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| Certification Vendor: | PMI (Project Management Institute) |
|---|---|
| Exam Name: | PMI Certified Professional in Managing AI |
| Exam Number: | PMI-CPMAI |
| Exam Duration: | 150 minutes |
| Exam Price: | USD $520 for PMI members / USD $670 for non-members |
| Real Exam Qty: | 120 |
| Related Certifications: | PMI-ACP (Agile Certified Practitioner) PMI-PBA (Professional in Business Analysis) PMP (Project Management Professional) |
| Certificate Validity Period: | 3 years |
| Passing Score: | Not publicly disclosed |
| Exam Format: | Multiple Choice |
| Available Languages: | English |
| Sample Questions: | PMI PMI-CPMAI Sample Questions |
| Exam Way: | Computer-based testing at PMI-authorized Pearson VUE test centers worldwide |
| Pre Condition: | No mandatory prerequisites. However, basic project management knowledge (PMP or equivalent experience) is recommended. Secondary school diploma required if pursuing PMI membership. |
| Official Syllabus URL: | https://www.pmi.org/certifications/certified-professional-managing-ai-cp-ai |
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NEW QUESTION # 17
A project manager is tasked with ensuring that an AI project complies with data regulations before data collection begins. This involves identifying all necessary requirements for trustworthy AI, including ethical considerations, privacy, and transparency.
What should the project manager do first?
Answer: D
Explanation:
The best answer is B. Perform a comprehensive assessment of data regulations and compliance requirements . In PMI-CPMAI, trustworthy AI begins with understanding the regulatory and policy environment before execution activities move forward. The exam content outline specifically includes monitoring regulatory and policy compliance, ensuring adherence to sector-specific requirements, coordinating with legal and compliance teams, and maintaining documentation for audits. It also explicitly includes privacy impact assessments, compliance with GDPR/CCPA, and secure data handling throughout the AI lifecycle. That makes a broad compliance assessment the logical first step, because the team must know which laws, standards, and ethical obligations apply before it can design governance, meetings, or data strategies.
Option A is important, but a governance framework should be built after the requirements are identified.
Option C may help collaboration, but discussion is not the first control activity. Option D addresses execution planning, not the prerequisite compliance review. PMI's structure places regulatory awareness and privacy oversight at the front of trustworthy AI work, so the project manager should begin by determining the full compliance landscape before data collection starts.
NEW QUESTION # 18
A project manager is preparing a contingency plan for an Al-driven customer service platform. They need to determine an effective strategy to handle potential system downtimes.
Which strategy addresses the project manager's objective?
Answer: A
Explanation:
PMI-CP-oriented AI risk and resilience practices emphasize continuity of service and graceful degradation when AI systems fail or are temporarily unavailable. For an AI-driven customer service platform, the contingency plan should ensure that customers still receive some level of assistance even when the main AI system is down. An automated fallback chatbot with limited capabilities (option C) embodies this principle by providing a simplified yet always-available channel.
Such a fallback system might offer only basic FAQs, simple intent handling, or routing to human agents, but it maintains a consistent experience and avoids a complete service outage. This is a classic "fail-soft" or
"degraded mode" strategy often highlighted in AI operations and MLOps guidance: if the primary model or service is unavailable, the system automatically switches to a simpler, more reliable backup.
Logging systems (option A) are important for diagnosis but do not directly serve customers during downtime.
Manual override for critical queries (option B) and extensive staff training (option D) are valuable complementary controls, yet they are human-dependent and slower to activate. PMI-style AI contingency planning stresses automated, pre-defined fallback paths wherever possible. Hence, developing an automated fallback chatbot with limited capabilities best addresses the objective of handling potential system downtimes.
NEW QUESTION # 19
A hospital wants to develop a medical records system with the primary goal of minimizing or eliminating paper records. They have identified where the cognitive AI solution will be applied. In addition, business objectives have been quantified and key performance indicators (KPIs) have been determined.
What else needs to be done to progress to the next Cognitive Project Management for AI (CPMAI) phase?
Answer: D
Explanation:
CPMAI's Phase I - Business Understanding focuses on clearly defining the business problem, aligning AI efforts with organizational goals, and establishing measurable success criteria including ROI expectations.
PMI's own overview of CPMAI notes that in this phase, teams should "set success criteria" and define both KPIs and ROI expectations so that everyone understands what success and failure look like before moving on Other CPMAI-oriented resources describe Phase I artefacts such as a problem statement, AI pattern fit, stakeholder analysis, and a preliminary ROI sheet that quantifies expected benefits and costs. In the scenario, the hospital has already identified where the cognitive solution will be applied, quantified business objectives, and defined KPIs. What is still missing from the core Phase I deliverables is a clear view of the project's expected ROI, linking reduced paper records and process improvements to financial and operational value.
Beginning prototype development (B) belongs to later modeling phases, exploring external data sources (D) is part of Data Understanding, and interdepartmental strategies (C) are broader organizational actions rather than a specific Phase I gating item. To progress to the next CPMAI phase in a way that matches the methodology, the team must determine the project ROI, making option A the correct answer.
NEW QUESTION # 20
A logistics company is operationalizing an AI system to improve delivery times. The project team needs to identify performance constraints that may impact the AI solution.
Which method should the project manager use to meet the team's objective?
Answer: D
Explanation:
When operationalizing an AI system to improve delivery times, PMI-style AI project guidance stresses the importance of identifying constraints and assumptions early, before heavy investment in build-out. A preliminary feasibility study is the standard method to surface key performance constraints that might impact the AI solution. This includes analyzing current logistics processes, data availability and latency, network conditions, service-level expectations (e.g., maximum response times for route optimization), infrastructure capacity, and integration limits with existing systems.
A feasibility study helps the team clarify: what throughput is required, how frequently predictions must be updated, what real-time vs. batch constraints exist, and whether current hardware, APIs, and data pipelines can support those requirements. This aligns with PMI-CPMAI's emphasis on evaluating technical, data, and organizational readiness before committing to full-scale deployment.
Benchmarking competitors (option A) may highlight external performance targets but does not systematically uncover the internal constraints. Implementing advanced visualization tools (option B) can help later with monitoring and communication but does not, by itself, identify constraints. Training employees on AI ethics (option D) is valuable from a governance standpoint, yet it does not address performance limitations. Thus, the method that directly meets the objective of identifying performance constraints is to conduct a preliminary feasibility study.
NEW QUESTION # 21
An AI project team with a manufacturing company needs to ensure data integrity before moving to model development. They discovered some data inconsistencies due to manual entry errors.
What is an effective method that helps to ensure data integrity?
Answer: A,D
Explanation:
In AI data management, PMI-CPMAI highlights data integrity as the property that data remains accurate, consistent, and reliable over its lifecycle. When the team discovers inconsistencies due to manual entry errors, the most direct and effective control is to prevent bad data at the point of capture. This is achieved by implementing real-time data validation rules-for example, enforcing allowed ranges, formats, mandatory fields, cross-field consistency checks, and lookup constraints before a record is accepted.
PMI's AI data practices emphasize that "controls at data entry" are preferable to downstream correction because they reduce rework, lower the risk of propagating errors into models, and create cleaner training datasets from the outset. Although automating data entry (option B) can also reduce manual errors, it does not, by itself, guarantee integrity if upstream systems or processes are flawed. Regular audits (option C) are useful as a monitoring mechanism, but they are periodic and reactive rather than preventive. Using ML algorithms to detect and correct errors (option D) adds complexity and itself relies on having sufficiently good data.
Thus, in alignment with PMI-style AI governance and quality management, real-time data validation rules are the most effective method named here to ensure data integrity before moving to model development.
NEW QUESTION # 22
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