data from forms and documents, specific to your use cases.

Reference: What is Azure AI Document Intelligence?

Explanation:

Natural language processing supports applications that can see, hear, speak with, and understand users. Using text analytics, translation, and language understanding services, Microsoft Azure makes it easy to build applications that support natural language.

Reference: Microsoft Azure AI Fundamentals: Natural Language Processing

Explanation:

Azure AI Language is a cloud-based service that provides Natural Language Processing (NLP) features for understanding and analyzing text.

The Language service enables you to use the below service features without needing to write code.

Named Entity Recognition (NER)
Named entity recognition is a preconfigured feature that categorizes entities (words or phrases) in unstructured text across several predefined category groups. For example: people, events, places, dates, organizations including companies and more.

Personally identifying (PII) and health (PHI) information detection
PII detection is a preconfigured feature that identifies, categorizes, and redacts sensitive information in both unstructured text documents, and conversation transcripts. For example: phone numbers, email addresses, forms of identification, and more.

Language detection
Language detection is a preconfigured feature that can detect the language a document is written in, and returns a language code for a wide range of languages, variants, dialects, and some regional/cultural languages.

Sentiment Analysis and opinion mining
Sentiment analysis and opinion mining are preconfigured features that help you find out what people think of your brand or topic by mining text for clues about positive or negative sentiment, and can associate them with specific aspects of the text.

Summarization
Summarization is a preconfigured feature that uses extractive text summarization to produce a summary of documents and conversation transcriptions. It extracts sentences that collectively represent the most important or relevant information within the original content.

Key phrase extraction
Key phrase extraction is a preconfigured feature that evaluates and returns the main concepts in unstructured text, and returns them as a list.

Entity linking
Entity linking is a preconfigured feature that disambiguates the identity of entities (words or phrases) found in unstructured text and returns links to Wikipedia.

Text analytics for health
Text analytics for health is a preconfigured feature that extracts and labels relevant medical information from unstructured texts such as doctor's notes, discharge summaries, clinical documents, and electronic health records.

Custom text classification
Custom text classification enables you to build custom AI models to classify unstructured text documents into custom classes you define.

Custom Named Entity Recognition (Custom NER)
Custom NER enables you to build custom AI models to extract custom entity categories (labels for words or phrases), using unstructured text that you provide.

Conversational language understanding
Conversational language understanding (CLU) enables users to build custom natural language understanding models to predict the overall intention of an incoming utterance and extract important information from it.

Orchestration workflow
Orchestration workflow is a custom feature that enables you to connect Conversational Language Understanding (CLU), question answering, and LUIS applications.

Question answering
Question answering is a custom feature that finds the most appropriate answer for inputs from your users, and is commonly used to build conversational client applications, such as social media applications, chat bots, and speech-enabled desktop applications.

Custom text analytics for health
Custom text analytics for health is a custom feature that extract healthcare specific entities from unstructured text, using a model you create.

Reference: What is Azure AI Language?

Explanation:

Anomaly detection enables you to monitor and detect anomalies in your time series data with little machine learning (ML) knowledge, either batch validation or real-time inference.

Reference: What is Anomaly Detector?