CLF-C02 Cloud Technology and Services Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Auto Scaling scheduled scaling

  Why it's wrong here

  Scheduled scaling is a time-based capacity planning feature within Auto Scaling. You define a recurring or one-time schedule to adjust the desired capacity, such as scaling out before a predictable traffic spike. It does not respond to instance health failures — it only reacts to a clock. Therefore, a failed instance at an unscheduled moment would remain broken until the next scheduled event, so it cannot maintain a minimum healthy instance count.

• ✓

  Auto Scaling self-healing / instance replacement

  Why this is correct

  Auto Scaling's self-healing mechanism continuously monitors the health of each instance in the Auto Scaling group using EC2 status checks and Elastic Load Balancing (ELB) health checks. When an instance fails these checks, the group automatically terminates it and launches a new replacement instance with a fresh instance ID. This process is oriented entirely around preserving the group's desired or minimum capacity, not around time or demand forecasting. It directly answers the goal of maintaining a minimum healthy fleet, making it the correct mechanism.

• ✗

  EC2 Auto Recovery

  Why it's wrong here

  EC2 Auto Recovery is a CloudWatch alarm action that restarts an EC2 instance on a new physical host while preserving its instance ID, private IP, and Elastic IP. It is designed to recover a single, standalone instance from hardware failure or impaired connectivity. Auto Scaling, by contrast, terminates a failed instance and launches a completely new one, which can have a different instance ID and may be in a different Availability Zone. Auto Recovery does not manage the size of an Auto Scaling group, so it cannot enforce a minimum healthy instance count across a fleet.

• ✗

  Predictive scaling

  Why it's wrong here

  Predictive scaling is a feature of Auto Scaling that uses machine learning to analyze historical traffic patterns and forecast future demand, proactively adjusting capacity ahead of anticipated load changes. It is a capacity-planning tool that helps handle expected surges, not a reactive health-check mechanism. Predictive scaling has no awareness of an instance's health or lifecycle; it only adjusts the desired capacity based on predictions. Thus, it cannot replace an instance that has failed, and it does not ensure the group maintains a minimum healthy count.

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