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NEW QUESTION # 46
Your organization wants to use Generative AI. What are examples of when Generative AI can and should be used? (Select all that apply.)
- A. Virtual Avatars and Characters
- B. Programmatic automated content generation
- C. Data Augmentation for Training
- D. Human Augmentation
- E. Content Generation
- F. Explainable Decision-support systems
Answer: A,B,C,D,E
Explanation:
The CPMAI Glossary's entry for Generative AI highlights its use in creating new content (text, images, or code), enhancing training datasets via data augmentation, powering virtual avatars/characters, and serving as an Augmented Intelligence tool to boost human productivity . It also underpins programmatic content generation across multiple media types. Generative AI is not designed primarily for explainable decision- support interfaces.
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NEW QUESTION # 47
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 until you can determine a clear problem definition.
- B. Do not move forward and cancel the project altogether.
- C. 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. - D. Move forward with the project as planned. The problem definition will become clear later on in the project.
Answer: A
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 # 48
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. Everyone on the team should understand the differences between automation and autonomous systems.
- B. Applying a hybrid approach of automation and AI best practices would have achieved better results.
- C. Nothing - running automation projects like autonomous projects is the correct thing to do.
- D. Senior management should become involved in the project.
Answer: A
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 # 49
You're working with a small inexperienced team on a new ML project. Choosing the best algorithm with the best settings given the training and test data is proving to be very hard for them. You lack the critical data science resources available on your team, and can't wait weeks until a data science resource becomes available to join your team.
What's your best course of action?
- A. Find a citizen data scientist to help
- B. Put the project on hold until the resources needed become available
- C. Use an AutoML solution
- D. Outsource the project ASAP
Answer: C
Explanation:
In Phase IV's Usage of AutoML task, CPMAI expressly recommends leveraging automated machine-learning tools to accelerate model creation when specialized expertise or time is limited. Documenting how AutoML will generate, evaluate, and export models allows teams to maintain pace without sacrificing rigor.
NEW QUESTION # 50
Your team is ready to operationalize the model they have been working on. It's a model that is meant to be used on an "edge device," specifically a mobile phone, and the user may sometimes be in remote locations without regular access to the internet.
What's the most important thing to consider here?
- A. Make sure the model lives in a hybrid environment
- B. Make sure the model lives on the edge device so it can be used regardless of internet connection
- C. Make sure that you can use Generative AI solutions on an edge device
- D. Make sure the model is available over a cloud-based API
Answer: B
Explanation:
In the Model Operationalization phase (Phase VI), when targeting edge devices, teams must capture the specific constraints around connectivity and on-device performance. The CPMAI Workbook's Edge Model Data Needs task group instructs project teams to list all requirements and constraints for running models on edge systems-including constraints on external data access-and to determine how models will be deployed locally so they function offline when connectivity is unavailable.
Under "Edge constraints," the Workbook requires teams to "List constraints on model usage including ...
requirements for model result response time, constraints on external data access," underscoring that reliance on a remote API is infeasible without connectivity.
Consequently, the critical operationalization decision is to deploy the model directly on the edge device, ensuring it remains fully functional regardless of internet availability.
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NEW QUESTION # 51
Your team is working on a project and is running into some issues. You need someone on the team who is able to solve problems in environments of uncertainty, can deal with failure, and has the math and data visualization skills needed to communicate the results with others so the issues can get resolved.
- A. Project Manager
- B. Citizen Data Scientist
- C. Data Engineer
- D. Data Scientist
Answer: D
Explanation:
CPMAI defines a Data Scientist as the role responsible for "formulating data-driven hypotheses, selecting and applying statistical algorithms, interpreting model results, and communicating insights to stakeholders," all of which require critical thinking under uncertainty, advanced mathematics, and strong data-visualization skills .
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NEW QUESTION # 52
Your team is working on an NLP model and has just operationalized the first model. Your team makes updates to the model, overwrites the original model, and puts this new model into operation. However, one of the teams using the model has seen a decrease in performance and is asking to use the original model.
What critical error did your team make?
- A. They did not have data governance in place
- B. They did not practice model iteration and properly iterate on the model
- C. They did not practice model versioning and keep all versions of the model
- D. They did not have a model retraining pipeline that took into account models
Answer: C
Explanation:
In Phase VI: Model Operationalization of the CPMAI v7 methodology, project teams must explicitly plan for "model versioning and iteration" as part of deploying and maintaining models in production. Overwriting the original model without preserving its prior version prevents rollback and comparison, which is a core requirement for robust AI operations.
The Workbook states that operationalization considerations include "model versioning and iteration" to ensure that previous model artifacts are retained and that updates can be managed safely.
Additionally, under Edge Model Data Needs, teams are instructed to "Determine methods for model versioning and update" to support proper tracking and governance of model changes across iterations.
NEW QUESTION # 53
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. Neural Network
- D. Hyperpersonalization
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 # 54
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. None of the phases
- C. Phase IV
- D. Phase VI
- E. Phase II
- F. Phase I
- G. Phase V
- H. All phases
Answer: H
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 # 55
You're running an AI project and want to speed up training of the model so that you can complete your current CPMAI iteration within the two week timeframe the team has set. What's one approach to speed up model training?
- A. Use brute force method
- B. Use cloud-based technology
- C. Use or extend a pre-trained model
- D. Use data from a previous project
Answer: C
Explanation:
The Transfer Learning task in Phase IV: Model Development recommends leveraging a pre-trained model as the starting point for a new, related task. By fine-tuning rather than training from scratch, teams dramatically reduce compute time and data requirements-ideal for tight iteration cycles.
NEW QUESTION # 56
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 III
- B. Phase IV
- C. Phase VI
- D. Phase II
- E. Phase V
- F. Phase I
Answer: F
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 # 57
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 identify the objective you're trying to solve or the ROI you desire and then use that to figure out the correct pattern
- B. First determine the pattern of AI you want to use and then work with stakeholders to come up with ROI
- C. First talk to senior managers who set the ROI of the project
- D. First identify the AI pattern you want to use and then figure out the ROI
Answer: A
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 # 58
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. Collect enormous amounts of data - the more data the better.
- B. Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.
- 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 # 59
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 "Goldilocks" approach
- B. The "less is better" approach
- C. The "more is better" approach
- D. There is no correct approach
Answer: A
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 # 60
Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time.
However, what's the one area you should not have the AutoML tool help with?
- A. Iterative modeling and evaluation
- B. Automatic hyperparameter tuning
- C. Automatic model assessment
- D. Automatic model selection
- E. Automatic algorithm selection
Answer: A
Explanation:
CPMAI's Usage of AutoML task instructs teams to "Document how AutoML tools will be used for model creation" and to verify that the output can be integrated into the overall I/O flow . While AutoML excels at automating algorithm selection, model selection, hyperparameter tuning, and even preliminary performance metrics, CPMAI places iterative modeling and evaluation squarely under the manual Model Evaluation phase-where teams must interpret results against business success criteria and decide on next steps.
Entrusting that high-level, iterative decision-making to an AutoML black box would undermine the human- centric evaluation that CPMAI mandates.
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NEW QUESTION # 61
Leadership wants a new HR system built that will better handle potential candidate matching. The project manager assigned to this project believes that the project is well-suited for AI, however they are unsure which pattern of AI this would be.
What should the project manager do?
- A. Move forward without determining which pattern of AI this falls under.
- B. Determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
- C. Pick an algorithm that seems best suited for the problem and then determine which pattern of AI it is based on the algorithm selected.
- D. Conduct a straw poll with stakeholders to determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
Answer: B
Explanation:
In Phase I: Business Understanding, after performing the Go/No Go assessment, the CPMAI methodology requires teams to perform AI Pattern identification-mapping business objectives to one or more of the Seven Patterns of AI-so that the right data requirements, algorithms, and team skills can be scoped effectively. This early pattern identification helps accelerate design by leveraging best practices for that pattern .
NEW QUESTION # 62
Enhancing and cleaning data is an important action during which phase of CPMAI?
- A. Phase III
- B. Phase IV
- C. Phase VI
- D. Phase II
- E. Phase I
- F. Phase V
Answer: A
Explanation:
The CPMAI v7 methodology groups all data-centric preparation activities-including both data cleansing ("Clean data") and data augmentation ("Enhance & Augment data")-into Phase III: Data Preparation. In this phase, teams focus squarely on constructing the dataset to be used for modeling by performing all required cleaning, transformation, and enhancement operations.
Phase III: Data Preparation is defined in the Workbook's Table of Contents as covering Data Cleansing & Enhancement tasks ("Clean data" and "Enhance & Augment data") .
Under Phase III, the Generic Task Group: Data Cleansing & Enhancement explicitly lists "Task: Clean data" (bringing data quality to modeling-ready levels) and "Task: Enhance & Augment data" (producing derived attributes and new records) as core activities .
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NEW QUESTION # 63
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PMI CPMAI_v7 Exam Syllabus Topics:
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