Quick Answer
The answer is sensitivity analysis, along with Monte Carlo simulation and decision tree analysis, as the three common techniques for quantitative risk analysis. Sensitivity analysis, often visualized as a tornado diagram, is correct because it examines how variations in individual project elements—like cost or duration—impact the overall project objective, helping prioritize which risks have the greatest potential effect. On the PMP exam, this topic tests your ability to distinguish quantitative methods from qualitative ones; a common trap is confusing sensitivity analysis with qualitative risk assessment, but remember that quantitative techniques rely on numerical data and probabilities. Monte Carlo simulation, for instance, uses repeated random sampling to model the range of possible outcomes and their likelihoods, providing a data-driven basis for contingency reserves. To recall these three, use the mnemonic “SMD” for Sensitivity, Monte Carlo, and Decision tree analysis.
This PMP practice question tests your understanding of process — managing technical aspects. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Which THREE are common techniques used for quantitative risk analysis? (Choose three.)
Monte Carlo simulation
Simulates project outcomes to assess risk.
SWOT analysis
Why wrong: Strategic planning technique, not quantitative risk analysis.
Checklist analysis
Why wrong: Used for risk identification, not quantitative analysis.
Decision tree analysis
Evaluates decisions under uncertainty.
Sensitivity analysis
Determines which risks have the most impact.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Monte Carlo simulation
Monte Carlo simulation is a quantitative risk analysis technique that uses repeated random sampling to model the probability distribution of potential project outcomes. It allows the project manager to see the range of possible completion dates or costs and their associated probabilities, providing a data-driven basis for contingency reserves.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Monte Carlo simulation
Why this is correct
Simulates project outcomes to assess risk.
Related concept
Read the scenario before looking for a memorised answer.
SWOT analysis
Why it's wrong here
Strategic planning technique, not quantitative risk analysis.
Checklist analysis
Why it's wrong here
Used for risk identification, not quantitative analysis.
Decision tree analysis
Why this is correct
Evaluates decisions under uncertainty.
Related concept
Read the scenario before looking for a memorised answer.
Sensitivity analysis
Why this is correct
Determines which risks have the most impact.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
The trap here is that candidates confuse qualitative techniques (like SWOT and checklist analysis) with quantitative techniques, because both are used in risk management but serve fundamentally different purposes—qualitative for prioritization and identification, quantitative for numerical modeling and probability analysis.
Detailed technical explanation
Monte Carlo simulation works by defining input variables (e.g., task durations) as probability distributions (e.g., triangular, beta-PERT) and then running thousands of iterations to generate a cumulative probability distribution of the output (e.g., project finish date). This technique is especially useful for complex projects with many interdependent tasks, where analytical solutions are impractical. In practice, tools like @RISK or Oracle Crystal Ball integrate with project schedules to perform this simulation, outputting a histogram and S-curve that show the likelihood of meeting specific targets.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A practitioner preparing for the PMP exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
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FAQ
Process — Managing Technical Aspects — This question tests Process — Managing Technical Aspects — Read the scenario before looking for a memorised answer..
The correct answer is: Monte Carlo simulation — Monte Carlo simulation is a quantitative risk analysis technique that uses repeated random sampling to model the probability distribution of potential project outcomes. It allows the project manager to see the range of possible completion dates or costs and their associated probabilities, providing a data-driven basis for contingency reserves.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This PMP practice question is part of Courseiva's free PMI certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMP exam.
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