Data Driven Recommendations and Impact Questions

Covers the end to end practice of using quantitative and qualitative evidence to identify opportunities, form actionable recommendations, and measure business impact. Topics include problem framing, identifying and instrumenting relevant metrics and key performance indicators, measurement design and diagnostics, experiment design such as A B tests and pilots, and basic causal inference considerations including distinguishing correlation from causation and handling limited or noisy data. Candidates should be able to translate analysis into clear recommendations by quantifying expected impacts and costs, stating key assumptions, presenting trade offs between alternatives, defining success criteria and timelines, and proposing decision rules and go no go criteria. This also covers risk identification and mitigation plans, prioritization frameworks that weigh impact effort and strategic alignment, building dashboards and visualizations to surface signals across HR sales operations and product, communicating concise executive level recommendations with data backed rationale, and designing follow up monitoring to measure adoption and downstream outcomes and iterate on the solution.

EasyTechnical

0 practiced

Explain the ICE and RICE prioritization frameworks. As a data engineer in a small team, describe how you would adapt either framework to prioritize a backlog of measurement, instrumentation, and pipeline work when product asks for many small experiments.

EasyTechnical

0 practiced

Given the events schema below, write an ANSI SQL query to compute Daily Active Users (DAU) and, for each day, the 7-day rolling retention percentage.

Schema: events(event_ts TIMESTAMP, user_id STRING, event_name STRING)

Explain assumptions about timezone, deduplication, and how you treat users with missing user_id.

HardSystem Design

0 practiced

Describe governance, metadata, and lineage requirements to ensure experiment results are auditable and reproducible across teams. Include experiment metadata (assignment seed/version), dataset versioning, transformation versioning, access controls, and tools or workflows that enforce reproducible analyses and approvals.

HardTechnical

0 practiced

Describe a scalable approach to estimate heterogeneous treatment effects at production scale using causal forests or two-model approaches in Spark. Outline feature engineering, cross-validation strategy, how to train at scale (distributed algorithms, memory considerations), validation metrics, and a serving plan for near-real-time scoring.

MediumSystem Design

0 practiced

Design an A/B experiment to evaluate a new recommendation algorithm on the homepage. Your product has 10M weekly active users. Describe traffic assignment strategy, sampling unit, metrics to track (primary and guardrail), rollout/ramp plan, minimum test duration, and safety checks to stop or rollback the experiment.

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