Azure AI Interview Preparation – 2

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Greetings for the day!!!

Preparing for Microsoft 365 and AI continues. Today sharing next set of questions and answers for AI

This article will also help to prepare Microsoft Certified: Azure AI Fundamentals : AI – 900 – https://learn.microsoft.com/en-us/credentials/certifications/azure-ai-fundamentals/?practice-assessment-type=certification

Visit our interview article series – Guide to Microsoft 365: Certification & Interview Tips – https://knowledge-junction.in/interview-preparation-2/

Explain the concept “AI-powered information extraction” in brief?

• AI-powered information extraction and analysis enables organizations to gain actionable insights from data.
• This data might be from documents, images, audio files, or other assets.
• Insights can come from structured and unstructured content.
• Structured content is information stored in a consistent format. Examples : invoices, tax forms, and tables.
• Unstructured content is information that isn’t in a predefined format. Examples: emails, audio recordings, images, and videos.

Enlist some use cases of AI-powered information extraction?

• A manufacturer has images of each of its products. The images need to be analyzed for defects and anomalies.
• A business works with a high volume of invoices, contracts, and reports with charts. Key data and summaries from the documents need to be extracted and logged.
• Many hours of customer calls are recorded for quality purposes. The audio needs to be transcribed, summarized, and analyzed for sentiment.
• A streaming catalog contains a large volume of video. Important moments in each video need to be tagged with metadata based on their content.
• A company needs to process employee expense claims, and has to extract expense descriptions and amounts from scanned receipts.
• A customer service agency wants to analyze recorded support calls to identify common problems and resolutions.
• A historical society needs to extract and store data from census records in scanned historical documents.
• A tourist organization wants to analyze video footage and images taken at popular sites. This is to help estimate visitor volumes and improve capacity planning for tours.
• A finance department in a large corporation wants to automate accounts-payable processing. They plan to route invoices received centrally to the appropriate departments for payment.
• A marketing organization wants to analyze a large volume of digital images and documents. Extracting and indexing the extracted data so it can be easily searched.

Explain OCR?

• Optical Character Recognition
• Optical Character Recognition (OCR) helps computers recognize that an element in an image contains text.
• OCR is the foundation of processing text in images
• OCR uses machine learning models that are trained to recognize individual shapes as letters, numerals, punctuation, or other elements of text
• Example
  • Much of the early work on implementing this kind of capability was performed by postal services. To support automatic sorting of mail based on postal codes.

What is Semantic Meaning?

• Semantic meaning refers to the intended meaning or interpretation of words, phrases, or symbols in a given context.
• Semantic meaning goes beyond just the literal definition of a word (syntax). Also focuses on what the word or sentence actually conveys.

What is Document Intelligence?

• Document intelligence describes AI capabilities that process text and attach semantic meaning to the extracted text.
• Document intelligence is an extension of optical character recognition (OCR)
• Document intelligence automates the process of extracting and understanding information.
• Example:
  • An organization that needs to process large numbers of receipts for expenses claims, project costs, and other accounting purposes.
  • Using document intelligence, the company can take a scanned image of a receipt. Digitize the text with OCR, and extract semantic meaning.

What is Knowledge Mining?

• Knowledge mining solutions provide automated information extraction from large volumes of often unstructured data
• A foundational knowledge mining solution is search.
• The process of retrieving relevant information from a large dataset in response to a user query.

What is Document cracking?

• Document cracking describes opening document formats like PDFs to extract the contents as ASCII text for analysis and indexing

Enlist the AZURE AI services for AI task – The Extraction and Analysis of Information from digital content?

Azure AI provides a wide range of cloud-based services for various AI tasks. Including the extraction and analysis of information from digital content.

Service	Description
Azure AI Vision Image Analysis	Azure AI Vision Image Analysis service enables to extract insights from images. It includes: The detection and identification of common objects in images The generation of relevant captions and tags for images The extraction of text in images.
Azure AI Content Understanding	Azure AI Content Understanding is a generative AI-based multimodal analysis service That can extract insights from structured documents, images, audio, and video.
Azure AI Document Intelligence	Azure AI Document Intelligence is designed to extract fields and values from digital (or digitized) forms, such as invoices, receipts, purchase orders, and others
Azure AI Search	Azure AI Search performs AI-assisted indexing in which a pipeline of AI skills are used to systematically extract and index information from structured and unstructured content

Which are the AZURE services available to train the Machine Learning Models?

Icon	Description
	Azure Machine Learning gives us many different options to train and manage our machine learning models. We can choose to work with the Studio for a UI-based experience. Or manage our machine learning workloads with the Python SDK, or CLI for a code-first experience.
	Azure Databricks is a data analytics platform We can use for data engineering and data science Azure Databricks uses distributed Spark compute to efficiently process our data. We can choose to train and manage models with Azure Databricks. Or integrate Azure Databricks with other services such as Azure Machine Learning.
	Microsoft Fabric is an integrated analytics platform. Designed to streamline data workflows between data analysts, data engineers, and data scientists. With Microsoft Fabric, we can prepare data, train a model, use the trained model to generate predictions, and visualize the data in Power BI reports
	Azure AI Services is a collection of prebuilt machine learning models. We can use for common machine learning tasks such as object detection in images. The models are offered as an application programming interface (API), so we can easily integrate a model with our application. Some models can be customized with your own training data, saving time and resources to train a new model from scratch

What is MLflow

• MLflow is an open source platform.
• It streamlines machine learning deployment, regardless of the type of model we trained and the framework you used.
• MLflow is integrated with Azure Machine Learning.
• When we train a machine learning model with Azure Machine Learning, we can use MLflow to register our model.
• MLflow standardizes the packaging of models. This means that an MLflow model can easily be imported or exported across different workflows.

Thanks for reading!!!

HAVE A FANTASTIC TIME AHEAD !!! LIFE IS BEAUTIFUL 🙂