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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

Topic 1

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.

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

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.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q72-Q77):

NEW QUESTION # 72
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?

A. Nothing is wrong with this workflow. You can move to the next iteration
B. You need to make sure the AI Go/No Go questions have been addressed
C. Model Evaluation requires continuous model evaluation, retraining, and operationalization
D. Team members should not be able to move to new projects until senior management signs off

Answer: B

Explanation:
Phase V of the CPMAI v7 methodology-Model Evaluation and Maintenance-includes a formal CPMAI Phase V Go/No-Go assessment before any model can be moved into production or on to the next iteration.
This checkpoint ensures that the model meets predefined business success criteria, quality metrics, and risk considerations prior to deployment. Skipping this Go/No-Go review bypasses critical governance questions and undermines the integrity of the AI lifecycle.
=========

NEW QUESTION # 73
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. Perform Data Sampling
B. Purchase the data from a third party
C. Perform Data Anonymization
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.
=========

NEW QUESTION # 74
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 IV
B. Phase V
C. Phase II
D. Phase I
E. Phase III
F. Phase VI

Answer: A

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 # 75
Your team is using a neural network algorithm to generate a Machine Learning Model. What specific artifacts need to be included? (Select all that apply.)

A. Hyperparameter settings
B. Bias-variance tradeoff
C. The algorithm code
D. Supporting training data

Answer: A,C,D

Explanation:
Algorithm selection/code must be documented under the Select Modeling Technique task, where teams
"document the actual algorithm/modeling technique to be used" .
Supporting training data pipelines are a core artifact of Phase III: Data Cleansing, which mandates "create a reusable data pipeline to collect, ingest, and prepare data for training purposes" .
Hyperparameter settings are captured in the Hyperparameter Optimization task, where teams "list the final, optimized settings" used for model building .
The bias-variance tradeoff is a conceptual consideration during evaluation but is not a discrete artifact to include in the project deliverables.
=========

NEW QUESTION # 76
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. Retraining Pipelines
D. Data Acquisition / Ingest / Capture

Answer: C

Explanation:
CPMAI's Model Development phase includes a specialized task-Fine-Tuning / Re-training of Pre-Trained Models-which requires teams to "determine and document what approach will be used to...re-train pre- trained models." Implementing retraining pipelines ensures the model can be iteratively updated with new data and configurations in a reproducible, automated fashion .
=========

NEW QUESTION # 77
......

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