AI for FP&A and Financial Forecasting Quiz

Answers and explanations appear as you take the quiz.

1. In FP&A, what makes a forecast driver-based?
   • It is produced only by a neural network
   • Financial lines are linked to operating drivers such as headcount, units, price or win rate
   • It uses last year's actuals with no changes
   • It is prepared only by the board of directors
2. What defines a rolling forecast as described in the guide?
   • A forecast made once per year and never revised
   • A forecast only of cash balances
   • A forecast that extends a set number of months ahead, such as 12 or 18, and is updated regularly
   • A forecast that covers only the current month
3. Why should a team backtest a machine learning forecast against a simple baseline such as same-week-last-year?
   • On short, noisy or disrupted histories, simple baselines can beat complex models
   • Simple baselines are required by accounting standards
   • Machine learning models cannot be backtested
   • Baselines are always more accurate
4. Which is an example of data leakage in a forecasting backtest?
   • Using several years of history to train the model
   • Reconciling SKU forecasts up to the total
   • Publishing prediction intervals
   • Including actual promotion results that would not have been known when the forecast was made
5. What is the main risk of letting a language model draft variance commentary without extra input?
   • It cannot read tables
   • It may state plausible but invented causes, since the data shows the size of a variance but not the reason
   • It always refuses to discuss budgets
   • It converts all figures into a different currency
6. What does forecast value added (FVA) analysis measure?
   • Whether human overrides improve accuracy compared with the statistical baseline
   • The dollar value of the forecast's revenue
   • How long a forecast takes to produce
   • The number of drivers in a model
7. In forecast evaluation, what does bias refer to?
   • The model's preference for one software vendor
   • The random scatter of errors around zero
   • A consistent tendency to over- or under-forecast
   • The difference between two currencies
8. In which situation does the guide suggest statistical and machine learning forecasts are most likely to struggle?
   • Forecasting many stable product lines with long histories
   • Producing baselines for thousands of cost centers
   • Updating a monthly rolling forecast
   • New products, pricing changes or economic shocks the model has never seen