A digital banking enterprise hosts its transaction processing platform on Google Cloud. The application architecture exposes two key endpoints: an `ExecuteInstantPayment` API where request latency over incurs financial compliance penalties and customer loss, and a `GenerateMonthlyStatement` API where batch report generation can be delayed by several hours without business revenue impact. The engineering team needs to establish a Site Reliability Engineering (SRE) framework that aligns technical operational metrics with actual business risk. Which TWO implementation strategies should the team execute? (Select TWO.)
- Establish a strict latency SLI for `ExecuteInstantPayment` measuring the proportion of successful requests completed under , paired with an SLO that triggers deployment freezes when error budget burns excessively.Cevap
- BDefine the target percentage of availability as the SLI metric while using the rolling 30-day aggregated metric measurement as the SLO metric.
- Set a relaxed availability and latency SLO for `GenerateMonthlyStatement` relative to `ExecuteInstantPayment`, allowing the batch system to consume error budget during peak load without incurring unnecessary over-provisioning costs.Cevap
- DEnforce a unified availability SLO across both endpoints to ensure consistent enterprise infrastructure standards across all microservices.
Cevap
The correct strategies are to establish a latency SLI for instant payments tied to the penalty threshold with error-budget deployment freezes, and to set a lower, more relaxed SLO for monthly statement generation to avoid over-engineering non-critical workloads.
Aligning technical service level objectives with business impact requires setting precise SLIs that capture real business degradation (such as the penalty threshold for instant payments) and establishing differentiated SLOs so non-critical batch processes do not consume unnecessary operational costs or trigger false-alarm engineering overhead.
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Aligning Technical SLOs/SLIs with Business Objectives