PDF (New 2025) Actual PMI CPMAI_v7 Exam Questions
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PMI CPMAI_v7 Exam Syllabus Topics:
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NEW QUESTION # 33
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. Critical thinking skills
- B. Strong math skills, especially in calculus and statistics
- C. Understanding of tools and technologies for manipulating, collecting, and preparing large data sets
- D. Automation skills, especially around creating RPA bots
- E. Prompt engineering skills
- F. Understanding of algorithms
Answer: A,B,C,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 # 34
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 V
- D. Phase I
- E. Phase IV
- F. Phase VI
Answer: C
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 # 35
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. Bring in data centric methodology best practices to get this project back on track
- B. Hire an entirely new team making sure there is at least one data scientist on this new team
- C. Hire an outside consulting firm to handle the technical aspects while you train the team yourself on data centric best practices
- D. Get the existing team up to speed and make sure existing Agile approaches can support the AI effort
Answer: A
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 # 36
Enhancing and cleaning data is an important action during which phase of CPMAI?
- A. Phase II
- B. Phase I
- C. Phase V
- D. Phase III
- E. Phase IV
- F. Phase VI
Answer: D
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 # 37
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. Data Drift
- B. An incomplete milestone list
- C. Model Drift
- D. Lack of stakeholder support
Answer: A
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 # 38
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 a statistics pattern. If it's statistical 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 probabilistic or deterministic patterns. If it's probabilistic then go with the AI project.
Answer: D
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 # 39
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. Process automation
- B. Code generation
- C. Text summarization
- D. Machine Translation
- E. Improve search quality
- F. Classify and categorize content
Answer: B,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 # 40
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 Augmented Intelligence system
- C. As an autonomous system removing the human from the loop
- D. As a programmatic approach for automation
Answer: B
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 # 41
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 III
- B. Phase II
- C. Phase I
- D. Phase V
- E. Phase IV
- F. Phase VI
Answer: E
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 # 42
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 I
- C. Phase V
- D. Phase III
- E. Phase IV
- F. Phase VI
Answer: D
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 # 43
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. Senior management should become involved in the project.
- D. Nothing - running automation projects like autonomous projects is the correct thing to do.
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 # 44
You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?
- A. Feature Engineering
- B. ELT Pipeline
- C. Data Acquisition / Ingest / Capture
- D. Retraining Pipelines
Answer: D
Explanation:
Model iteration requires regularly updating a deployed model with new data and configuration. The CPMAI Workbook's Task: Fine-Tuning / Re-training of Pre-Trained Models prescribes defining and documenting re- training pipelines as part of the model-building lifecycle to ensure seamless iteration and ongoing performance improvements.
NEW QUESTION # 45
Your organization has just rolled out a new image recognition system and is asking all employees to use it. It was trained using images from the ImageNet test set. After a few weeks, users are finding the results are not as expected and are asking for visibility into all the aspects of what went into building an AI system. What area of Trustworthy AI is being addressed here?
- A. Responsible AI
- B. Transparent AI
- C. Explainable AI
- D. Governed AI
- E. AI Systemic Transparency
Answer: B
Explanation:
In CPMAI's Trustworthy AI framework, Transparent AI focuses on providing clear documentation of data sources, modeling approaches, evaluation methods, and deployment plans so that stakeholders can audit and understand how the system was built. The users' request for "visibility into all aspects" of model development, training data, and test sets directly maps to the Required AI Transparency Considerations task early in the methodology .
NEW QUESTION # 46
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. Hyperpersonalization
- B. Neural Network
- C. K Nearest Neighbor
- D. K-means
Answer: C
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 # 47
Your team has been asked to summarize and highlight patterns in historical purchasing data, identifying prior performance metrics and patterns. What type of analytics is most appropriate to apply for this need?
- A. Predictive Analytics
- B. Diagnostic Analytics
- C. Projective Analytics
- D. Descriptive Analytics
Answer: D
Explanation:
Descriptive analytics focuses on summarizing and reporting on past data-exactly what's needed to highlight historical purchasing patterns and performance metrics. The CPMAI Glossary defines analytics as "the use of statistical and computational methods to extract meaningful insights from data (related: predictive, descriptive, ...)" where descriptive analytics provides the retrospective view.
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NEW QUESTION # 48
During CPMAI Phase IV: Model Development, which of the following is not done during this phase?
- A. Model tuning
- B. Algorithm Selection
- C. Model training
- D. Model Selection
Answer: D
Explanation:
The Phase IV: Model Development generic tasks include:
Select Modeling Technique (algorithm selection)
Generate model test design
Model Training / Model Building
Hyperparameter Optimization (model tuning)
Final Model Selection (choosing the best candidate against business criteria) is performed in Phase V: Model Evaluation, not in Phase IV .
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NEW QUESTION # 49
Your team has created a model that is going to be used for monitoring systems and it needs to provide analysis on a weekly basis. What's the most appropriate Model Operationalization approach?
- A. Web service / Microservice
- B. Batch prediction
- C. Real-time prediction
- D. Stream learning
Answer: B
Explanation:
In Phase VI: Model Operationalization, CPMAI specifies that batch prediction-"generating predictions offline for large datasets on a scheduled basis"-is the ideal deployment mode when analysis is needed at regular, non-continuous intervals (e.g., weekly) . Continuous methods like real-time or streaming are overkill for periodic reporting.
NEW QUESTION # 50
You're in charge of marketing at your organization and you've been tasked with using AI to help create marketing images. What's a good solution for this need?
- A. Decision tree and Random Forest approaches
- B. Generative AI solutions for content generation
- C. Image and object detection and recognition systems
- D. Autonomous patterns and process automation
Answer: B
Explanation:
Generative AI is defined in the CPMAI Glossary as "AI systems that create new data (e.g., text, images, music) based on patterns learned from existing data." Using Generative AI for content generation directly addresses the need to produce marketing images automatically.
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NEW QUESTION # 51
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 III
- B. Phase I
- C. Phase II
- D. Phase V
- E. Phase IV
- F. Phase VI
Answer: C
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 # 52
You're being told by upper management that you need to manage a new AI project. You need to determine the AI project fit to make sure you're actually solving a real business problem.
During Phase I: Business Understanding, you should consider at least one of the following (Select all that apply):
- A. Explores a proof of concept for an AI project
- B. Enhance revenue
- C. Has the "cool" factor
- D. Solves an already solved problem but does it better and cheaper
- E. Improve company competitiveness in the market
- F. Solves a previously unsolved problem
Answer: B,D,E,F
Explanation:
Phase I begins with Determine Business Objectives to ensure the AI initiative aligns to real business needs.
Teams evaluate whether the solution will enhance revenue, solve a previously unsolved problem, improve competitive positioning, or "provide enough ROI/impact...and be better/more impactful than the current, non- cognitive, heuristic approach" (i.e., do something already addressed but do it better and cheaper). Proof-of- concepts (A) and "cool" factors (E) are not valid business objectives under CPMAI's Business Understanding tasks.
NEW QUESTION # 53
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