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5-Minute Skill Check
Position: Principal Quality Engineer
Before we move forward, we'd like to understand your experience. It's 8 multiple-choice questions and takes about 5 minutes.
Name
*
Email
*
You join a data-intensive product with multiple layers (ingestion, streaming, KPI calculation, persistence, visualization) and have limited time before a release. How do you decide what to test and at which level?
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A
Aim for as close to 100% code coverage as possible across all modules before release
B
Rely mainly on manual end-to-end testing for the flows considered most important
C
Test every acceptance criteria with the same depth regardless of the module's architectural criticality
D
Prioritize based on architectural risk and failure impact, deciding unit/component/API/integration/e2e coverage per risk area
You maintain an automated end-to-end suite covering multi-service flows (API, event stream, database). Tests pass locally but fail intermittently in CI, blocking releases. What's the most effective first step?
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A
Remove the flaky tests from the CI pipeline to keep it green
B
Investigate timing/race conditions, service dependencies and test data isolation causing flakiness
C
Increase the test timeout values until the failures disappear
D
Configure the pipeline to automatically retry failed tests until they pass
Do you have hands-on experience establishing automated quality gates within CI/CD pipelines (e.g. blocking merges or releases based on test results, coverage thresholds, or quality metrics)?
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A
No
B
Yes
Before a major release, you need to validate the system can reliably handle 3x the current data volume. What's the most rigorous approach?
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A
Extrapolate capacity linearly from current single-node benchmark numbers
B
Design representative load tests with realistic data distributions, establish capacity baselines, and run load/stress/endurance tests against them
C
Run a short load test at expected peak traffic and confirm no errors occur
D
Rely on production monitoring after go-live to catch any capacity issues
Do you have hands-on experience designing or validating systems built on Kafka or a comparable event-streaming platform?
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A
No
B
Yes
Do you have hands-on experience working with Kubernetes or OpenShift in a cloud-native delivery environment?
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A
No
B
Yes
Do you have hands-on experience with stream or batch processing engines such as Flink or Spark?
*
A
No
B
Yes
How much do you use AI tools (ChatGPT, Claude, Copilot…) in your day-to-day professional work?
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A
Never / almost never
B
Occasionally, for specific tasks
C
Daily, for specific parts of my work
D
It's a core part of how I work
Anything else you would like us to know? (optional)
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