CPMAI_v7 Questions - Truly Beneficial For Your PMI Exam (Updated 102 Questions)
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PMI CPMAI_v7 Exam Syllabus Topics:
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NEW QUESTION # 35
Enhancing and cleaning data is an important action during which phase of CPMAI?
- A. Phase IV
- B. Phase III
- C. Phase II
- D. Phase V
- E. Phase I
- F. Phase VI
Answer: B
Explanation:
Phase III: Data Preparation is dedicated to all data-centric tasks, including cleaning ("Clean data") and enhancement ("Enhance & Augment data") of raw inputs so they're ready for model consumption.
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NEW QUESTION # 36
Your team is working on an image recognition project, have collected the appropriate data for the project, and have picked a neural network algorithm. They are now ready to train their model.
In which phase of CPMAI is this done?
- A. Phase IV
- B. Phase II
- C. Phase V
- D. Phase I
- E. Phase III
- F. Phase VI
Answer: A
Explanation:
Phase IV: Model Development is explicitly where "Model Training / Model Building" occurs. This phase includes tasks for selecting modeling techniques, conducting hyperparameter optimization, and executing the actual training runs on the prepared datasets.
NEW QUESTION # 37
Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What's the most critical role to staff for in the Big Data / Data Engineering Environment?
- A. Data Scientists
- B. Senior management
- C. All roles are critical to staff in the Four different AI Tech environments
- D. Data Engineering
- E. Data Engineering and Data Scientists
Answer: D
Explanation:
CPMAI underscores that preparing and managing data pipelines is foundational: in Phase III: Data Preparation, teams "create a reusable data pipeline to collect, ingest, and prepare data for training" and for inference . Ensuring these pipelines exist and are maintained falls squarely to Data Engineering specialists.
While data scientists leverage these pipelines for modeling, the dedicated Data Engineering role is the single most critical hire to support a Big Data environment.
NEW QUESTION # 38
Your team is working on a new facial recognition application. Since this technology has the potential to be mis-used you think it's important to set guidelines for the proper use of this application and you want to make sure the AI system is built for some positive purpose. What area of Trustworthy AI does this best fall under?
- A. Governed AI
- B. Responsible AI
- C. Explainable AI
- D. Transparent AI
Answer: B
Explanation:
Under Domain VI: Trustworthy AI in the CPMAI Exam Content Outline, Responsible AI covers establishing policies, guidelines, and governance that ensure AI solutions are developed for positive, ethical use and prevent misuse. Defining proper-use guidelines and embedding ethical intent into facial recognition directly align with Responsible AI practices .
NEW QUESTION # 39
As an organization building an AI solution for your current customers based in NYC, but with possible plans for future expansion, how should you handle worldwide AI laws and regulations?
- A. Make sure to follow relevant data, privacy, and other important laws both in the US and where you're likely to expand to in the coming year
- B. Make sure to follow relevant data, privacy, and other important laws as it pertains to the United States
- C. You're too small of an organization to be worried about laws at the moment
- D. Make sure to follow relevant data, privacy, and other important laws as it pertains to NYC
Answer: A
Explanation:
CPMAI's Trustworthy AI - Navigating AI Regulations and Frameworks tasks require continuously monitoring "AI-relevant data privacy laws and regulations" both where you operate today and where you plan to expand . Furthermore, the Workbook's Task: Required Compliance with Regulations and Laws instructs teams to identify all laws and regulations that might apply based on location, industry, and other factors, ensuring legal and liability risks are addressed before operationalization .
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NEW QUESTION # 40
The team is evaluating where the sources of the data for training are. What phase of CPMAI are they in?
- A. Phase IV
- B. Phase V
- C. Phase I
- D. Phase III
- E. Phase II
- F. Phase VI
Answer: E
Explanation:
Phase II, Data Understanding, is explicitly focused on identifying data needs and sources-including
"Identify appropriate datasets for machine learning" and "Evaluate training data requirements" under the Managing the Data Understanding Phase tasks. This is the phase where teams determine where and how they will collect the training data .
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NEW QUESTION # 41
You recently completed an image recognition project at your company that was focused on identifying different types of cars. You have now been assigned a new image recognition project that is focused on identifying different types of animals. You know you can shortcut model development by using a specific technique.
What is this technique called?
- A. Pre-Trained Models
- B. Transfer Learning
- C. Reinforcement Learning
- D. Generative AI
Answer: B
Explanation:
Transfer learning is defined as a technique that "leverages a pretrained model (trained on a large, relevant data set) as a starting point for a new, related task," allowing you to reuse learned representations (e.g., convolutional layers trained on one image domain) when training on another domain (like cars # animals) .
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NEW QUESTION # 42
You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?
- A. Governed AI
- B. Ethical AI
- C. Explainable AI
- D. Transparent AI
- E. Responsible AI
Answer: C
Explanation:
In CPMAI's Trustworthy AI requirements, the Explainable AI layer specifically covers "legal, compliance, and risk considerations [that] might require that the AI system used for decision-making ... provide some level of explainability for audit, root cause analysis, or other purposes." Providing applicants with the ability to contest hiring decisions depends on furnishing clear, human-understandable explanations of how and why the model arrived at its result-exactly the focus of the Required AI Explainability Considerations task.
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NEW QUESTION # 43
You are being tasked to manage an AI project at your company and you need to identify which project to start with. What's the best way to approach this?
- A. Ask key stakeholders from your group and find a small problem that would have a big return on investment and start there.
- B. Find a project that requires 100% accuracy in the results and start with that one.
- C. Go through all possible scenarios to come up with the perfect first project.
- D. Ask key stakeholders from all groups for input about their pain points.
Answer: A
Explanation:
In Phase I: Business Understanding, CPMAI directs teams to "determine business objectives" by engaging stakeholders to surface specific pain points, estimate time-to-ROI, and prioritize projects that deliver tangible business value quickly. Focusing on a narrowly scoped problem with high ROI ensures early success, builds momentum, and validates the AI methodology before tackling larger or more complex initiatives.
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NEW QUESTION # 44
You are leading a project to develop a new predictive maintenance solution. Together with your project team you determine your data needs, see if you have access to the data, and then begin working on the project.
Which phase best describes the work you are performing?
- A. Phase IV
- B. Phase V
- C. Phase I
- D. Phase III
- E. Phase II
- F. Phase VI
Answer: E
Explanation:
Phase II: Data Understanding is dedicated to identifying data requirements, collecting initial data, assessing data quality, and verifying that necessary datasets are accessible and fit for modeling. Determining what data you need and confirming access are the core activities of this phase .
NEW QUESTION # 45
Your team is running a simulation-based optimization exercise to increase routing efficiency. Learning for this exercise is done through "trial and error." Which type of machine learning approach is being leveraged for this exercise?
- A. Supervised Learning
- B. Unsupervised Learning
- C. Reinforcement Learning is defined in CPMAI as the paradigm where agents learn optimal actions via interactions labeled by reward/punishment signals-essentially a "trial and error" process. Domain III of the CPMAI Exam Content Outline covers "Design reinforcement learning approaches with appropriate agents and environments," confirming that simulation-based, trial-and-error optimization is the hallmark of Reinforcement Learning .
- D. Reinforcement Learning
- E. All would work equally well
Answer: C
NEW QUESTION # 46
You have been receiving customer data for the past six months. However recently you notice that this data has drastically changed due to the upcoming holiday season.
What seems to be taking place?
- A. An incomplete milestone list
- B. Model Drift
- C. Data Drift
- D. Lack of stakeholder support
Answer: C
Explanation:
A sudden shift in the incoming data distribution-such as seasonal changes in customer behavior-is known as data drift. CPMAI defines model drift as "degradation in a model's performance over time as the underlying data distribution changes," implying that the root cause is the data itself shifting. Recognizing data drift is the first step in adapting both data pipelines and models to maintain performance .
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NEW QUESTION # 47
A team has started working on their first AI project and they are running this project like a traditional software development project. About two months into the project the team is hitting some major issues, and you're tasked with coming in to help manage this project. Immediately you realize that AI projects need to be treated like data-centric projects.
What's the next best course of action?
- A. Get the existing team up to speed and make sure existing Agile approaches can support the AI effort
- B. Hire an outside consulting firm to handle the technical aspects while you train the team yourself on data centric best practices
- C. Bring in data centric methodology best practices to get this project back on track
- D. Hire an entirely new team making sure there is at least one data scientist on this new team
Answer: C
Explanation:
Domain II of the CPMAI Exam Content Outline highlights the need to "adapt traditional methodologies for data-centric projects" and "implement continuous AI project lifecycles" rather than treating AI as conventional software development. Bringing in CPMAI's data-centric best practices-phased, iterative, and focused on data understanding/preparation-directly addresses the root causes of AI project failures and realigns the team to proven AI project management frameworks.
NEW QUESTION # 48
Your team has collected petabytes of data for your AI project. As the project lead, you understand this is too much data to use for this iteration of the project.
What is the best course of action to take with this data?
- A. Careful algorithm selection that reduces the need for data.
- B. Data integration focused on reducing the number of data sources.
- C. Data Deduping to reduce overall size and data complexity.
- D. Data selection and attribute pruning to reduce overall size and data complexity.
Answer: D
Explanation:
In Phase III: Data Preparation, the Select Data task instructs teams to choose only the records and attributes needed for modeling-documenting inclusions and exclusions to reduce volume and complexity. This selective pruning of columns and rows is the primary mechanism for trimming excessive data before modeling.
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NEW QUESTION # 49
Your team is planning an AI-enabled chatbot project to help reduce call center load. They are currently determining if the project can get off the ground and working through the AI Go/No Go feasibility questions.
What stage of CPMAI is the team currently working on?
- A. Phase IV
- B. Phase II
- C. Phase V
- D. Phase III
- E. Phase I
- F. Phase VI
Answer: E
Explanation:
The AI Go/No Go assessment is part of Phase I: Business Understanding under the Cognitive Project Requirements generic task group. In Phase I, teams perform business-feasibility, data-feasibility, and execution-feasibility checks before proceeding with any AI work .
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NEW QUESTION # 50
Your team is trying to determine which pattern best fits their AI problem. To do this the project team is running through the seven patterns of AI to figure out what pattern best applies to their problem.
Which of the following is the best approach?
- A. Apply every pattern to the project.
- B. When in doubt, go with the Patterns & Anomalies pattern as all AI projects are about pattern matching.
- C. Determine what you're trying to accomplish and see which pattern(s) of AI fit best.
- D. When in doubt, don't apply any pattern of AI.
Answer: C
Explanation:
CPMAI's Task: AI Pattern Identification requires teams to map their specific business objectives to the most appropriate one or more of the Seven Patterns of AI. Starting from "what are we trying to accomplish?" and then selecting the pattern(s) that align with those goals is the prescribed approach.
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NEW QUESTION # 51
You want to create a model to figure out if a customer would be likely to repurchase a certain item. The project owner doesn't want you to create anything too complicated, and you have a limited data set to work with.
- A. Neural Networks
- B. Naive Bayes
- C. Ensemble models
- D. Generative AI
Answer: B
Explanation:
The CPMAI Glossary defines a naive Bayes classifier as "a family of simple probabilistic classifiers based on Bayes' theorem with the assumption of feature independence," making it ideal for small or limited datasets where model simplicity and interpretability are priorities.
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NEW QUESTION # 52
Your team is starting a new facial recognition project and you want to ensure that the project is being done with Trustworthy AI in mind. At what phase of CPMAI would Trustworthy AI be considered?
- A. Phase IV
- B. All phases
- C. Phase II
- D. Phase V
- E. Phase I
- F. Phase III
- G. None of the phases
- H. Phase VI
Answer: B
Explanation:
Trustworthy AI is not confined to a single phase but is woven throughout the entire CPMAI lifecycle:
The CPMAI Exam Content Outline under Domain VI: Trustworthy AI specifies tasks such as "Apply ethical AI concepts throughout the development lifecycle," "Ensure compliance with privacy/security requirements," and "Implement transparency and explainability" at every stage .
The CPMAI Workbook's Task Group: Trustworthy AI Requirements (covering transparency, explainability, ethics, compliance, and responsible-AI frameworks) appears as an overarching set of artifacts and considerations that map back to multiple phases-beginning with Business Understanding and continuing through Model Operationalization .
Thus, Trustworthy AI considerations apply across all CPMAI phases.
NEW QUESTION # 53
The growth of Big Data has led to a desire to be able to do more to process and extract more value from Big Data. Simply storing data and providing analytics is no longer enough anymore to remain competitive.
To keep your organization competitive, you need to:
- A. Make sure the technical team has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
- B. Make sure all senior leadership is data literate, understands the V's of big data, data's connections to your specific team, and how to extract value from big data to unleash it for competitive advantage.
- C. Make sure senior management has deep understanding of big data and how best to extract value from big data to unleash it for competitive advantage.
- D. Make sure everyone on the team has an understanding of data, its connections to the organization, and how to extract value from big data to unleash it for competitive advantage.
Answer: B
Explanation:
CPMAI's Domain IV: Data for AI - Task 1: Managing Data Fundamentals and Big Data Concepts emphasizes that leaders-not just technical practitioners-must grasp the core characteristics of Big Data (the V's: volume, velocity, variety, veracity) and its strategic role in delivering business advantage. Ensuring senior leadership is data literate and understands how to leverage Big Data concepts across teams is critical for sustaining a competitive edge; merely upskilling the technical team or distributing data literacy unevenly will leave strategic gaps.
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NEW QUESTION # 54
The team is working to build a data preparation pipeline for the conversational chatbot project. Which phase of CPMAI is this done?
- A. Phase IV
- B. Phase III
- C. Phase II
- D. Phase V
- E. Phase I
- F. Phase VI
Answer: B
Explanation:
Phase III: Data Preparation focuses on constructing and documenting reusable data pipelines-including training and inference pipelines-alongside cleansing, augmentation, and labeling tasks to prepare data for modeling . This is where teams build the end-to-end data preparation workflows for AI solutions such as chatbots.
NEW QUESTION # 55
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