AI and Corporate Finance Practice Quiz (58 questions)

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The simulation uses five sessions and six decisions, covering January through June of the first year. A six-session structure reverses the stated relationship between sessions and decisions.

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The assessment assigns 10%, 35%, 40%, and 15% to the four listed components in that order. The second option swaps the weights of the team reports and final synthesis, while the other alternatives change multiple percentages.

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The orange regime allows conceptual tutoring but excludes formulas and figures. The red regime prohibits AI in the listed activities, while documented strategic criticism and report auditing belong to the green regime.

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Kolumpi is a Mediterranean soap factory, and its product range includes the six named products such as Oliplas, Lavbox, and Duobox. The other descriptions shift Kolumpi's role from manufacturing soaps to retailing, packaging, or distribution.

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Production begins with olive or lavender soap base and packaging, continues through cutting and conditioning, and ends with finished products distributed through several channels. The other sequences place workshops or distribution in an incorrect order.

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Each machine provides 9,100 minutes of capacity per month. Using a different figure would distort the comparison between theoretical consumption and workshop capacity.

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Raw-material stock is expressed as days of production, whereas finished-product stock is expressed as days of sales. Applying one unit to both inventories confuses the operational meaning of the two measures.

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Customer satisfaction is one of the eight steering indicators, alongside areas such as profitability, cash, stocks, workshops, human resources, and innovation. The team grade is not itself one of those management indicators.

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Cash is assessed using available bank and savings balances, while agios apply to overdrafts rather than positive cash. The zero score applies at 45 or more days of cash coverage, not simply because cash is present.

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The recommended raw-material range is 30 to 60 production days, while finished-product stockouts are penalized. Finished-product overstock mainly ties up cash, so maximizing it is not the stated objective.

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Corrected sales are calculated by dividing observed sales by the seasonality coefficient, so $1{,}200 \div 1.2 = 1{,}000$ units. Multiplying by the coefficient would adjust in the wrong direction.

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The cascade first calculates the variable-cost margin, then subtracts product-specific fixed costs to obtain contribution margin, and finally subtracts common fixed costs to obtain operating income. The alternative involving common fixed costs at the first stage confuses costs assigned to products with costs shared across products.

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Contribution margin in euros shows the absolute amount a product contributes toward covering fixed costs. The variable-cost margin rate instead indicates price sensitivity and does not identify the largest absolute contribution.

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The revenue threshold is calculated as $\Delta CA=\frac{\Delta CF}{m}=\frac{€12{,}000}{0.30}=€40{,}000$. Dividing by the margin rate is necessary because only that proportion of additional revenue contributes toward the added fixed costs.

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The lower unit margin means Duobox must sell about 28,929 units, which is a 15.7% increase, to preserve the former total margin. The 10% figure describes the price reduction rather than the required increase in quantity.

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A large language model is trained on vast text collections to predict likely text continuations, not to establish truth. A verified source provides evidence, so plausibility generated by the model should not be treated as verification.

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The framework identifies Delegation, Description, Discernment, and Diligence as its four AI Fluency dimensions. The other sets replace one or more of these dimensions with related but different terms.

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Delegation requires deciding what to entrust to AI while retaining hypothesis choices, decisions, and fieldwork. AI can help structure or criticize supplied material, but those human judgments and verification activities remain with the user.

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Professional use involves describing the task, evaluating the result, iterating or rejecting it, and then verifying, declaring, and taking responsibility for the final use. Producing or polishing an answer does not replace verification and accountability.

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AI hallucination risk is particularly high for precise figures, citations, sources, and URLs, so an unsupported number cannot serve as evidence. A plausible response may sound credible, but it does not replace verification through a primary source.

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Sycophancy leads an AI to validate the user's assumptions, whereas an adversarial prompt directs it to identify and rank counterarguments. Asking for a clearer summary does not create the deliberate contradiction required by adversarial prompting.

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Professional use requires that real personal data not be entered into AI tools and that interview quotations be anonymized beforehand. Retaining addresses or distinctive details can still expose identifiable individuals, even when names are removed.

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The framework delegates formatting, ideas, and criticism to the AI while retaining hypotheses, decisions, and fieldwork with the human. Having the AI generate alternatives does not transfer responsibility for the final professional judgment.

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The AI Fluency framework is organized around Delegation, Description, Discernment, and Diligence. The alternative sequences contain the same terms but do not preserve the framework's stated order.

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The required safeguard is to anonymize interview verbatims before use and to keep real personal data out of the AI tool. Requesting anonymization after uploading identifiable material does not prevent the initial exposure.

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A reliable workflow treats the output as potentially false, evaluates its accuracy, relevance, reasoning, and form, and then re-describes the request for two to four further rounds. The first output is therefore a draft for examination rather than a validated conclusion.

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Effective prompts specify the deliverable, context and purpose, examples, reasoning process, structure, format, constraints, and self-criticism. A polished tone or requested length can help, but those features alone do not provide the full guidance needed for critical work.

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A prompt should explicitly require the AI to acknowledge missing knowledge or unreliable sourcing rather than inventing a response. Encouraging a likely or confident answer when evidence is absent increases the risk of hallucination.

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An external fund-raising strategy must account for investor ownership dilution and a future exit. A bootstrap model instead emphasizes operating through customer revenue and cash independence.

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The LTV/CAC ratio compares customer lifetime value with customer acquisition cost, with a target greater than 3. Gross margin instead measures what remains after variable costs, while CAC payback measures recovery time.

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Runway is calculated as cash balance divided by burn rate, so $120{,}000 \div 20{,}000 = 6$ months. CAC and LTV/CAC use customer and marketing data rather than cash survival data.

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The balance sheet presents the company’s assets and liabilities at a particular date. The income statement covers products and charges over a period, while the cash flow statement tracks actual bank inflows and outflows.

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The financial triangle requires every decision to be examined through the balance sheet, income statement, and cash flow statement. The other combinations contain useful business measures but do not represent the three required financial perspectives.

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Amortization records a charge in the income statement and reduces the asset’s carrying value, but it does not represent a new cash movement after the purchase. A cash payment refers to an actual bank movement and is distinct from amortization.

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The assumptions tab contains blocks for revenue, costs, payroll, investments and amortization, capital and subsidies, and borrowing. Customer segmentation, valuation, and product design may inform a model but are not part of this specified seven-block structure.

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Revenue is derived by multiplying volume by price, giving $2{,}000 \times 15 = 30{,}000$ euros. Division, subtraction, and addition do not represent the stated revenue-driver relationship.

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Variable costs are expressed per unit multiplied by quantities sold, while purchases can create a cash outflow and leave unsold goods in inventory. Treating purchases as the income-statement charge confuses procurement cash timing with consumption or sales.

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Monthly principal repayment is $24{,}000 \div 24 = 1{,}000$ euros, and first-month interest is $0.06 \times 24{,}000 \div 12 = 120$ euros. The monthly payment would combine these two amounts, while the interest declines as principal is repaid.

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The cascade begins with revenue less variable costs to produce gross margin, then subtracts the listed operating and financial expenses to reach net income. EBITDA excludes amortization, whereas EBIT includes it, so treating those measures as identical would be incorrect.

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Corporate tax is calculated as 25% of cumulative positive pre-tax income, expensed in year N, and carried as a liability until payment in March of year N+1. The common error is confusing the accounting period of the expense with the later cash-payment date.

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The balance sheet balances assets such as net fixed assets, inventory, receivables, and December cash against capital, cumulative result, remaining debt, and unpaid tax. Income-statement measures such as revenue and gross margin do not form the required asset-and-liability structure.

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The process uses the AI to identify ten possible errors, classifies their severity, and then requires the team to verify each claim and document the verdict and justification. The AI generates accusations, but it does not replace the team’s verification responsibility.

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A genuine formula-column offset is founded, while the claim that tax is paid monthly is unfounded under the stated annual-tax timing. A conversion-rate criticism lacking supporting evidence remains to be verified rather than being accepted or rejected immediately.

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Sensitivity analysis changes selected assumptions, commonly by plus or minus 20%, and observes effects on cumulative net income and the lowest cash balance. An audit instead tests whether the existing model is correctly constructed, which is a different activity.

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Dates and names make fieldwork traceable and distinguish observed facts from text that merely sounds plausible when generated by AI. A plausible AI statement still requires independent evidence and does not become factual through wording alone.

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Each AI accusation requires human assessment and classification as founded, unfounded, or requiring verification. An AI accusation is not automatically true; a founded verdict requires independent human verification.

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The adverse audit combines AI error-finding with severity classification, independent verification, and documentation of each verdict and justification. The team, rather than the AI, is responsible for deciding whether a reported problem is valid.

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Identifying a false accusation demonstrates the rare competence of challenging an AI claim that sounds certain and separating rhetoric from evidence. A rejected claim does not prove the model is error-free, nor does it justify rejecting future criticisms without review.

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Sensitivity analysis changes four or five important variables by plus or minus 20% and observes the effects on cumulative net income and the lowest cash position. Interpreting which assumptions require real-world testing is a separate step from measuring model consequences.

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Sensitivity analysis reveals the assumptions and thresholds that determine whether the model survives, so the plan becomes a map of bets. A prediction claims knowledge of the future, which is different from identifying model vulnerabilities.

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Contradictory sourcing requires checking the cited primary sources, recording discrepancies, and treating an untraceable figure as a hypothesis or removing it. Consistency with a plan does not establish that a market figure is sourced accurately.

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An agent acts through chained steps and tools toward an objective bounded by scope and an audited deliverable. A chatbot primarily answers questions, even when its answers draw on multiple documents.

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Preparation includes the eight-minute investor-format pitch, public cross-team audit draw, five written and ranked questions with a one-page investment opinion, and an individual three-minute examination. The written audit opinion and the individual examination serve different purposes and are not interchangeable.

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The dossier is submitted the day before the committee at 17:00 and contains the specified seven-tab workbook, four-page maximum memo, AI logbook, four field proofs, and usage declarations. The oral committee occurs afterward and is not a component that replaces the dossier.

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The pitch must form a connected investment case from the problem and solution through sourced market evidence, traction, team, cash-related model figures, and the requested use of money. Topics such as office costs or presentation design do not substitute for that full chain.

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The session follows the sequence of rules, pitch, audit questions, individual examination, deliberation and voting, and final debrief. During the presentation the team acts as founders, while the rest of the session requires an investor perspective.

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The opinion must combine a decision, precise strengths, severity-ordered risks, conditions before signing, and reflection on the strongest and weakest difficult-question answers. A general compliment such as calling a team good is not a precise, evidence-based strength.

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An execution risk concerns whether the team can deliver the project, which matches the stated situation. A market risk instead concerns whether anyone will buy the offering.