GCP Professional Cloud Architect Practice Question
Your architecture team must select a managed relational database service on Google Cloud for a new globally distributed e-commerce platform. Requirements include multi-region availability at 99.999 %, external consistency for financial transactions, automatic petabyte-scale growth, and cost limits. Finance, operations, and developer stakeholders all need to understand why the final choice is made before funding a proof-of-concept. Which decision-making technique best provides a transparent, repeatable way to quantify how each option meets the agreed KPIs?
Conduct a white-board architecture review with senior engineers and choose the option that receives consensus support.
Build a small proof-of-concept for each database and select the one that shows the highest throughput in benchmarks.
Create a weighted decision matrix that scores Cloud SQL, Spanner, and AlloyDB against stakeholder-approved KPIs, then choose the highest-scoring service.
Randomly route a portion of production traffic to each database (A/B test) and select the option with the best real-time customer satisfaction metrics.
Using a weighted decision matrix forces the team to document the key KPIs (for example availability, consistency model, scalability, and cost) and assign weights that reflect the relative importance each stakeholder group gives to those criteria. Each database option is then scored against the weighted KPIs, producing an objective total that can be shared and discussed. This approach makes the trade-offs explicit, provides the traceability the CTO is asking for, and can be reused for future architectural choices.
Running separate proofs-of-concept without an agreed scoring framework would generate data but not guarantee a transparent comparison. A white-board discussion relies on subjective opinion and gives no quantitative audit trail. Randomly A/B testing databases in production for critical financial workloads is risky and does not evaluate all KPIs up-front.
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What is a weighted decision matrix?
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How does Google Spanner meet the requirements listed?
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Why is A/B testing risky in this scenario?
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What is a weighted decision matrix?
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How does Google Cloud Spanner support global consistency?
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Why isn't A/B testing suitable for database selection in this case?
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