Updated May-2026 Test Engine to Practice CPMAI_v7 Test Questions [Q32-Q51]

Rate this post

Updated May-2026 Test Engine to Practice CPMAI_v7 Test Questions

CPMAI_v7 Real Exam Questions Test Engine Dumps Training With 102 Questions

PMI CPMAI_v7 Exam Syllabus Topics:

Topic	Details
Topic 1	Machine Learning: This section is aimed at the Data AI Lead and addresses practical machine learning applications. It begins with classification, clustering, and reinforcement algorithms, including ensemble methods and evaluation against business needs. Afterwards, it examines neural network architecture design and deep learning implementation across multiple problem types. Generative AI and LLMs follow, covering use-case suitability, limitations, operation explanations, prompt engineering, fine-tuning, and integrating these technologies into augmented intelligence solutions.
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	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.

NO.32 You are establishing the data requirements for the project. Which of the following tasks is the least likely to impact data requirements?

The quality of the data you collect

The makeup of your data team

The volume of the data you collect

The location/source of your data collection

NO.33 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

NO.34 Your organization wants to keep an eye on AI systems for Governance purposes. What are the most crucial things to consider? (Select all that apply.)

Vendor procurement methods

AI System testing requirements

Continuous System monitoring

Data source identification

Algorithm selection

Human chain of accountability

Key Performance Indicators (KPIs)

ROI determination

NO.35 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

NO.36 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?

Automatic model assessment

Iterative modeling and evaluation

Automatic hyperparameter tuning

Automatic model selection

Automatic algorithm selection

NO.37 Upper management is looking to roll out a new product and wants to see if there are any patterns and insights that can be discovered from customer data. Your team has been tasked to discover these potential patterns and structures within this data.
Which type of machine learning approach would be most appropriate to pick for this problem?

All would work equally well

Unsupervised Learning

Supervised Learning

Reinforcement Learning

NO.38 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?

Data Scientists

Data Engineering and Data Scientists

Senior management

Data Engineering

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

NO.39 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?

K Nearest Neighbor

K-means

Neural Network

Hyperpersonalization

NO.40 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?

Purchase the data from a third party

Perform Data Transformation & Multiplication

Perform Data Sampling

Perform Data Anonymization

NO.41 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?

Outsource the project ASAP

Find a citizen data scientist to help

Put the project on hold until the resources needed become available

Use an AutoML solution

NO.42 As an organization building an AI solution for your current customers based in NYC, but with possible plans for future expansion, how should you handle worldwide AI laws and regulations?

Make sure to follow relevant data, privacy, and other important laws both in the US and where you’re likely to expand to in the coming year

Make sure to follow relevant data, privacy, and other important laws as it pertains to NYC

Make sure to follow relevant data, privacy, and other important laws as it pertains to the United States

You’re too small of an organization to be worried about laws at the moment

NO.43 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

NO.44 You want to make sure that in your HR hiring system that applicants have the ability to contest the result. In what layer of the Trustworthy AI framework do we address this need?

Responsible AI

Ethical AI

Transparent AI

Explainable AI

Governed AI

NO.45 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

NO.46 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.)

Process automation

Text summarization

Machine Translation

Classify and categorize content

Code generation

Improve search quality

NO.47 Enhancing and cleaning data is an important action during which phase of CPMAI?

Phase I

Phase II

Phase III

Phase IV

Phase V

Phase VI

NO.48 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

NO.49 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?

They did not have data governance in place

They did not practice model versioning and keep all versions of the model

They did not have a model retraining pipeline that took into account models

They did not practice model iteration and properly iterate on the model

NO.50 You are working for a large multinational organization and have been assigned to a new project. For your new ML project you need to make sure you’re managing data privacy and security as you’re working with sensitive customer data.
What critical security issues do you need to make sure you address? (Select all that apply.)

Compliance with Data Privacy Laws even if they are out of your physical jurisdiction

Securing model data and metadata

Securing data at rest

Securely storing all data collected for training purposes

NO.51 During CPMAI Phase II of your project, your team is going through their data collection needs. One team member wants to make use of pre-trained models while another member is adamantly against it.
As the project lead, what should you do?

Evaluate your data and use only what you have and build all models in house.

Evaluate your data and see if using pre-trained models make sense. If so, have the team see what pre- trained models your company already owns and use those.

Evaluate your data and see if using pre-trained models make sense. If so, have the team do research to find the ones that best suit your project.

Have one team build all models in-house and the other team use pre-trained models and see which team’ s models perform better.