Outcomes and Progress Tracking Questions

Mindset and practices for defining success and tracking progress across projects programs and roles. Covers how to define measurable success criteria align work to objectives and key results and key performance indicators set baselines targets and guardrail metrics and choose appropriate review cadences. Includes team and agile measures such as velocity burndown cycle time sprint completion rates and capacity planning as well as program and product measures such as adoption usage business impact and technical health. Also addresses how to visualize progress with dashboards run regular tracking processes communicate status to different audiences and avoid misuse of metrics for punitive evaluation.

MediumTechnical

0 practiced

Given sprint story point completions for the last 6 sprints: [30, 32, 28, 40, 35, 33], calculate a forecast for next sprint velocity and provide a 95% confidence interval using simple statistical assumptions. Explain your approach and discuss other forecasting techniques you might consider for capacity planning.

MediumTechnical

0 practiced

Implement a Python function that flags anomalies in a daily active users time series using the Median Absolute Deviation (MAD) method. Signature: detect_anomalies(dates: list, values: list, threshold: float = 3.0) -> list of dates flagged. Explain choices for threshold and handling of seasonality.

EasyBehavioral

0 practiced

Tell me about a time you had to explain a complex dashboard to non-technical executives. Use the STAR structure (Situation, Task, Action, Result). What visualization choices did you make, how did you validate stakeholders understood the insights, and what was the business outcome?

HardSystem Design

0 practiced

Design a data model suitable for cohort analysis by acquisition channel, feature usage, and revenue. Provide table sketches for users, events, purchases, and a daily pre-aggregate table used for fast cohort queries. Explain keys, indexes, and how you would materialize pre-aggregations.

HardTechnical

0 practiced

Discuss the tradeoffs between using randomized experiments (A/B tests) and observational metrics to measure product impact. When should you rely on experiments, when on observational analysis, and how can BI combine both to inform decisions? Include limitations, cost, and time considerations.

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