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Answer: Identify, measure, and eliminate toil by automating repetitive tasks.
The question centers on balancing service availability, reducing technical debt, minimizing costs, and preparing for business changes while adhering to Google SRE best practices. The service currently exceeds its 99% availability target at 99.5%, indicating stability. Option A (adding redundancy) would increase costs without necessity, as availability is already above target, and it doesn't address technical debt. Option C (defining and minimizing the error budget) is counterproductive; minimizing the error budget implies avoiding any changes, stifling innovation and new feature launches. Option D (allocating engineers to the feature backlog) risks ignoring technical debt and operational inefficiencies, potentially leading to future reliability issues. Option B (identifying, measuring, and eliminating toil via automation) aligns with Google SRE principles. It reduces repetitive tasks, frees engineering resources for strategic work (e.g., new features), lowers operational costs, and mitigates technical debt by streamlining processes. This approach ensures the service remains within availability targets while enabling business agility.
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You manage a global service with a 99% availability target and limited engineering resources. This month, the service achieved 99.5% availability. To maintain compliance with availability goals, adapt to upcoming feature launches, reduce technical debt, and minimize operational costs while adhering to Google-recommended practices, what actions should you take?
A
Add N+1 redundancy to your service by adding additional compute resources to the service.
B
Identify, measure, and eliminate toil by automating repetitive tasks.
C
Define an error budget for your service level availability and minimize the remaining error budget.
D
Allocate available engineers to the feature backlog while you ensure that the service remains within the availability target.
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