Cloud Engineer Research & Evidence-Based Practice 5

Question 1

A cloud team is evaluating whether multi-cloud strategy reduces vendor lock-in risk. Which type of evidence would most strongly support or refute this hypothesis?

Accepted Answer

Longitudinal case studies documenting actual migration timelines, costs, and operational complexity from organizations that pursued multi-cloud

Answer

Longitudinal case studies from organizations that actually executed multi-cloud strategies provide real-world evidence of costs, benefits, and trade-offs over time.

Question 2

When designing a load test to generate evidence for a cloud capacity planning decision, which factor is most critical to control?

Accepted Answer

Ensuring the load profile (request types, concurrency, think time) accurately reflects production traffic patterns

Answer

A load test only yields valid capacity planning data if the synthetic traffic accurately mimics real-world traffic distribution and patterns.

Question 3

Which practice best embodies the 'measure twice, cut once' principle in cloud infrastructure changes?

Accepted Answer

Collecting baseline metrics before any change and defining rollback criteria, then deploying and comparing against baseline

Answer

Establishing a pre-change baseline and clear rollback criteria creates the evidence needed to evaluate whether the change achieved its goal without harm.

Question 4

A cloud engineer wants to apply findings from Google's Site Reliability Engineering (SRE) book to their small startup. What critical thinking step is most important?

Accepted Answer

Assess which SRE principles apply at your scale and organizational maturity, adapting rather than directly copying Google's context

Answer

Practices from hyperscalers often embed assumptions about scale, team size, and resources that don't transfer directly to smaller organizations, requiring contextual adaptation.

Question 5

What is 'Hawthorne effect' and why is it relevant to cloud performance experiments?

Accepted Answer

A bias where systems perform better during observation periods due to increased attention, potentially inflating experiment results

Answer

The Hawthorne effect can cause systems or teams to perform atypically during a monitored experiment period, making results less representative of normal operation.

Cloud Engineer Practice Test

Cloud Engineer Practice Test

Cloud Engineer Research & Evidence-Based Practice 5