PMP Practice Questions #174
Which of the following techniques can be applied to drive a confidence factor in the estimates and determine the potential range of estimation?
A. Monte Carlo Simulation
B. Bottom-Up Estimate
C. Cone of Uncertainty
D. Parametric Estimate
Analysis
This question is about understanding which technique can be used to establish a confidence factor in estimates and determine the potential range of those estimates. The focus is on identifying the tool or technique that allows you to assess the range of possible outcomes in project estimations by using a probabilistic approach, often involving simulations.
Analysis of Options:
Option A: Monte Carlo Simulation: Monte Carlo Simulation is a technique that uses statistical methods and random sampling to model the probability of different outcomes in a process. By running multiple simulations, it generates a range of possible outcomes and provides a confidence level in the estimates. This method is specifically designed to determine the potential range of estimates and assess their variability, making it a strong candidate for this question.
Option B: Bottom-Up Estimate: Bottom-Up Estimating involves breaking down the project into smaller components and estimating the cost or duration of each component individually. While this method provides detailed and accurate estimates by aggregating these smaller parts, it does not inherently provide a range of estimates or assess the confidence factor. It focuses on generating precise estimates but without the probabilistic element needed to determine a range.
Option C: Cone of Uncertainty: The Cone of Uncertainty is a model used to represent the level of uncertainty in project estimates over time. It illustrates how uncertainty decreases as more information becomes available and the project progresses. While it visualizes the potential error range in estimates at various stages, it is not a technique used to drive confidence factors or to determine specific ranges in estimates. It is more of a conceptual tool rather than a simulation or estimation technique.
Option D: Parametric Estimate: Parametric Estimating uses statistical relationships between historical data and other variables to estimate project costs or durations. This method provides estimates based on parameters but, like Bottom-Up Estimating, it does not inherently provide a range of estimates or assess confidence levels. It is a deterministic method that focuses on generating a single estimate rather than a range of possibilities.
Conclusion: Option A (Monte Carlo Simulation) is the correct answer. Monte Carlo Simulation is specifically designed to generate a range of possible outcomes and determine the confidence factor in estimates through probabilistic methods. It allows project managers to understand the variability and uncertainty in estimates, which aligns perfectly with the requirement of driving a confidence factor and determining the potential range of estimation. Option C (Cone of Uncertainty) is more of a conceptual model rather than a tool for generating estimate ranges, and Options B and D (Bottom-Up and Parametric Estimates) focus on generating precise estimates without the probabilistic range assessment needed here.
PMP Exam Content Outline Mapping
| Domain | Task |
|---|---|
| Process | Task 5: Plan and manage budget and resources |
| Process | Task 6: Plan and manage schedule |
Topics Covered
- Estimations
- Simulations
