Passing PMI CPMAI_v7 Exam Using 2025 Practice Tests [Q20-Q36]

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Passing PMI CPMAI_v7 Exam Using 2025 Practice Tests

CPMAI_v7 Study Guide Brilliant CPMAI_v7 Exam Dumps PDF

PMI CPMAI_v7 Exam Syllabus Topics:

Topic	Details
Topic 1	AI Fundamentals: This section measures the abilities of a Project Manager and explores foundational AI concepts, including its definition, links to human cognition, and differences across AGI, Strong, Weak, and Narrow AI. It includes understanding the Turing Test and cognitive computing, dispelling myths, and applying augmented intelligence in business contexts. The historical progression of AI, such as AI winters, symbolic logic, expert systems, and fuzzy logic, is examined along with reasons for AI’s current prominence and its role in digital transformation. The section continues to assess the identification of suitable AI use cases, understanding limitations, and adoption patterns like conversational AI, speech processing, anomaly detection, RPA, goal-driven systems, and integrated AI solutions.
Topic 2	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 3	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 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 20
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 21
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 22
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 23
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?

Phase I

Phase II

Phase III

Phase IV

Phase V

Phase VI

NEW QUESTION 24
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?

Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.

Collect enormous amounts of data – the more data the better.

Understanding which algorithms are best for your data needs.

Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.

NEW QUESTION 25
During which phase of an AI project should you consider Trustworthy AI considerations?

Phase I: Business Understanding

Phase II: Data Understanding

Phase VI: Model Operationalization

Every Phase of the AI project

NEW QUESTION 26
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 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.

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.

NEW QUESTION 27
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?

Take all the data your company has as well as purchase additional external data

Take inventory of all data your company has and use the relevant data

Take all the existing data you have and apply it to this project

Take inventory of all data your team has and use the relevant data

NEW QUESTION 28
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 29
Your organization wants to use Generative AI. What are examples of when Generative AI can and should be used? (Select all that apply.)

Human Augmentation

Explainable Decision-support systems

Content Generation

Data Augmentation for Training

Programmatic automated content generation

Virtual Avatars and Characters

NEW QUESTION 30
The team is working to build a data preparation pipeline for the conversational chatbot project. Which phase of CPMAI is this done?

Phase I

Phase II

Phase III

Phase IV

Phase V

Phase VI

NEW QUESTION 31
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?

Evaluate whether the solution can be done with automation.

Determine if the project fits within the scope, budget, and timeline set out.

Determine whether the project has a cognitive technology component and meets a short-term need.

Determine the long-term need for the organization and build the project to that long-term goal.

NEW QUESTION 32
Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What’s the most critical role to staff for in the Big Data / Data Engineering Environment?

Senior management

All roles are critical to staff in the Four different AI Tech environments

Data Engineering and Data Scientists

Data Scientists

Data Engineering

NEW QUESTION 33
When building your model you need to make sure you’re not only checking for performance and making sure the model is giving the expected results. You also need to make sure the model is accomplishing the business objective.
At what phase of CPMAI is this most appropriate to do this?

Phase IV

Phase I

Phase II

Phase VI

Phase III

Phase V

NEW QUESTION 34
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 35
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.

Data Scientist

Data Engineer

Citizen Data Scientist

Project Manager

NEW QUESTION 36
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