Foundation of AI, ML, NN and GenAI + Capstone Project

Last reviewed: 17 July 2026Last course-content update: 17 July 2026Official exam guide reviewed: Study guide for Exam DP-300: Administering Microsoft Azure SQL Solutions, skills measured as of 24 April 2026Syllabus alignment: Reviewed against the official objectives available as of 17 July 2026Course Update History17 July 2026: Completed six domain-based practice tests containing 1,500 independently created questions.17 July 2026: Updated migration and SQL Server VM servicing references to reflect current Microsoft tooling.17 July 2026: Completed duplicate, explanation, source, and CSV-integrity checks across the full question bank.Prepare for Microsoft Certified: Azure Database Administrator Associate by practising the technical judgement required for Exam DP-300: Administering Microsoft Azure SQL Solutions.This practice-test-only course contains six focused assessments aligned with the current exam blueprint. The question bank combines concepts, configuration choices, realistic scenarios, troubleshooting symptoms, migration decisions, security controls, performance evidence, automation workflows, and recovery trade-offs.Each option includes its own explanation, helping you understand why the correct response works and why the alternatives fail in the stated context.Course OverviewCourse type: Practice-test-only coursePractice tests: 6Questions per test: 250Total questions: 1,500Difficulty: Mixed beginner, intermediate, and advancedFormat: Single-answer multiple-choice, including scenario, troubleshooting, architecture, configuration, command, and comparison questionsStructure: Topic-focused and domain-basedExplanations: Separate explanation for every option plus a detailed overall explanationDomain

Shop Floor Management is an important and intrinsic part of any manufacturing operations and is instrumental in managing execution of production orders in an efficient manner. This course focuses on various kinds of manufacturing viz. Discrete, Repetitive Process Manufacturing and multiple ways of completing production orders.Shop Floor Management provides a crucial input to Manufacturing accounting, which is instrumental in calculating production cost and different kinds of variances viz. Planned Variance, Engineering Variance and Actual Variances.Shop Floor Management training builds on the basis of Product Data Management setups and focuses on execution of work orders.A shop floor management system plays a key role in managing the flow of materials inside the plant. An effectively implemented shop floor management system serves as a mediator between production control and the shop floor. The JD Edwards EnterpriseOne Shop Floor Management system from Oracle provides an effective way to maintain and communicate information that the system requires to complete production requests.JD Edwards EnterpriseOne Shop Floor Management enables you to manage and track manufacturing work orders. It uses data from the shop floor to maintain and communicate status information regarding materials, work centers, routing instructions, and end operations that are required to complete the production requests. A traditional shop floor uses dispatch lists, capacity requirements, finite scheduling, capacity planning, capacity simulation, and optimization. Some companies might also use bar coding, kanban, and just-in-time manufacturing processes on the shop floor.JD Edwards EnterpriseOne Shop Floor Management course, teaches students to:Create and process work orders, including attaching things like parts lists, routing instructions, co-products and by-products, and intermediate items.Process lots, including

This course covers the fundamental of Generative AI, or simply GenAI. It is the most hyped-up technology existing in the market today. Gartner has listed it on the top of its two hype curves for 2023: AI technologies hype curve and Emerging Technologies hype curve. This technology became popular since the launch of ChatGPT, a GenAI-powered chatbot, released by OpenAI, on 30th Nov 2022. ChatGPT has 100 million+ users and its website sees nearly 1.5 billion monthly visits. GenAI has a massive impact on individuals and businesses around the globe. It is a digital transformation technology, that has the potential to change the ways of doing business. Businesses around the globe are exploring how to best exploit its potential to add value to their customers. It is the foundation of Agentic AI. Traditional AI centers on prediction, while Generative AI focuses on creation. Agentic AI takes this a step further by making action its defining characteristic—specifically, the creation of purposeful, autonomous action tailored to complex environments. By enabling systems to synthesize information, reason dynamically, and generate a unique sequence of decisions to solve a problem, this dynamic execution forms the core foundation of Agentic AI.It is important that individuals have the fundamental knowledge of this technology, which will play a significant role in shaping their lives in the coming years- so that they can be more ethically aware and socially responsible while using GenAI. Businesses too must cultivate this knowledge to understand their customer needs better and offer the best GenAI solutions for them. However, most of the content available today is either too high-level or too complex with lots of technical diagrams and jargon. This has inspired me to develop this course with substantial content in a simplified and easy-to-understand language for people with both technical and non-technical backgrounds. The lecture con

Are you preparing for a Security Operation Center (SOC) Analyst role or looking to strengthen your cybersecurity skills with real-world practice?This course is designed to help you build practical knowledge through carefully crafted practice questions that simulate real Security Operations Center (SOC) scenarios. Whether you're a beginner or already working in cybersecurity, these tests will help you think like an analyst and improve your decision-making skills.What You’ll LearnAnalyze real-world security alerts and logsIdentify and respond to cyber threats and incidentsUnderstand SOC workflows and processesImprove your skills in threat detection and incident responseStrengthen knowledge of SIEM tools and log analysisPractice with scenario-based questions similar to real job situationsWhy This Course?Unlike theory-heavy courses, this program focuses on hands-on practice. Each question is designed to:Test your analytical thinkingSimulate real SOC environmentsProvide detailed explanations for better understandingThis approach ensures you're not just memorizing—but actually learning how to respond in real situations.Who This Course Is ForAspiring SOC AnalystsCybersecurity beginnersIT professionals transitioning into securityAnyone preparing for SOC-related interviews or rolesCourse FeaturesMultiple practice test setsRealistic scenario-based questionsClear and detailed explanationsRegular updates with new questions

Google Cloud Associate Data Practitioner Practice Exam is a comprehensive exam preparation resource designed to help learners evaluate, strengthen, and validate their understanding of essential data concepts and Google Cloud data technologies. This practice exam provides a realistic learning experience for individuals preparing for the Google Cloud Associate Data Practitioner certification and for professionals who want to improve their practical knowledge of data management, analytics, governance, and cloud-based data solutions.In today’s digital environment, organizations rely heavily on data to make informed decisions, improve operational efficiency, create innovative products, and gain competitive advantages. Cloud data platforms have become a critical foundation for collecting, storing, processing, analyzing, and transforming large amounts of information. Google Cloud provides a powerful ecosystem of data services that enables businesses to build scalable, secure, and intelligent data solutions. This practice exam helps learners understand how these technologies are applied in real-world scenarios and prepares them to confidently approach certification challenges.=Google Cloud Associate Data Practitioner Practice Exam is designed around the key skills and knowledge areas required for modern data professionals. It focuses on fundamental data concepts, data lifecycle management, data storage solutions, analytics principles, data processing methods, security practices, governance strategies, and the use of Google Cloud services for managing and analyzing data.Through realistic practice questions, learners can test their understanding of important topics such as structured and unstructured data, relational and non-relational databases, data warehouses, data lakes, data pipelines, data quality, and data-driven decision-making. The questions are designed to encourage practical thinking and help students understand how to choose appropriate solutions based on

This course contains the use of artificial intelligenceDisclosure: AI tools were used only to assist in creating the course outline and course thumbnail. All instructional content, explanations, and project walkthroughs were fully created manually by the instructor.Welcome to Computer Vision: Vision Transformers Vision Language Model course. This is a comprehensive project based course where you will learn how to build modern computer vision applications using Vision Transformers, Segment Anything Model, Contrastive Language Image Pre Training, attention mechanism, and other AI models. This course is a perfect combination between artificial intelligence and computer vision, making it an ideal opportunity for you to practice your programming skills while improving your technical knowledge in deep learning. In the introduction session, you will learn the basic fundamentals of Vision Transformers and Vision Language Model, such as getting to know its use cases and how the system works. Then, in the next section, we will start the projects, in the first project, we are going to build a satellite image classification system using Vision Transformers. This system will be able to analyze satellite images and categorize different land types, for example, forests, rivers, residential areas, industrial areas, and highways. Then, in the second project, we are going to build a soil type classification system using Vision Transformers. This system will enable us to analyze soil images and classify different soil categories like black soil, clay soil, red soil, and other soil types. Afterward, in the third project, we are going to perform image segmentation using the Segment Anything Model. Firstly, we will remove product backgrounds by isolating the main object from its surrounding environment to create clean product images for e-commerce. After that, we will also segment flood areas by identifying and separating water affected regions from aerial images to support disaster monitoring and analysis. Then