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This lesson introduces AWS CloudTrail as the foundational service for auditing and governance. We will cover its core function of recording all user activity and API usage across an AWS account. The session will differentiate between the two main types of logged events: Management Events (control plane operations like creating an EC2 instance) and Data Events (high-volume, data-plane operations like S3 object access). We will explore the default 90-day Event History and then explain the necessity of creating a "Trail" to deliver immutable, long-term log files to an S3 bucket for security analysis and compliance. A key focus will be on the log file integrity validation feature, which ensures logs have not been tampered with. The lesson will conclude with a practical walkthrough of the CloudTrail console to view recent events.
This lesson provides a foundational understanding of the YouTube advertising ecosystem within the broader Google Ads platform. It will cover the primary YouTube ad formats (Skippable in-stream, Non-skippable in-stream, Bumper, Video discovery) and their strategic applications. We will explore the core concept of 'avatar-based' targeting on YouTube and how it differs from 'intent-based' search targeting, providing a strategic lens relevant to a data-focused professional. The session will conclude with an overview of the Google Ads auction system, highlighting how data signals and AI determine ad placement and cost.
This lesson introduces the core concepts of AI agents and the Model Context Protocol (MCP). It starts by defining what constitutes a true 'AI Agent' by explaining the 'action-feedback' loop where an LLM directs its own processes, contrasting it with simpler 'agentic workflows' which are deterministically coded. The central focus will be on understanding the 'MxN problem' in AI development (connecting M models to N tools) and how MCP provides a standardized interface, analogous to the Language Server Protocol (LSP), to simplify this complexity to a more manageable M+N integration effort. The lesson concludes by surveying key industrial use cases for AI agents, such as automated coding, advanced research, customer service automation, and the creation of business-specific copilots.
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