PMI Certification PMI-RMP Book

 A decision tree analysis is a tool that helps to evaluate and select the best option among different alternatives based on costs and probabilities. A decision tree analysis uses a graphical representation of a decision problem, where each node represents a decision point, a chance event, or an outcome. The branches of the tree show the possible choices, events, or consequences that can occur at each node. The end nodes of the tree show the expected value or payoff of each option, which is calculated by multiplying the probability and the cost or benefit of each outcome. A decision tree analysis can help to compare the expected values of different options and choose the one that maximizes the benefit or minimizes the cost1. A decision tree analysis can also help to incorporate uncertainty and risk into the decision making process, as it shows the range of possible outcomes and their likelihoods2. Therefore, the project manager should perform a decision tree analysis to evaluate and select the best option based on costs and probabilities for a mega facility development project. Performing a FMECA fault tree analysis, conducting a sensitivity analysis, or conducting an analytic hierarchy process are not the best options to evaluate and select the best option based on costs and probabilities. A FMECA fault tree analysis is a tool that helps to identify and analyze the potential causes and effects of failures in a system or process. It uses a graphical representation of a failure event, where each node represents a basic or intermediate event that contributes to the failure. The branches of the tree show the logical relationships between the events, using AND or OR gates. A FMECA fault tree analysis can help to calculate the probability and severity of failures, as well as to prioritize and mitigate the risks3. However, a FMECA fault tree analysis does not help to compare different options or alternatives, as it focuses on a single failure scenario. Conducting a sensitivity analysis is a tool that helps to measure how the uncertainty in the input variables of a model affects the output or outcome of the model. It uses a graphical or numerical representation of the relationship between the input and output variables, showing how the output changes when the input changes. A sensitivity analysis can help to identify the most critical or influential variables, as well as to test the robustness or reliability of the model4. However, a sensitivity analysis does not help to compare different options or alternatives, as it focuses on a single model or option. Conducting an analytic hierarchy process is a tool that helps to evaluate and select the best option among different alternatives based on multiple criteria. It uses a mathematical method of pairwise comparison, where each alternative is compared to each other in terms of each criterion. The results of the comparisons are then aggregated into a matrix, which shows the relative importance or preference of each alternative. An analytic hierarchy process can help to rank the alternatives and choose the one that best satisfies the criteria5. However, an analytic hierarchy process does not help to incorporate costs and probabilities into the decision making process, as it relies on subjective judgments and preferences. References: 1, 2, 3, 4, 5.

A decision tree analysis is a quantitative risk analysis technique that helps evaluate and select the best option based on costs and probabilities. It visually represents different decision paths and their associated probabilities, allowing the project manager to compare and select the most appropriate option for the project.

A risk checklist is a tool for identifying risks based on historical information and knowledge from similar projects. It is a list of potential risk sources or categories that can be used to prompt the project team to consider possible risks that may affect the project. A risk checklist should include information that is relevant and useful for identifying risks, such as lessons learned from similar completed projects and previous project risks that may be relevant to this project. These two pieces of information can help the project manager to learn from past experiences and avoid repeating the same mistakes or overlooking the same threats or opportunities. A risk checklist should not include information that is not directly related to risk identification, such as budget variance data from previously completed projects, project scope and cost management plans from previous projects, or stakeholder analysis metrics from projects with similar risk profiles. These pieces of information may be useful for other aspects of project management, such as planning, monitoring, or controlling, but they are not helpful for identifying risks on a project. References: PMI. (2017). A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Sixth Edition. Chapter 11: Project Risk Management, p. 397. 5

Lessons learned and previous project risks are valuable sources of information for creating a risk checklist. They provide insights into potential risks that may impact the current project and help the project manager develop appropriate risk responses. Budget variance data, project scope and cost management plans, and stakeholder analysis metrics, although useful, are not directly related to risk identification. (Reference: Project Management Institute. A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Sixth Edition, Section 11.2)

Quantitative risk analysis helps determine the minimum acceptable level of exposure and impact for each risk. It also helps to understand if the maximum level of exposure has been reached before escalating the risk. (Reference: PMBOK Guide, 6th Edition, p. 423)

The team should perform quantitative risk analysis, which is the process of numerically analyzing the effect of identified risks on overall project objectives. Quantitative risk analysis can help the team to establish the minimum acceptable level of exposure and impact for each risk, as well as the maximum level of exposure before escalation. Quantitative risk analysis can also provide probabilistic estimates of project outcomes, such as cost and schedule, and support risk prioritization and decision making. References: PMI, A Guide to the Project Management Body of Knowledge (PMBOK® Guide), Sixth Edition, 2017, p. 399; PMI, The Standard for Risk Management in Portfolios, Programs, and Projects, 2019, p. 69.

Project risks should be closed when the stakeholders agree a risk is no longer applicable. This ensures that risks are actively managed and only relevant risks are considered throughout the project lifecycle.

According to the PMI Risk Management Professional (PMI-RMP) Reference Materials, project risks are uncertain events or conditions that may have a positive or negative effect on one or more project objectives1. Project risks can be closed when they are no longer applicable to the project or its activities. The process of closing project risks involves verifying that the risk responses have been completed, documenting the outcomes, and evaluating the effectiveness of the risk management process2. The decision to close a project risk should be made by the stakeholders who are responsible for or affected by the risk, as they are the ones who can determine whether the risk is still relevant or not. Therefore, the correct answer is B. When the stakeholders agree a risk is no longer applicable.

References: 1: PMI, A Guide to the Project Management Body of Knowledge (PMBOK® Guide), Sixth Edition, 2017, p. 397 2: PMI, Practice Standard for Project Risk Management, 2009, p. 111