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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

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 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.

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PMI Cognitive Project Management in AI CPMAI v7 - Training & Certification Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
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?

A. Have one team build all models in-house and the other team use pre-trained models and see which team' s models perform better.
B. 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.
C. Evaluate your data and use only what you have and build all models in house.
D. 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.

Answer: B

Explanation:
The Pre-Trained and Third-Party Model Usage task in Phase II: Data Understanding directs teams to first assess whether external or foundation models are appropriate given the current data and objectives. If so, they should then research and select the specific pre-trained models that best align with the project's domain, performance needs, and integration constraints. This ensures suitability before committing to fine-tuning or ensemble strategies.
=========

NEW QUESTION # 72
Leadership wants a new HR system built that will better handle potential candidate matching. The project manager assigned to this project believes that the project is well-suited for AI, however they are unsure which pattern of AI this would be.
What should the project manager do?

A. Conduct a straw poll with stakeholders to determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
B. Determine which pattern of AI this project falls under so they can best collect the data needed and skill sets for the team.
C. Pick an algorithm that seems best suited for the problem and then determine which pattern of AI it is based on the algorithm selected.
D. Move forward without determining which pattern of AI this falls under.

Answer: B

Explanation:
In Phase I: Business Understanding, after performing the Go/No Go assessment, the CPMAI methodology requires teams to perform AI Pattern identification-mapping business objectives to one or more of the Seven Patterns of AI-so that the right data requirements, algorithms, and team skills can be scoped effectively. This early pattern identification helps accelerate design by leveraging best practices for that pattern .

NEW QUESTION # 73
Enhancing and cleaning data is an important action during which phase of CPMAI?

A. Phase I
B. Phase V
C. Phase III
D. Phase VI
E. Phase II
F. Phase IV

Answer: C

Explanation:
The CPMAI v7 methodology groups all data-centric preparation activities-including both data cleansing ("Clean data") and data augmentation ("Enhance & Augment data")-into Phase III: Data Preparation. In this phase, teams focus squarely on constructing the dataset to be used for modeling by performing all required cleaning, transformation, and enhancement operations.
Phase III: Data Preparation is defined in the Workbook's Table of Contents as covering Data Cleansing & Enhancement tasks ("Clean data" and "Enhance & Augment data") .
Under Phase III, the Generic Task Group: Data Cleansing & Enhancement explicitly lists "Task: Clean data" (bringing data quality to modeling-ready levels) and "Task: Enhance & Augment data" (producing derived attributes and new records) as core activities .
=========

NEW QUESTION # 74
Your team is working on an AI-enabled chatbot to be placed on the website. The goal of the chatbot is to be able to answer questions 24/7 to service clients around the globe. When evaluating your data you realize you don't have enough data to train the model.
What's the best course of action?

A. Do not move forward with the project
B. Ask your customer service team to generate additional data for you to use for the project
C. Research what Third-Party Models are available and purchase them to keep the project moving
D. Use the data that you have and keep the project moving

Answer: C

Explanation:
In Phase II: Data Understanding, the Pre-Trained and Third-Party Model Usage task directs teams to
"determine if team will use a model...developed by another team, or by a third-party organization" and to gather information about "costs to use model, origin of data and methods of training, performance measures, and other factors that will determine appropriateness of model" before proceeding . When in-house data is insufficient, leveraging existing third-party models is the recommended path to avoid project stall.
=========

NEW QUESTION # 75
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. ELT pipeline
B. Retraining Pipelines
C. Feature Engineering
D. Data Acquisition / Ingest / Capture

Answer: B

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 # 76
......

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Peter Miles Peter Miles

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