Structuring and dataset selection
What it tests. Organizing a business problem and identifying the data that actually moves the target metric.
Worked example. Revenue grew 10% but operating margin fell 15%: which framework best isolates the cause? (Segment revenue and disaggregate operating costs, not external Porter analysis.)
Common traps. Picking overly broad frameworks or data decoupled from the stated objective.
How to handle it. Anchor to the exact metric (profitability vs market share) and use elimination to keep only residual drivers.
Quantitative and business math
What it tests. Margins, breakeven, percentage change and blended math with zero manual errors.
Worked example. Enterprise revenue $80M at 20% margin grows 10%; SMB revenue $120M at 5% margin stays flat. Blended margin to one decimal? (11.3%.)
Common traps. Unit-conversion errors (millions vs thousands) and missing rounding instructions.
How to handle it. Write the formula on scratch paper first, plug in raw numbers, track zeroes, and sanity-check before submitting.
Graph and data interpretation
What it tests. Reading stacked bars, waterfalls, scatterplots and tables to isolate the true systemic issue.
Worked example. A cost waterfall shows +$12M support, -$4M hosting, +$18M commissions, -$6M real estate: sales and support drove a net $30M increase outpacing $10M of infrastructure savings.
Common traps. Confusing absolute values with percentage growth or ignoring axis labels.
How to handle it. Read the title and axes, find the dominant trend, then translate it into a business insight.
Critical thinking
What it tests. Determining which conclusions are strictly validated by the data, without outside assumptions.
Worked example. Top reps spend 40% more time on face-to-face pitches and 90% use an advanced CRM: only "most high performers used the CRM" and "there is a correlation" must be true, not causation.
Common traps. Mistaking correlation for causation and extrapolating realistic-but-unstated dynamics.
How to handle it. Grade each statement: +2 if explicitly confirmed, 0 if inconclusive, -2 if contradicted; select only definitive matches.
Video recommendation
What it tests. Answer-first synthesis under intense time pressure.
Worked example. Recommendation in 0:00-0:15, two or three quantified data points in 0:15-0:45, risks and next steps in 0:45-1:00.
Common traps. Spending too long on background and getting cut off before the recommendation.
How to handle it. Use a rigid Pyramid-Principle blueprint and do not deviate from it.