CPMAI_v7 100% Pass Guaranteed Download CPMAI Exam PDF Q&A [Q28-Q48]

August 25, 2025 CPMAI_v7, PMI 0 Comments

CPMAI_v7 100% Pass Guaranteed Download CPMAI Exam PDF Q&A [Q28-Q48]

4.6/5 - (5 votes)

CPMAI_v7 100% Pass Guaranteed Download CPMAI Exam PDF Q&A

CPMAI_v7 Practice Test Dumps with 100% Passing Guarantee

PMI CPMAI_v7 Exam Syllabus Topics:

Topic	Details
Topic 1	Data for AI: This domain targets the Data AI Lead and explores the central role of data in AI deployments, including Big Data concepts and unstructured data utility. It defines data governance strategies such as steering, stewardship, lifecycle mapping, lineage tracking, and master data practices.
Topic 2	Domain VI Trustworthy AI: This section is designed for the Project Manager and focuses on ethical, responsible, and transparent AI development. It covers building trustworthy systems, dispelling misconceptions, evaluating real-world ethical concerns, defining responsible frameworks, and implementing mitigation tactics for unintended harms. It addresses data privacy, GDPR compliance, protection of PII, anonymization techniques, security against adversarial threats, and monitoring.
Topic 3	Managing AI: This section is for the Project Manager and involves assessing model performance through quality assurance practices, validation techniques, overfitting and underfitting strategies, alignment with KPIs, and iterative refinements. It additionally covers the deployment of AI from training to inference, operationalization in production environments, on-premise or cloud resource selection, data lifecycle management, version control, and the choice of appropriate machine learning services.
Topic 4	CPMAI Methodology: This domain measures the skills of a Project Manager and outlines the distinctive characteristics of AI projects compared to traditional software development. It investigates failure drivers, ROI justification, data quantity and quality challenges, proof-of-concept issues, real-world deployment barriers, lifecycle continuity, vendor mismatches, stakeholder misalignment, and adaptation of waterfall, lean, and agile approaches through the six phases of the CPMAI framework.

NEW QUESTION 28
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?

Reinforcement Learning

Generative AI

Transfer Learning

Pre-Trained Models

NEW QUESTION 29
In the case that an algorithm you want to use isn’t algorithmically explainable, AI systems should try to do the following:

Provide a means to have contestability of the algorithm selected

Provide a means to interpret AI results so that cause and effect can be represented.

Provide a means to have a different team on the project

Provide a means to reverse-engineer the algorithm to inspect its performance

NEW QUESTION 30
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.

Ensemble models

Naive Bayes

Neural Networks

Generative AI

NEW QUESTION 31
You’re working with petabytes of data and need to make this dataset more manageable. To do this, you want to reduce the number of variables under consideration. What is the name for this process?

Dimensionality Reduction

Gradient Descent

Multivariate regression

Data selection

NEW QUESTION 32
An inexperienced team is training a neural network model on a desktop computer and this is taking a significant amount of time. What would you recommend to them to speed up model training?

Train the model over multiple desktop computers

Train the model on GPUs

Use a contractor to do the training portion

Break the dataset up into multiple smaller datasets and train the model on each of the smaller datasets over a desktop computer

NEW QUESTION 33
You’ve built your model and now need to see if it actually works as expected. In which phase of CPMAI is this done?

Phase I

Phase II

Phase III

Phase IV

Phase V

Phase VI

NEW QUESTION 34
Your team has built a new robot that roams the halls at your organization and helps with various things such as small deliveries. However, you notice that many employees are opting not to use the robot. When you ask them why they tell you that the robot looks “creepy” and they would rather not interact with it. What’s going on here?

Lack of understanding the robot’s usefulness

The bot is falling into The “Uncanny Valley”

Bias towards the robot

Safety and reliability issues that impact bot usefulness

NEW QUESTION 35
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?

Data Acquisition / Ingest / Capture

Retraining Pipelines

Feature Engineering

ELT Pipeline

NEW QUESTION 36
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?

Feature Engineering

Data Acquisition / Ingest / Capture

ELT pipeline

Retraining Pipelines

NEW QUESTION 37
You’re working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs. Specifically, the images you need are of people in different poses. What is the best way to obtain this data?

Make use of this data by having employees pose in the positions required

Make use of data from different departments

Make use of this data from surveillance footage

Make use of Synthetic Training Data

NEW QUESTION 38
Your team is testing the NLP model they just created to make sure it’s performing as expected. Some of your team members want to move this model to production and move to the next iteration.
What’s wrong with this workflow?

You need to make sure the AI Go/No Go questions have been addressed

Nothing is wrong with this workflow. You can move to the next iteration

Team members should not be able to move to new projects until senior management signs off

Model Evaluation requires continuous model evaluation, retraining, and operationalization

NEW QUESTION 39
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?

Do not move forward and cancel the project altogether.

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.

Do not move forward until you can determine a clear problem definition.

Move forward with the project as planned. The problem definition will become clear later on in the project.

NEW QUESTION 40
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?

Bring in data centric methodology best practices to get this project back on track

Get the existing team up to speed and make sure existing Agile approaches can support the AI effort

Hire an entirely new team making sure there is at least one data scientist on this new team

Hire an outside consulting firm to handle the technical aspects while you train the team yourself on data centric best practices

NEW QUESTION 41
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?

Phase I

Phase II

Phase III

Phase IV

Phase V

Phase VI

NEW QUESTION 42
Your model is going to be used for continuous monitoring of machinery, with need for continuous, instant model predictions. What’s the most appropriate Model Operationalization approach?

Real-time prediction

Web service / Microservice

Batch prediction

Stream learning

NEW QUESTION 43
Using machine learning and other cognitive approaches to understand how to take past/existing behavior and predict future outcomes or help humans make decisions about future outcomes using insight learned from past behavior/interactions/data is a core part to which pattern(s) of AI?

Goal Driven Systems

Predictive Analytics & Decision Support and Patterns and Anomalies

Recognition Pattern

Predictive Analytics & Decision Support

NEW QUESTION 44
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:

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.

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.

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.

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.

NEW QUESTION 45
During CPMAI Phase IV: Model Development, which of the following is not done during this phase?

Algorithm Selection

Model training

Model tuning

Model Selection

NEW QUESTION 46
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?

Used for Hyperpersonalization

As an autonomous system removing the human from the loop

As an Augmented Intelligence system

As a programmatic approach for automation

NEW QUESTION 47
Your team is working on a new loan decision model that takes a number of factors and data points into consideration and then automatically approves or denies a loan. After a month in operation someone does a review and notices that the system is denying a large number of loans from a certain demographic when all other factors from people in other regions (such as age, salary, and credit score) are the same.
What is most likely happening here?

Biased data sets leading to algorithmic discrimination

Generative AI models hallucinating data results

Nothing is wrong, algorithmic decisions will never be 100%

Data privacy issues leading to data sharing concerns

NEW QUESTION 48
You’re working on a project and are working with personally identifiable information (PII). What’s the best approach to take when it comes to collecting and using this data?

Use noise reduction techniques to reduce all forms of data noise

Implement a new data privacy policy

Store the data in a data warehouse

If this data is not needed, use Data anonymization techniques to remove it before feeding to models