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[2025] CPMAI_v7 PDF Questions - Perfect Prospect To Go With ActualTorrent Practice Exam
PMI CPMAI_v7 Exam Syllabus Topics:
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NEW QUESTION # 17
The team is evaluating where the sources of the data for training are. What phase of CPMAI are they in?
- A. Phase VI
- B. Phase I
- C. Phase V
- D. Phase III
- E. Phase II
- F. Phase IV
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 # 18
During which phase of an AI project should you consider Trustworthy AI considerations?
- A. Phase VI: Model Operationalization
- B. Phase I: Business Understanding
- C. Phase II: Data Understanding
- D. Every Phase of the AI project
Answer: D
Explanation:
CPMAI's Domain VI: Trustworthy AI is designed to span the entire project lifecycle, embedding ethics, transparency, fairness, privacy, and security checks into every phase-from Business Understanding through Operationalization-so that trustworthy practices are not an afterthought but a continuous, integrated activity .
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NEW QUESTION # 19
A team is getting ready to begin working on a ML project. They need to build a data preparation pipeline and someone on the team suggests they reuse the same pipeline they created for their last project.
What's wrong with this suggestion?
- A. Pipelines are model operationalization need specific.
- B. There is no issue. Pipelines can be reused as needed between projects.
- C. Pipelines are pattern needs specific so as long as it's the same pattern then you can reuse the pipeline.
- D. Pipelines are pattern and model need specific.
Answer: D
Explanation:
In Phase III: Data Preparation, CPMAI specifies that data pipelines must be designed to address the specific modeling pattern and model requirements of the current project. Even if two projects use similar ingestion or cleaning steps, the pipeline must be tailored for the exact feature transformations, label mappings, and data schemas of the new model. Therefore, pipelines are pattern- and model-specific, and blindly reusing one from a prior project without adaptation will likely break downstream model training or inference requirements.
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NEW QUESTION # 20
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. Generative AI
- B. Pre-Trained Models
- C. Reinforcement Learning
- D. Transfer Learning
Answer: D
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 # 21
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing.
What type of problem is this?
- A. You are overfitting the data
- B. You have selected the wrong data
- C. You have selected the wrong algorithm
- D. You are underfitting the data
Answer: A
Explanation:
Overfitting occurs when a model learns not only the underlying patterns but also the noise in the training data, causing it to perform well on seen data but poorly on unseen data. The CPMAI Glossary defines overfitting as "a modeling error where a model learns the training data too well, including its noise, resulting in poor performance on new data."
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NEW QUESTION # 22
You're running an image recognition project and realize that you do not have enough data of a certain type of vehicle. What is the best course of action to get the additional labeled data you need?
- A. Purchase the data from a third party
- B. Perform Data Anonymization
- C. Perform Data Sampling
- D. Perform Data Transformation & Multiplication
Answer: D
Explanation:
In CPMAI v7's Phase III: Data Preparation, teams are instructed to construct the final modeling dataset through a variety of enhancement activities-including data augmentation, which specifically covers transforming existing records or generating entirely new records to increase volume and variety. This
"augmentation" is described as "constructive data preparation operations such as the production of derived attributes or entire new records, or transformed values for existing attributes" .
Moreover, under the Training & Test Data Requirements task, the Workbook explicitly asks project teams to determine "What transformation or multiplication activities can be done to increase training data volume while maintaining quality" . Performing data transformation (e.g., image rotations, color jitter, cropping) and multiplication (synthetic record generation) directly addresses the lack of labeled samples without incurring the cost or delay of third-party purchases, making option B the correct approach.
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NEW QUESTION # 23
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 determine the pattern of AI you want to use and then work with stakeholders to come up with ROI
- B. First talk to senior managers who set the ROI of the project
- 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 identify the AI pattern you want to use and then figure out the 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 # 24
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. Use an AutoML solution
- B. Outsource the project ASAP
- C. Find a citizen data scientist to help
- D. Put the project on hold until the resources needed become available
Answer: A
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 # 25
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 # 26
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. AI Systemic Transparency
- B. Responsible AI
- C. Transparent AI
- D. Explainable AI
- E. Governed AI
Answer: C
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 # 27
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. Try to improve the quality of your data through more preparation
- B. Try to get additional data - at least 5 training examples for each dimension in the representation
- C. Try to get additional information from project lead to see how many examples per dimension are needed
- D. Keep going as planned and the problem will eventually correct itself
Answer: B
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 # 28
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. Automation skills, especially around creating RPA bots
- B. Critical thinking skills
- C. Strong math skills, especially in calculus and statistics
- D. Understanding of algorithms
- E. Prompt engineering skills
- F. Understanding of tools and technologies for manipulating, collecting, and preparing large data sets
Answer: B,C,D,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 # 29
Recently your company has been getting a large number of spam emails and some employees have been clicking on these suspicious emails causing a headache for IT. The head of IT wants to create a more robust spam filter and your team has been tasked with this project.
What type of algorithm would you select for this problem?
- A. Clustering
- B. Regression
- C. Binary (or Binomial) Classification
- D. Multiclass Classification
Answer: C
Explanation:
A spam filter must decide between exactly two categories-spam or not spam-making it a binary (or binomial) classification task. The CPMAI Glossary defines binary classification as "a classification task where data is categorized into one of two classes (e.g., spam vs. not spam)."
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NEW QUESTION # 30
Your team is working on an AI-enabled chatbot to be placed on the website. The goal of the chatbot is to be able to answer questions 24/7 to service clients around the globe. When evaluating your data you realize you don't have enough data to train the model.
What's the best course of action?
- A. Use the data that you have and keep the project moving
- B. Ask your customer service team to generate additional data for you to use for the project
- C. Research what Third-Party Models are available and purchase them to keep the project moving
- D. Do not move forward with the project
Answer: C
Explanation:
In Phase II: Data Understanding, the Pre-Trained and Third-Party Model Usage task directs teams to
"determine if team will use a model...developed by another team, or by a third-party organization" and to gather information about "costs to use model, origin of data and methods of training, performance measures, and other factors that will determine appropriateness of model" before proceeding . When in-house data is insufficient, leveraging existing third-party models is the recommended path to avoid project stall.
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NEW QUESTION # 31
Enhancing and cleaning data is an important action during which phase of CPMAI?
- A. Phase VI
- B. Phase I
- C. Phase V
- D. Phase II
- E. Phase III
- F. Phase IV
Answer: E
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 # 32
A project manager meets with a customer for initial discussions about an upcoming project. At the end of the meeting, the customer asks the project manager for a rough estimate of the project duration. Based on her experience with three similar projects, the project manager provides an estimate of 8-10 months.
What's wrong with this timeframe?
- A. It's not accounting for potential project delays
- B. It's underestimating the project timeline by 3 months
- C. It fits into a waterfall timeframe, but not an agile project timeframe
- D. It's not accounting for data preparation timelines
Answer: D
Explanation:
CPMAI's Phase III: Data Preparation is a distinct phase that encompasses data cleansing, augmentation, labeling, and pipeline construction. Because data engineering often accounts for the majority of AI project effort, omitting this phase from initial estimates leads to significant timeline underestimation. Project timelines must explicitly include Phase III activities to be realistic .
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NEW QUESTION # 33
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 VI
- B. Phase I
- C. Phase V
- D. Phase III
- E. Phase IV
- F. Phase II
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 # 34
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 VI
- B. Phase I
- C. Phase V
- D. Phase III
- E. None of the phases
- F. Phase II
- 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 # 35
As the project manager, you are leading a brainstorming session with key stakeholders around a new Hyperpersonalization project. What's a key feature for this project that should happen to ensure success?
- A. Develop a unique profile of each individual, and have that profile learn and adapt over time for a wide variety of purposes
- B. Develop a unique profile of each individual, and have that profile both learn and adapt over time as well as be programmed for a wide variety of purposes
- C. Develop a unique profile of each individual, and manually update that profile over time for a wide variety of purposes
- D. Develop a unique profile of each type of individual, and have that profile stay the same over the lifetime of that user
Answer: A
Explanation:
The Hyperpersonalization pattern is defined as tailoring experiences based on individual user characteristics or behavior-requiring each profile to learn and adapt continuously as more data arrives. Manually updating or pre-programming profiles undermines this dynamic learning capability.
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NEW QUESTION # 36
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