Decision Making Under Uncertainty Questions

Focuses on frameworks, heuristics, and judgment used to make timely, defensible choices when information is incomplete, conflicting, or evolving. Topics include diagnosing unknowns, defining decision criteria, weighing probabilities and impacts, expected value and cost benefit thinking, setting contingency and rollback triggers, risk tolerance and mitigation, and communicating uncertainty to stakeholders. This area also covers when to prototype or run experiments versus making an operational decision, how to escalate appropriately, trade off analysis under time pressure, and the ways senior candidates incorporate strategic considerations and organizational constraints into choices.

HardTechnical

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A planned API version change may break downstream dashboards and alerts. Create a risk register listing failure modes with estimated probabilities, business impact, mitigation/rollback steps, monitoring requirements, and a communication plan. Describe how you would prioritize mitigations based on expected value.

EasyTechnical

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List the top five observability metrics a BI analyst should produce to monitor the health of a microservice during a multi-region rollout. For each metric explain why it matters, recommended aggregation window, and an illustrative example threshold useful for early detection of regressions.

MediumTechnical

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You're given a timeseries of request latencies with missing data during partial outages. As a BI analyst, describe methods to impute missing values, quantify the uncertainty introduced, and how to present SLA reports that account for imputation bias. Discuss pros and cons of LOCF, linear interpolation, exclusion, and model-based imputation.

HardTechnical

0 practiced

A proposed microservice split introduces a new critical cross-service dependency, increasing blast radius. As the BI analyst, propose controls you should require before approval (dashboards, synthetic checks, SLOs, change windows), quantify expected reduction in outage probability from those controls, and show how you'd enforce compliance with teams.

HardTechnical

0 practiced

Propose a method to propagate uncertainty from distributed tracing and sampling (e.g., partial sampling of spans, probabilistic tracing) into BI dashboards so stakeholders can see both point estimates and confidence for counts, latencies, and failure rates under partial observability. Detail algorithms and display choices.

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