Scenario and Sensitivity Analysis Questions

Techniques for designing executing and interpreting scenario and sensitivity analyses to understand how changes in assumptions affect financial outcomes. Coverage includes designing base case upside and downside scenarios one way and multi way sensitivity testing stress testing key drivers running variance analysis against budgets or forecasts and projecting how operational metric changes cascade through income statement balance sheet and cash flow. Candidates should be able to model business changes such as price increases volume shifts cost reductions or inventory adjustments state and justify assumptions perform contribution margin reasoning interpret variances and communicate limitations implications and recommended actions.

MediumTechnical

0 practiced

Implement a Monte Carlo simulation in Python to model monthly sales given demand uncertainty. Use a normal distribution with mean 100,000 units and std dev 15,000, run 10,000 simulations, apply unit price $20 and compute the distribution of monthly revenue; return expected revenue and 95% confidence interval. Outline code structure and any library imports you would use.

HardTechnical

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Design a governance policy for scenario assumptions and modeling standards across the company. Include required metadata for each scenario (owner, date, rationale, version), testing/validation steps prior to approval, access controls for editing assumptions, periodic review cadence, and an incident process when models materially mis-predict outcomes.

MediumTechnical

0 practiced

Describe how you would implement scenario parameterization in Looker / LookML so analysts can select scenario multipliers without changing the underlying transaction data. Explain the use of derived tables, user attributes or parameter tables, caching considerations, and how you'd avoid high-cost queries on large datasets.

MediumTechnical

0 practiced

Write an ANSI SQL query that computes the sensitivity of total revenue to a 1% uniform price increase and a separate sensitivity to a 1% uniform volume increase across products. Use schema:

sales(order_date, product_id, units_sold, unit_price)

Output: product_id, current_revenue, revenue_if_price_plus_1pct, delta_price_pct1, revenue_if_volume_plus_1pct, delta_volume_pct1. Handle nulls and group by product_id.

HardTechnical

0 practiced

Case study: CFO asks you to quantify the ROI of a proposed price increase using price elasticity of demand. You have historical price/volume pairs that suggest elasticity ranges from -0.5 to -1.2. Describe your modeling approach to test the pricing change across this elasticity range, how you'd compute expected revenue and profit at each elasticity point, and how you'd recommend a go/no-go decision with sensitivity to elasticity uncertainty.

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