Exam: PMI-CPMAI

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PMI-CPMAI PMI Certified Professional in Managing AI Exam

What you'll learn
Understand and apply the six phases of the CPMAI methodology to plan, manage, and deliver successful AI projects.
Identify and use the seven core AI patterns (conversational, recognition, predictive analytics, etc.) to match business needs with the right AI approach.
Prepare, manage, and evaluate data for AI projects by applying best practices in data understanding, preparation, and governance.

Project Management Institute (PMI)’s acquisition of Cognilytica in September 2024 has significantly strengthened PMI’s advanced AI resources, empowering project managers to implement AI initiatives across organizations worldwide.

Building on the Cognitive Project Management in AI (CPMAI) Methodology, PMI enhanced the certification and developed the PMI Certified Professional in Managing AI (PMI-CPMAI). The focus of the certification is the skill set necessary to successfully build AI implementations through the CPMAI Methodology rather than general project management knowledge and skills covered in our broader certifications.

A rigorous job analysis using the Developing a Curriculum (DACUM) methodology was conducted to ensure alignment with real-world professional practice and industry needs in AI development. PMI chose eight AI project and product management subject matter experts to contribute their knowledge during a DACUM workshop held in May 2025 and facilitated by The Ohio State University.

The Ohio State University Center on Education for Employment (CETE) administered a survey for PMI to verify which tasks, knowledge, and skills comprise the work done by AI project and product management professionals. The DACUM job-task analysis identified 68 tasks that may be necessary for these professionals to perform in their job activities. CETE staff followed up on the work of the DACUM committee by requesting ratings from members of the population (i.e., AI project and product managers). Respondents provided ratings of duties and tasks regarding importance, frequency, and difficulty. Composite criticality scores were calculated based on importance and frequency ratings. The Task Verification Report can be found here.

The PMI-CPMAI Exam Prep Course and PMI-CPMAI exam are vital components of earning this professional certification. The examination reflects proven, vendor-agnostic best practices for artificial intelligence (AI), machine learning (ML), advanced data analytics, intelligent automation, and AI projects of any size. All examination questions have been developed and reviewed by AI subject matter experts.

This examination tests a professional’s ability to apply the CPMAI Methodology and manage AI initiatives from inception through operationalization, addressing the unique challenges and requirements that distinguish AI projects from traditional software development initiatives. The PMI-CPMAI certification demonstrates competency in managing data-driven, iterative AI projects while ensuring responsible, ethical, and trustworthy AI implementation practices. These questions are mapped against the PMI-CPMAI Examination Content Outline to ensure that an appropriate number of questions are in place for a valid examination.

Implement Trustworthy AI principles (ethical, responsible, transparent, governed, explainable) to ensure responsible and sustainable AI solutions.
Differentiate between Proof-of-Concepts and real-world Pilots to avoid the common trap of AI projects that never reach production.
Detect and manage model drift and data drift to keep AI systems accurate, reliable, and aligned with business goals over time.

Domain
Percentage of Questions on Test
Support Responsible and Trustworthy AI Efforts 15%
Identify Business Needs and Solutions 26%
Identify Data Needs 26%
Manage AI Model Development and Evaluation 16%
Operationalize AI Solution 17%

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Sample Question and Answers

QUESTION 1
An AI project team is assessing the scalability of a healthcare solution. Which factor should the project manager consider to help ensure the solution is scalable?

A. Compliance with data regulations
B. Ability to handle increased loads
C. Human oversight requirements
D. Integration with the existing infrastructure

Answer: B

Explanation:
Scalability in AI initiatives is defined within PMI-CPMAI as the solutions ability to maintain
performance, reliability, and accuracy when subjected to increased data volume, user demand, or
computational workload. The PMI AI Management Framework emphasizes that an AI system must
be architected to oeexpand capacity, data throughput, and model processing without degradation of
service quality (PMI-CPMAI Learning Path: AI Solution Design and Implementation).
PMI further states that when assessing scalability, project managers must evaluate whether the AI
system can oeadapt to higher-than-forecast usage levels, larger datasets, and future feature growth
using modular and distributed architectures. The official guidance notes that scalable AI solutions
often rely on elastic cloud environments, containerized deployments, and horizontally scalable
compute layers. This is captured in PMIs explanation that oeAI performance must remain stable as
demand increases, requiring testing against progressively higher loads to validate computational
capacity, latency thresholds, and throughput expectations (PMI-CPMAI: AI Technical Foundations).
The project managers responsibility includes verifying that the model pipelines, data ingestion
systems, and inferencing services continue to operate effectively under expanded operational
demand. PMI stresses that this factor”ability to handle increased loads”is the cornerstone of
scalability evaluation, whereas regulatory compliance, human oversight, and integration concerns,
while important, relate to governance, ethics, and interoperability rather than scalability.
Therefore, the correct factor that ensures AI scalability is the solutions ability to handle increased loads.


In a clustering analysis for data use, the project team finds that the clusters are not meaningful and
do not provide actionable insights. Which activity should the project manager do with the project team?

A. Assess the trade-offs of the various algorithms.
B. Establish data governance protocols.
C. Identify the data gaps and address deficiencies.
D. Conduct an algorithm analysis on the data sources.

Answer: C

Explanation:
In the PMI approach to managing AI initiatives, clustering and other unsupervised techniques
depend heavily on data quality, completeness, and relevance. When clusters are not meaningful or
actionable, the primary recommended action is to reassess and improve the underlying data rather
than immediately changing algorithms. PMI guidance on AI data practices emphasizes that AI teams
should oeensure that datasets are sufficiently complete, representative, and aligned with the business
problem before drawing conclusions from models. This includes identifying data gaps, missing
attributes, bias, and noisy or inconsistent records, and then addressing these deficiencies through
improved collection, integration, cleaning, and feature engineering.
The PMI-CPMAI content further stresses that data readiness assessments and iterative refinement of
data are critical tasks before and during model development. Poor or incomplete data typically leads
to patterns that do not map to real-world segments or behaviors, which is exactly what happens
when clusters lack business meaning. While algorithm selection and trade-off analysis are also
important, PMI characterizes them as secondary to ensuring that data is oefit for purpose for the
targeted use case. Therefore, the project manager should lead the team to identify data gaps and
address deficiencies, which best aligns with PMIs emphasis on data quality as the foundation of
reliable AI outcomes.

QUESTION 3
A government agency plans to increase personalization of their AI public services platform.
The agency is concerned that the personal information may be hacked.
Which action should occur to achieve the agencys goals?

A. Standardize service protocols to deliver services for reliability.
B. Educate employees on new technologies so they can help users.
C. Develop user-friendly interfaces which are tested by users.
D. Enhance data privacy to increase user trust and confidence.

Answer: D

Explanation:
PMIs guidance on responsible and trustworthy AI highlights data privacy, security, and protection of
personal information as central when deploying AI in public-sector services. For personalization in egovernment
platforms, PMI notes that organizations must oedesign AI solutions that safeguard
personally identifiable information (PII) and comply with applicable privacy regulations, because
public trust is especially fragile in government contexts. Strengthening privacy controls”through
techniques such as data minimization, access controls, encryption,
anonymization/pseudonymization, and robust cybersecurity practices”is described as a direct way
to protect citizens and maintain confidence in AI-enabled services.
The PMI-CPMAI materials also emphasize that user trust is a prerequisite for adoption, particularly
when AI uses sensitive personal or behavioral data. They state that AI programs should oeembed
privacy-by-design and security-by-design into architectures and workflows so that personalization
does not compromise confidentiality or expose citizens to heightened risk. While standardizing
protocols, educating employees, and improving interfaces have value, they do not address the
agencys specific concern about hacking and misuse of personal data. Enhancing data privacy and
security directly aligns with both the risk concern (hacking) and the strategic goal (personalized
services that users trust), making it the action most consistent with PMIs responsible AI and data
governance guidance.

QUESTION 4
Doctors have been utilizing a sophisticated AI-driven cognitive solution to help with diagnosing
illnesses. The AI system is integrated with several medical databases. This allowed the AI system to
learn from new patient data and adapt to the latest medical knowledge and practices.
The final project report indicated that the AI model had degraded over time, impacting reliability and effectiveness.
The AI system must comply with healthcare regulations from various countries.
What is the likely cause for the degradation issue?

A. Data drift affecting model precision
B. Changes in business model requirements
C. Inadequate initial model validation
D. Impact of data drift on model accuracy

Answer: D

Explanation:
PMIs AI management guidance explains that models deployed in dynamic domains”such as
healthcare”are particularly vulnerable to data drift, where oethe statistical properties of input data or
underlying real-world processes change over time, leading to performance degradation if models are
not monitored and updated. In the scenario, the cognitive diagnostic system is continuously
exposed to new patient data and evolving medical knowledge from multiple databases. PMI notes
that in such cases, oeAI models that are not periodically retrained, recalibrated, or revalidated against
current data will show reduced accuracy, reliability, and clinical usefulness over time.
The final report states that the models performance degraded over time, affecting reliability and
effectiveness, which is the hallmark symptom of data drift rather than an initial validation issue. PMICPMAI
content stresses setting up continuous monitoring, performance dashboards, and drift
detection mechanisms specifically to track oethe impact of data drift on model accuracy and business
or clinical outcomes, triggering model refresh or redesign when thresholds are exceeded. Changes
in business model requirements could affect alignment of outputs to objectives but would not, by
themselves, explain gradual technical degradation in predictions. Therefore, the most appropriate
cause, as framed in PMIs lifecycle and MLOps perspective, is the impact of data drift on model
accuracy, requiring ongoing monitoring and retraining to restore performance.

QUESTION 5
A company needs to launch an AI application quickly to be the first to the market. The project team
has decided to use pretrained models for their current AI project iteration.
What is a key result of leveraging pretrained models?

A. The team can see a reduction in the overall project timeline.
B. The team can encounter compatibility issues with existing systems.
C. The custom project development time can increase due to adjustments.
D. The project can face unexpected scalability challenges.

Answer: A

Explanation:
Within PMI-CPMAI, one of the key strategic levers for AI projects is reusing existing AI assets,
including pretrained models, to accelerate delivery and reduce initial development complexity. PMI
describes pretrained and foundation models as allowing organizations to oeleverage previously
learned representations so that teams can focus effort on adaptation, integration, and value
realization rather than building models from scratch. This often results in a shorter experimentation
cycle, reduced training time, and faster deployment, especially when speed-to-market is a primary objective.
PMI emphasizes that such reuse is particularly valuable in early iterations or minimum viable
products (MVPs), where the aim is to oedeliver functional AI capability quickly, validate value
hypotheses, and gather user feedback. While the team still needs to handle integration, fine-tuning,
and risk controls, the heavy lifting of initial training on massive datasets has already been done by
the pretrained model provider. This is contrasted with full custom model development, which PMI
characterizes as more resource-intensive and time-consuming, requiring substantial data
preparation, training, and optimization. Potential challenges such as compatibility or scalability must
be managed, but they are not the key, primary effect identified by PMI. The most central and
intended result of using pretrained models in this context is that the overall project timeline is
reduced, enabling the company to reach the market faster.

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