Best PMI CPMAI_v7 Exam Practice Material Updated on May 11, 2026
New CPMAI_v7 Actual Exam Dumps, PMI Practice Test
PMI CPMAI_v7 Exam Syllabus Topics:
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NEW QUESTION # 18
You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?
- A. The volume of the data you collect
- B. The quality of the data you collect
- C. The location/source of your data collection
- D. The makeup of your data team
Answer: D
Explanation:
In Phase II: Data Understanding, CPMAI's Generic Task Groups focus on:
Collecting initial data (identifying sources and volumes) and describing data (location/source) .
Verifying data quality to ensure completeness and correctness .
Team composition (the makeup of your data team) is addressed earlier under Phase I: Assess Situation, not during the Data Understanding phase where data requirements (quality, volume, source) are determined.
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NEW QUESTION # 19
Your company is insisting on running an automation project and applying AI best practices and methodologies to the project. You understand that automating things is just the act of using machines to repeat tasks, and does not require AI to achieve results. You think it is overkill but the project moves forward as planned.
What would likely have helped avoid this conflict?
- A. Applying a hybrid approach of automation and AI best practices would have achieved better results.
- B. Everyone on the team should understand the differences between automation and autonomous systems.
- C. Nothing - running automation projects like autonomous projects is the correct thing to do.
- D. Senior management should become involved in the project.
Answer: B
Explanation:
During Phase I's Cognitive Project Requirements tasks, CPMAI instructs teams to "Determine when to implement automation versus AI." Explicitly distinguishing between simple rule-based automation (RPA) and true cognitive solutions prevents misapplication of AI methodology to non-AI use cases. Ensuring everyone understands this distinction up front would have avoided misalignment on methodology.
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NEW QUESTION # 20
You have been brought on to manage a recognition project, specifically an image recognition project, for an Autonomous Retail application. You know that you need to make sure you have sufficient data for this project. What's the best way to approach this?
- A. Take all the data your company has as well as purchase additional external data
- B. Take inventory of all data your company has and use the relevant data
- C. Take all the existing data you have and apply it to this project
- D. Take inventory of all data your team has and use the relevant data
Answer: B
Explanation:
In Phase II: Data Understanding, CPMAI's Data Selection tasks require teams to "Decide on the data to be used for analysis" by first listing all available sources and then selecting only those records and attributes that meet quality and relevance criteria . Taking a company-wide data inventory ensures you don't overlook relevant datasets before narrowing down to what truly applies.
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NEW QUESTION # 21
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. Generative AI
- B. Naive Bayes
- C. Neural Networks
- D. Ensemble models
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 # 22
You are working with a dataset that has a high number of dimensions. You're running into issues because some dimensions don't have enough real examples to properly train the systems for predictable results. What' s your best course of action?
- A. Keep going as planned and the problem will eventually correct itself
- B. Try to improve the quality of your data through more preparation
- C. Try to get additional information from project lead to see how many examples per dimension are needed
- D. Try to get additional data - at least 5 training examples for each dimension in the representation
Answer: D
Explanation:
CPMAI's Phase II: Data Understanding includes verifying that you have sufficient data volume for each feature to support reliable model training. The learning curve concept underscores that model performance improves with additional training examples. When dimensions are under-represented, the team must source or generate more data-aiming for a minimum number of examples per feature-to avoid underfitting and ensure stable predictions.
NEW QUESTION # 23
In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:
- A. Provide a means to have contestability of the algorithm selected
- B. Provide a means to reverse-engineer the algorithm to inspect its performance
- C. Provide a means to have a different team on the project
- D. Provide a means to interpret AI results so that cause and effect can be represented.
Answer: D
Explanation:
Under Required AI Explainability Considerations, CPMAI mandates that when a chosen model is a "black- box" with limited native interpretability, teams must implement post-hoc interpretability techniques (e.g., feature#importance plots, surrogate models) to "interpret AI results so that cause and effect can be represented," ensuring stakeholders understand why the model makes its predictions.
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NEW QUESTION # 24
Major factors for the project you are currently working on are around the training time, cost, and complexity of training your models. Which algorithm is not the best choice given these constraints?
- A. Naive Bayes
- B. Gaussian Mixture
- C. Support Vector Machines (SVM)
- D. Neural Networks
Answer: D
Explanation:
Neural Networks-especially deep architectures-typically require extensive computational resources, longer training times, and higher infrastructure costs compared to simpler methods. In contrast, algorithms like Naive Bayes train very quickly on large datasets, and Gaussian Mixture Models or SVMs have more moderate training complexity and infrastructure demands. Therefore, given strict constraints on training time, cost, and complexity, Neural Networks are the least suitable choice.
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NEW QUESTION # 25
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)
- A. Supporting training data
- B. Hyperparameter settings
- C. Bias-variance tradeoff
- D. The algorithm code
Answer: A,B,D
Explanation:
Algorithm selection/code must be documented under the Select Modeling Technique task, where teams
"document the actual algorithm/modeling technique to be used" .
Supporting training data pipelines are a core artifact of Phase III: Data Cleansing, which mandates "create a reusable data pipeline to collect, ingest, and prepare data for training purposes" .
Hyperparameter settings are captured in the Hyperparameter Optimization task, where teams "list the final, optimized settings" used for model building .
The bias-variance tradeoff is a conceptual consideration during evaluation but is not a discrete artifact to include in the project deliverables.
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NEW QUESTION # 26
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. Responsible AI
- B. Explainable AI
- C. Transparent AI
- D. Governed AI
Answer: A
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 # 27
During CPMAI Phase II, it's important to not only understand the sources of your data but also what data is required for training as well as identifying the features that are required.
When looking to gather data, what approach is best when determining how much data you need?
- A. The "less is better" approach
- B. The "more is better" approach
- C. The "Goldilocks" approach
- D. There is no correct approach
Answer: C
Explanation:
Phase II: Data Understanding centers on identifying just the right amount of data for model training-neither too little (risking underfitting) nor too much (wasting resources and introducing noise). This balanced-
"Goldilocks"-approach ensures you collect sufficient high-quality, relevant records to meet cognitive objectives without incurring unnecessary cost or complexity.
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NEW QUESTION # 28
Use cognitive technologies/AI when you can't code the rules or you can't scale easily with people or automation. As a good rule of thumb when deciding if AI is right for the project you should:
- A. Decide if it's probabilistic or deterministic patterns. If it's probabilistic then go with the AI project.
- B. Decide if it's probabilistic or deterministic patterns. If it's deterministic then go with the AI project.
- C. See if simple rules work. If yes, then pick the right AI solution to solve the problem.
- D. Decide if it's a statistics pattern. If it's statistical then go with the AI project.
Answer: A
Explanation:
The CPMAI Glossary contrasts automation (for deterministic, rule-based tasks) with AI (for probabilistic, learning-based tasks). As a rule of thumb, if a problem exhibits probabilistic patterns that can't be captured by fixed rules, then AI is the appropriate solution; deterministic problems are better handled by simple automation.
NEW QUESTION # 29
You have been tasked with creating a model that will recommend products based on what other customers have similarly purchased. Which algorithm is the best choice given this situation?
- A. K-means
- B. K Nearest Neighbor
- C. Hyperpersonalization
- D. Neural Network
Answer: B
Explanation:
CPMAI's Generic Task Group: Select Modeling Technique in Phase IV: Model Development outlines common cognitive algorithms. For recommendation systems-which rely on finding similar user or item profiles-the K-Nearest Neighbor algorithm is the canonical choice, using customer purchase vectors to locate "nearest neighbors." In contrast, K-means is purely unsupervised clustering, Neural Networks are more complex and not necessary for basic collaborative filtering, and Hyperpersonalization is an AI pattern, not an algorithm.
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NEW QUESTION # 30
A team is retraining a model and creating a new version of that model. What's the most important thing for the team to have in place before doing this?
- A. Model discovery
- B. Model operations
- C. Data operations
- D. Model Governance
Answer: D
Explanation:
In Phase VI: Model Operationalization, CPMAI specifies that a Model Governance Framework must be established before any model versioning or retraining occurs. This governance framework ensures proper version control, audit trails, and clear ownership for each model iteration, maintaining accountability and compliance throughout the model lifecycle.
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NEW QUESTION # 31
Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled.
Which phase of CPMAI is this done?
- A. Phase II
- B. Phase III
- C. Phase I
- D. Phase V
- E. Phase VI
- F. Phase IV
Answer: B
Explanation:
Phase III: Data Preparation includes the Data Labeling generic task group. Specifically, the Label data task covers "identifying methods for data labeling and engaging in data labeling efforts," which is essential for supervised learning workflows like image recognition.
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NEW QUESTION # 32
You need to hire a data scientist to join your team. What skill sets should you be looking for when hiring and interviewing this person? (Select all that apply.)
- A. Strong math skills, especially in calculus and statistics
- B. Automation skills, especially around creating RPA bots
- C. Prompt engineering skills
- D. Understanding of tools and technologies for manipulating, collecting, and preparing large data sets
- E. Critical thinking skills
- F. Understanding of algorithms
Answer: A,D,E,F
Explanation:
In Phase I's AI Skills Assessment, CPMAI directs teams to "List the cognitive skills you have available" and to identify "What expertise and skills you have available to you that you can use for this project" as well as any skills gaps to address . The methodology-and the CPMAI Glossary's definition of a data scientist- emphasizes core competencies in:
Data Engineering & Preparation (manipulating, collecting, transforming large data sets) Critical Thinking (interpreting insights to align with business goals) Algorithmic Understanding (selecting and applying the right statistical or ML models) Mathematical Proficiency (especially statistics and calculus underpinning model creation) By contrast, prompt engineering (A) is a specialized role for LLM interactions, not a general data-science core competency; and RPA-centric automation skills (E) fall outside the CPMAI focus on cognitive/ML capabilities.
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NEW QUESTION # 33
You have been tasked at your organization to manage a large language model (LLM) project. Identify what LLMs are useful for. (Select all that apply.)
- A. Code generation
- B. Process automation
- C. Text summarization
- D. Machine Translation
- E. Improve search quality
- F. Classify and categorize content
Answer: A,C,D,E,F
Explanation:
Large language models (LLMs) excel at generating, understanding, and manipulating text. According to the CPMAI Glossary:
Content summarization is a core NLP function: "the process of using AI/ML techniques to generate a concise overview of a larger body of text." Machine translation: "the use of AI to automatically translate text or speech from one language to another." Classification: LLMs can assign content to categories via fine-tuned classification heads ("classifier" term), making them suitable for content categorization.
Code generation: As generative AI, LLMs can produce new content, including code snippets, by pattern learning from programming corpora ("generative AI" term).
Search quality improvement: LLMs can rephrase queries, expand keywords, and rank results to enhance search relevance. Though not explicitly detailed in the glossary, this capability derives directly from their generative and understanding strengths.
LLMs are not designed for pure process automation (option A), which is handled by RPA or orchestrators rather than by text-centric models.
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NEW QUESTION # 34
You have just joined a team and they are working on a new project. The project lead isn't sure what type of technology should be used on this project-AI or a traditional software development approach. What is the best way to determine if you have the criteria for a good AI/ML Project?
- A. Determine if the project fits within the scope, budget, and timeline set out.
- B. Evaluate whether the solution can be done with automation.
- C. Determine whether the project has a cognitive technology component and meets a short-term need.
- D. Determine the long-term need for the organization and build the project to that long-term goal.
Answer: B
Explanation:
During Phase I: Business Understanding, one of the foundational CPMAI tasks is to "determine when to implement automation versus AI," ensuring that rule-based or non-cognitive alternatives are considered first and AI is only selected when those approaches won't suffice.
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NEW QUESTION # 35
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 III
- B. Phase II
- C. Phase I
- D. None of the phases
- E. Phase V
- F. Phase VI
- G. All phases
- H. Phase IV
Answer: G
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 # 36
You've built your model and now need to see if it actually works as expected. In which phase of CPMAI is this done?
- A. Phase III
- B. Phase II
- C. Phase I
- D. Phase VI
- E. Phase V
- F. Phase IV
Answer: E
Explanation:
Phase V: Model Evaluation is dedicated to validating a trained model's performance against technical metrics and the business success criteria defined earlier. This phase encompasses tasks such as generating performance results, measuring KPIs, and deciding whether to retrain or proceed to operationalization.
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NEW QUESTION # 37
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. Go through all possible scenarios to come up with the perfect first project.
- C. Ask key stakeholders from all groups for input about their pain points.
- D. Find a project that requires 100% accuracy in the results and start with that one.
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 # 38
Your team is looking for a short term ROI project and decides that an AI-enabled chatbot will be the project to start with. During Phase I of CPMAI you go through the AI Go/No Go decision chart and realize that you have not answered yes to all the business feasibility questions. You and the team have not determined a clear problem definition.
What's the best course of action with how to proceed?
- A. Do not move forward and cancel the project altogether.
- B. Cautiously move forward as planned. You do not need to answer yes to all the questions in the AI Go
/No Go decision chart to start your project. - C. Do not move forward until you can determine a clear problem definition.
- D. Move forward with the project as planned. The problem definition will become clear later on in the project.
Answer: C
Explanation:
In Phase I's AI Go/No Go task group, the Business Feasibility step mandates that every business-feasibility question-including a clear problem definition-must be answered "Go" before proceeding. If any critical feasibility criteria remain unanswered or "No Go," the project must pause and resolve those uncertainties rather than advance prematurely.
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NEW QUESTION # 39
Your team is working on an AI system to provide a more personalized experience for customers on your website. What should the team do in regard to determining the pattern of AI with regards to the ROI of the project?
- A. First talk to senior managers who set the ROI of the project
- B. First identify the AI pattern you want to use and then figure out the ROI
- C. First identify the objective you're trying to solve or the ROI you desire and then use that to figure out the correct pattern
- D. First determine the pattern of AI you want to use and then work with stakeholders to come up with ROI
Answer: C
Explanation:
In CPMAI's Executing the Business Understanding Phase, teams first "formulate AI-specific business questions" and "estimate time-to-ROI for various AI project types" before matching business needs to cognitive patterns . This ensures ROI-driven objectives guide the selection of one or more of the Seven Patterns of AI, rather than the reverse.
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NEW QUESTION # 40
For AI projects the code and systems don't matter as much as the data. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?
- A. Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.
- B. Collect enormous amounts of data - the more data the better.
- C. Understanding which algorithms are best for your data needs.
- D. Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.
Answer: D
Explanation:
CPMAI emphasizes that data is only as valuable as the team's ability to manage, prepare, and harness it effectively. In Phase I: Business Understanding, one of the first tasks under Assess Situation is an "AI Skills Assessment," which ensures that the project team has the right mix of experience and tooling expertise to handle data- intensive AI work. Without skilled data engineers and AI practitioners, even the largest datasets cannot be transformed into business value.
The Workbook's Task Group: Assess Situation in Phase I explicitly calls out "AI Skills Assessment" alongside resource and tooling considerations, highlighting that team capability is a foundational requirement for any data-centric initiative.
Furthermore, in Domain IV: Data for AI of the CPMAI Exam Content Outline, managing data fundamentals and Big Data concepts hinges on having personnel who can "apply Big Data approaches to enhance AI capabilities", which presupposes the presence of experienced data professionals.
Thus, the single most critical factor is ensuring you have team members with the right experience and tool expertise to handle and derive value from massive volumes of data.
NEW QUESTION # 41
In what way would you be using Generative AI if you used the results of the Generative AI solution to improve and accelerate your job?
- A. Used for Hyperpersonalization
- B. As an autonomous system removing the human from the loop
- C. As an Augmented Intelligence system
- D. As a programmatic approach for automation
Answer: C
Explanation:
The CPMAI Glossary defines Augmented Intelligence as "enhancing human abilities with AI," where AI outputs are leveraged by humans to improve decision-making or productivity. Using Generative AI to accelerate or improve your own work is precisely an Augmented Intelligence use case, distinct from full autonomy or simple automation .
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NEW QUESTION # 42
The team is working to build a data preparation pipeline for the conversational chatbot project. Which phase of CPMAI is this done?
- A. Phase II
- B. Phase III
- C. Phase I
- D. Phase V
- E. Phase VI
- F. Phase IV
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 # 43
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