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This lesson introduces the core concepts of Performance Max (PMax) as an AI-driven evolution in Google Ads. We will contrast the traditional, siloed campaign structure (Search, Display, YouTube) with PMax's unified, goal-oriented approach. The focus will be on the three foundational pillars of a successful PMax campaign: 1) Goals: Understanding how defining clear conversion actions serves as the primary objective for Google's machine learning algorithms. 2) Assets: Explaining the shift from creating static ads to providing a portfolio of creative components (headlines, images, videos, logos) that the AI dynamically assembles and optimizes for different channels and users. 3) Audience Signals: Detailing how PMax uses audience suggestions not as rigid targets, but as a starting point for its AI to discover new, high-performing customer segments. The 'doomsday preppers' vs. 'boating and sailing' example will be used to illustrate the power of this machine learning-based audience expansion.
This lesson introduces the fundamentals of Linux kernel security through direct interaction with kernel parameters. We will start by exploring the /proc pseudo-filesystem, understanding its structure and how it serves as a runtime interface to the kernel. Then, we will focus on the 'sysctl' utility to view and modify key security-related network parameters, such as those for disabling ICMP ping responses, enabling TCP SYN cookie protection against DoS attacks, and preventing IP spoofing with rp_filter. The session will include a practical exercise where you apply these settings, check their status, and make them permanent by creating a configuration file in /etc/sysctl.d/, mirroring a real-world hardening task.
This lesson introduces the core principles of designing resilient systems on AWS. It begins by defining resiliency and availability, using the industry-standard "nines" (e.g., 99.9%, 99.99%) to quantify uptime and acceptable downtime. The main focus will be on two key AWS services: EC2 Auto Scaling and Simple Queue Service (SQS). We will cover how to configure an Auto Scaling group using Launch Templates, including setting minimum, maximum, and desired capacity. We will also explore dynamic scaling policies, specifically Target Tracking, to automatically adjust capacity based on metrics like CPU utilization. The second part of the lesson will explain how to use SQS to decouple application components, improving fault tolerance. This includes the producer/consumer pattern, the function of a Dead-Letter Queue (DLQ) for handling message failures, and the benefits of loose coupling in preventing cascading failures.
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