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Data Analysis and Requirements Translation Questions

Focuses on translating ambiguous business questions into concrete, actionable data analysis plans. Candidates should identify what data is needed to answer the question, define the metrics or KPIs that would settle it, state and validate the assumptions behind those definitions, and lay out the concrete analysis steps or queries that would produce an answer. Strong answers connect analysis choices back to the business decision at stake: what would change stakeholder behavior or strategy, what data quality or data availability issues could undermine the conclusion, and what additional data collection, reporting, or systems changes would be needed to answer the question reliably going forward.

EasyTechnical
51 practiced
Describe five best practices when designing an executive dashboard for C-suite stakeholders. For each practice, give a concrete example of a metric or visualization and explain why it matters for decision-making and what assumptions underlie each metric.
MediumTechnical
62 practiced
You manage a team delivering metrics to Sales and Finance but they disagree on the definition of 'active customer'. Describe a process (governance and technical) to reach consensus and implement a single source of truth. Include stakeholders, artifacts, and enforcement mechanisms.
MediumTechnical
45 practiced
A business user requests a weekly ad-hoc report every Monday that slices conversions by campaign and country. Propose a repeatable automation solution using SQL + Power BI or Tableau that is parameterized and scheduled. Describe template design, parameter inputs, and monitoring to ensure data freshness.
EasyTechnical
49 practiced
A dashboard number (total sign-ups) doesn't match the CRM export. Outline a step-by-step troubleshooting plan you would follow to find the discrepancy: include SQL checks, instrumentation verification, timezone considerations, and stakeholder questions.
EasyBehavioral
55 practiced
Behavioral: Tell me about a time when a stakeholder gave you an ambiguous request and you had to elicit requirements, translate them into a deliverable, and manage expectations. Use the STAR method. What was the outcome and what did you learn?

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