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| Certification Vendor: | Project Management Institute (PMI) |
|---|---|
| Exam Name: | PMI Certified Professional in Managing AI (PMI-CPMAI)™ Certification Exam |
| Exam Number: | PMI-CPMAI |
| Passing Score: | Not publicly disclosed |
| Real Exam Qty: | 120 (including 20 unscored pre-test questions) |
| Related Certifications: | Project Management Professional (PMP)® PMI Agile Certified Practitioner (PMI-ACP)® |
| Exam Price: | $699 (PMI members), $899 (non-members) |
| Certificate Validity Period: | 3 years |
| Exam Format: | Scenario-based, Multiple-choice, Application-focused |
| Exam Duration: | 160 minutes |
| Available Languages: | Japanese, French, Spanish (Latin America), English, German, Korean, Brazilian Portuguese, Arabic, Chinese (Simplified), Chinese (Traditional) |
| Recommended Training: | PMI-CPMAI Exam Prep Course |
| Exam Registration: | PMI Official Registration Pearson VUE Scheduling |
| Sample Questions: | PMI PMI-CPMAI Sample Questions |
| Exam Way: | Computer-based test at test center or online proctored via Pearson VUE |
| Pre Condition: | Minimum age 18; recommended completion of PMI-CPMAI official training; no formal education/experience requirements |
| Official Syllabus URL: | https://www.pmi.org/-/media/pmi/documents/public/pdf/certifications/pmicpmai-exam-content-outline2025-updated.pdf |
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NEW QUESTION # 71
An aerospace company is exploring the potential of using AI for predictive maintenance. They need to determine if AI is the appropriate solution while weighing factors such as scalability, existing non-AI solutions, and data availability.
What should the project manager do first?
Answer: A
Explanation:
The best answer is B. Evaluate the scalability of current non-AI solutions . In PMI-CPMAI, the project manager should not assume that AI is the right answer simply because the problem is important or data-rich.
The methodology emphasizes first determining whether an AI approach is actually needed and comparing it with non-cognitive or non-AI alternatives before moving deeper into data planning or implementation. PMI's official exam content outline includes conducting AI go/no-go assessments , separating cognitive from non-cognitive tasks , and aligning the solution approach to the real business need. It also stresses that understanding the AI pattern involved helps teams choose the right data strategy and scope responsibly.
Predictive maintenance is a recognized AI pattern area, but that still does not remove the need to assess whether a simpler existing solution can scale sufficiently.
Option A matters, but data suitability should be examined after the team has confirmed that AI is justified.
Option C is part of business-case work, and Option D is even later because operationalization planning only makes sense once AI has been chosen. Since the question asks what should be done first while weighing existing alternatives, PMI-aligned logic supports evaluating whether the current non-AI approach can already meet the need at scale.
NEW QUESTION # 72
A healthcare provider plans to deploy an AI system to predict patient readmissions. The project manager needs to conduct a risk assessment to ensure patient safety and data integrity. What is an effective method to help ensure the AI system adheres to ethical standards?
Answer: A
Explanation:
PMI guidance for responsible and trustworthy AI stresses that ethical performance is not a one-time checkbox; it requires ongoing oversight, including transparency, accountability, and continuous controls. PMI- CPMAI's exam outline explicitly highlights maintaining audit trails for algorithmic decision-making, implementing compliance monitoring mechanisms, and managing accountability documentation- foundational practices that align directly with continuous monitoring and auditing. In high-stakes healthcare use cases like readmission prediction, model drift, data drift, and shifting patient populations can degrade performance and fairness over time, which can create patient safety risks. Continuous monitoring enables the team to detect deteriorating accuracy, emerging bias, and unexpected failure modes early; auditing supports traceability of decisions, data lineage, and adherence to governance requirements. PMI also emphasizes that ethical AI demands validation and transparency, noting that accountability and continuous monitoring are crucial to maintain ethical standards and minimize undesirable outcomes. Encryption (A) protects confidentiality, and explainability (B) supports transparency, but neither alone ensures sustained ethical compliance. Stakeholder impact analysis (D) is valuable during assessment, yet monitoring/auditing is the most direct operational method to ensure ethics remain intact after deployment.
NEW QUESTION # 73
An AI project team has prepared the data and is ready to proceed with model development.
Which action should the project manager perform next?
Answer: A
Explanation:
Once data preparation is complete and the team is ready for model development, PMI-aligned AI lifecycle guidance calls for clear definition and documentation of performance metrics and success criteria before training models. The project manager should ensure that everyone agrees on which metrics will be used (e.g., accuracy, precision, recall, F1, AUC, business KPIs) and what thresholds will be considered acceptable. This supports traceability, objective evaluation, and transparent go/no-go decisions in later stages.
Because the question states that the data is already prepared and the team is ready to proceed, it implies that initial data quality activities have already occurred. Repeating a "final assessment of data quality" (option A) is less critical at this specific point than locking in evaluation metrics. Go/no-go questions (option C) and scalability reporting (option D) depend on having those metrics explicitly defined; they are downstream decisions and artifacts. PMI-style AI guidance stresses that model development should be driven by pre-defined, documented performance metrics that connect technical outputs to business value and risk tolerances. Therefore, the next action for the project manager is to document the performance metrics for the model.
NEW QUESTION # 74
A project team is using a generative AI assistant to draft stakeholder communications. The drafts are often generic and miss project constraints. What is the most likely cause?
Answer: B
Explanation:
PMI guidance on using GenAI highlights that prompts must provide context, guidance, and constraints; otherwise outputs tend to be vague or unhelpful. If stakeholder communications miss constraints (scope boundaries, timeline, dependencies, risk posture), the most likely cause is insufficient prompt specificity-e.
g., missing audience, intent, tone, project phase, constraints, and success criteria. PMI explains that the utility of GenAI outputs is strongly tied to the granularity of input: when prompts lack detail, results often become generic and misaligned with the real need. In CPMAI-aligned execution, this is addressed by iteratively refining prompts (diverge then converge), adding structured context such as assumptions, constraints, and acceptance criteria, and validating outputs against governance expectations for accuracy and appropriateness.
Compute (C) may affect latency, not relevance; "model efficiency" (B) is not a driver of generic content; monitoring (D) improves trustworthiness rather than causing generic outputs. The PMI-consistent diagnosis is insufficient contextual prompting.
NEW QUESTION # 75
A healthcare organization plans to use an AI solution to predict patient readmissions. The data science team needs to identify data sources and ensure data quality.
Which method will meet the project team's objectives?
Answer: D
Explanation:
In PMI-CPMAI's treatment of data for AI, especially in sensitive domains like healthcare, the first responsibility of the project and data science teams is to understand and assess data quality and suitability before model development. The guidance states that AI teams should "systematically profile candidate data sources to evaluate completeness, consistency, validity, and coverage of key populations and variables relevant to the use case." Data profiling tools are highlighted as a practical means to inspect distributions, missing values, outliers, and anomalies across structured clinical, administrative, and claims data.
For a patient readmission prediction use case, PMI-CPMAI stresses that teams must identify which sources (EHR, discharge summaries, lab results, prior admissions, demographics, social determinants, etc.) are available and then "quantify data quality metrics such as completeness and timeliness to determine whether the dataset is fit for training and deployment." While techniques such as augmentation or real-time validation might be valuable later, they build upon an initial understanding obtained via profiling. Operationalizing a catalog supports governance and discovery but does not directly satisfy the immediate need to measure data quality.
Therefore, the method that best meets the objective of identifying data sources and ensuring data quality is to use data profiling tools to assess data completeness and other quality dimensions, providing an evidence-based foundation for subsequent preprocessing, feature engineering, and model training.
NEW QUESTION # 76
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