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PMI CPMAI_v7 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

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.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
You're looking to take an image and have a Generative AI solution generate additional content beyond the bounds of the current image size. What Generative AI approach can you use?

A. Use of super#resolution to enhance the existing image
B. Use of inpainting to replace image components
C. Use of Generative Outpainting
D. Prompt engineering for new image generation

Answer: C

Explanation:
Generative AI encompasses techniques that create new data based on learned patterns. Outpainting is the specific generative approach for extending an image's content beyond its original borders. It leverages the same learned representations as inpainting or super#resolution but focuses on generating entirely new regions around an existing image.

NEW QUESTION # 43
Senior management has tasked your group to analyze a data set to uncover insights into the data. What is the best approach to use to do this?

A. Data Mining or Data Analytics
B. Data Governance
C. Data preparation
D. Data Integration

Answer: A

Explanation:
CPMAI defines analytics as "the use of statistical and computational methods to extract meaningful insights from data." When the goal is to discover and highlight patterns and insights within existing datasets, applying data mining or analytics techniques is the appropriate action.

NEW QUESTION # 44
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 Anonymization
B. Perform Data Sampling
C. Perform Data Transformation & Multiplication
D. Purchase the data from a third party

Answer: C

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 # 45
Your team is looking to develop an RPA bot to help assist call center agents while on providing support. What type of bot should your team be creating?

A. RPA is not the right solution to this problem
B. Unattended bot
C. Augmented Intelligence
D. Attended bot

Answer: D

Explanation:
In the CPMAI Glossary, attended bots are defined as "software automation tools that work alongside humans (typically in front-office roles) to assist with tasks and boost productivity." Call-center assistance is a classic front-office scenario requiring a bot that human agents can invoke interactively.
=========

NEW QUESTION # 46
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. Find a citizen data scientist to help
B. Outsource the project ASAP
C. Put the project on hold until the resources needed become available
D. Use an AutoML solution

Answer: D

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 # 47
......

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