A payroll application is idle overnight and is hammered every weekday between 08:00 and 10:00. The pattern has been the same for three years. The team wants the extra capacity in place before the users arrive, not several minutes after a processor alarm has fired. Which Auto Scaling approach fits best?

AWS Certified Cloud Practitioner (CLF-C02), objective 3. Cloud technology and services medium

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The options

Correct A scheduled scaling action that raises the desired capacity before 08:00 and lowers it after 10:00

Correct. When the pattern is known in advance, scaling on the clock puts the instances in service before the load arrives, and removes them again so you are not paying for the peak all night.

Not correct A target tracking policy on average processor utilisation

Wrong on its own for this requirement, though it is a good companion. A dynamic policy by definition reacts after load appears, and new instances take minutes to boot and pass health checks, so the earliest arrivals still get a slow application.

Not correct Have an engineer add instances manually each morning

Wrong. It works exactly until the engineer is ill, on holiday or in a meeting, and it is the kind of toil automation exists to remove.

Not correct Replace the fleet with one permanently larger instance

Wrong. That pays the peak price twenty-four hours a day, still has a ceiling, and reduces the fleet to a single point of failure.

Why

EC2 Auto Scaling offers manual scaling, scheduled scaling for known patterns, dynamic scaling that reacts to a metric such as average processor use or request count, and predictive scaling that forecasts from history. A mature configuration usually combines the scheduled action for the pattern you know with a dynamic policy as the safety net for the day that is not like the others. The point of all of it is elasticity: capacity follows demand in both directions, and the second direction is where the money is.

Where this comes from

Cited
AWS exam guide task statement 3.3

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