data consultancy Interview Questions
The questions to prepare for data consultancy interviews, across all roles. Questions from real interview reports rank first. Updated daily.
Optimize a slow PostgreSQL query that aggregates completed transactions while preserving customers with no matching activity.
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Approach for maintaining data quality and integrity across ETL pipelines.
Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.
Explain how you would manage scope creep without damaging stakeholder trust or putting delivery at risk.
Set a clear north star, supporting KPIs, leading indicators, and guardrails for a new product feature.
A framework for prioritizing AI product features based on user value, feasibility, evaluation quality, and trade-offs.
Identify the main pitfalls that can distort A/B test interpretation and explain how to guard against them.