SOA-C03 practice question: EC2 Auto Scaling (2 of 3)

Reliability and Business Continuity A free question from our original SOA-C03 practice exam. Try it, then open the answer to see why each option is right or wrong.

An esports league's match-replay service runs in an EC2 Auto Scaling group with a target tracking policy.

Each new instance needs about 12 minutes to start, because it loads a large index into memory during its first boot. Sudden demand spikes after big matches are not predictable, and users see errors before new capacity is ready.

The league wants new capacity in service much faster, while keeping the cost of idle capacity LOW.

What should a CloudOps engineer configure?

Choose one answer.

A. Add a warm pool to the group that keeps pre-initialized instances in the Stopped state.
B. Add a predictive scaling policy so that the group launches instances ahead of demand based on forecast load.
C. Raise the group's minimum and desired capacity so that enough running instances are always ready for the largest spike.
D. Lower the default instance warmup on the target tracking policy so that new instances count toward the metric sooner.

Show the answer and explanation

Answer: A

A. Right: Correct. Warm pool instances complete the slow initialization once and then wait stopped, so the group mostly pays only for their EBS volumes. On scale-out they start and enter service much faster than new launches. Learn more: Warm pools for Amazon EC2 Auto Scaling (opens in a new tab)
B. Wrong: Predictive scaling forecasts from daily and weekly patterns in past load. Spikes after big matches are not predictable, so the forecast would not launch capacity at the right time. Learn more: Predictive scaling for Amazon EC2 Auto Scaling (opens in a new tab)
C. Wrong: Keeping peak capacity running all the time removes the delay, but it pays for many idle running instances. That breaks the low idle-cost requirement. Learn more: Target tracking scaling policies for Amazon EC2 Auto Scaling (opens in a new tab)
D. Wrong: Instance warmup controls when a new instance's metrics are included in scaling decisions. It does not make the 12-minute initialization faster, so users still see errors. Learn more: Target tracking scaling policies for Amazon EC2 Auto Scaling (opens in a new tab)

Explanation

When instances have a long initialization and demand is not predictable, a warm pool keeps pre-initialized instances ready next to the group. Stopped warm instances avoid most compute charges and still reach service far faster than a fresh launch. Predictive scaling needs repeating patterns, and keeping extra instances running costs more.

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Warm pools for Amazon EC2 Auto Scaling

SOA-C03 practice question: EC2 Auto Scaling (2 of 3)

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