PMP · Question #972
During a risk management meeting, most of the project team members use a computer to iterate the quantitative risk analysis model numerous times. There will be a cumulative probability distribution…
The correct answer is D. Monte Carlo analysis. The project team is performing a quantitative risk analysis using iterative simulations to generate a cumulative probability distribution (S-curve), which is characteristic of Monte Carlo analysis.
Question
During a risk management meeting, most of the project team members use a computer to iterate the quantitative risk analysis model numerous times. There will be a cumulative probability distribution (S-curve) representing the probability of achieving any particular outcome. Which method should the project manager use to assess and manage project risks?
Options
- AInfluence diagrams
- BSensitivity analysis
- CDecision tree analysis
- DMonte Carlo analysis
How the community answered
(41 responses)- A17% (7)
- B7% (3)
- C2% (1)
- D73% (30)
Why each option
The project team is performing a quantitative risk analysis using iterative simulations to generate a cumulative probability distribution (S-curve), which is characteristic of Monte Carlo analysis.
Influence diagrams visually represent decisions, uncertainties, and objectives but do not involve iterating models to produce probability distributions.
Sensitivity analysis determines which project risks have the most potential impact by examining the extent to which the uncertainty of each project element affects the outcome, typically displayed as a tornado diagram.
Decision tree analysis is a diagrammatic technique used to evaluate possible outcomes and their probabilities when making choices under uncertainty, but it does not involve iterating a model numerous times to generate an S-curve.
Monte Carlo analysis is a quantitative risk analysis technique that uses simulations to model the probability of different outcomes in a process that cannot easily be predicted due to random variables. It repeatedly runs calculations using random inputs to produce a range of possible outcomes and their probabilities, often resulting in an S-curve showing the cumulative probability distribution.
Concept tested: Quantitative risk analysis; Monte Carlo simulation
Source: https://www.pmi.org/pmbok-guide-standards/foundational/pmbok/risk-management/quantitative-risk-analysis
Topics
Community Discussion
6
Zlatan X.Jul 2, 2026D is the correct answer here. The key giveaway in the stem is the team iterating the quantitative risk model numerous times to generate a cumulative probability distribution, which is exactly what Monte Carlo analysis does. At the program level, we rely on this method to understand the probability of achieving cost and schedule targets across interdependent components. The S-curve output is the standard signature of a Monte Carlo simulation, none of the other options produce that.
Grace U.Jul 5, 2026I first leaned toward B because sensitivity analysis also shows up in quantitative risk analysis, but the giveaway here is the computer iterating the model numerous times to produce that S-curve, which is exactly how Monte Carlo simulation works.
Prof. SaraJul 5, 2026Saw this exact scenario on my real exam last month, and the keywords "iterate" plus "numerous times" plus "S-curve" immediately pointed me to D. Mnemonic to keep handy: "Many Iterations Make S-curves" maps to Monte Carlo, which falls under the Quantitative Risk Analysis tools in the PMBOK Risk Management domain.
Nina C.Jul 5, 2026I first leaned toward B because sensitivity analysis felt like the obvious risk tool, but the dead giveaway is iterating the model numerous times to produce that S-curve, which is exactly how Monte Carlo simulation works.
Mei-Ling H.Jun 29, 2026C because decision trees map probability branches that produce S-curve distributions.
Nina C.Jul 1, 2026Think you might be overthinking this one, Mei-Ling. Decision trees do handle probability branches but the S-curve distribution question is pointing to D because logistic regression specifically produces that sigmoid S-curve output. Could you double-check the wording on your end to confirm we are looking at the same question stem?