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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Identify Business Needs and Solutions	26%	- Define requirements, scope, and success criteria - Evaluate feasibility and value of AI solutions - Align AI initiatives with organizational strategy
Operationalize AI Solution	17%	- Manage change, adoption, and governance post-launch - Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production
Identify Data Needs	26%	- Plan data collection, storage, and infrastructure - Define data requirements and sources - Ensure data quality, privacy, security, and compliance
Support Responsible and Trustworthy AI Efforts	15%	- Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks - Ensure fairness, transparency, accountability
Manage AI Model Development and Evaluation	16%	- Oversee model design, training, and validation - Address model drift, explainability, and limitations - Monitor performance, accuracy, and reliability

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q23-Q28):

NEW QUESTION # 23
Your team is working on a new project for finding the most optimal flow of warehouse robots on the warehouse floor. Which type of machine learning approach would be most appropriate to pick for this problem?

A. All would work equally well
B. Supervised Learning
C. Unsupervised Learning
D. Reinforcement Learning

Answer: D

NEW QUESTION # 24
In order for Supervised Learning approaches to work, they must be fed clean, well-labeled data that the system can use to learn from examples. But how do you get Labeled Data? As a team leader at a small startup, what approach would not be beneficial when trying to gather labeled data?

A. Hire a Contractor Workforce
B. Get your Users to Do it
C. Contract with Third Party Data Labeling Firms
D. Find a source of already labeled data

Answer: B

Explanation:
Relying on users to label data can lead to inconsistent, low-quality labels and is generally inefficient, making it less beneficial compared to professional or curated labeling approaches.

NEW QUESTION # 25
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?

A. They did not have a model retraining pipeline that took into account models
B. They did not have data governance in place
C. They did not practice model versioning and keep all versions of the model
D. They did not practice model iteration and properly iterate on the model

Answer: C

NEW QUESTION # 26
A project charter states that the AI solution will "significantly improve customer experience." What is the deficiency in this statement?

A. It does not specify the AI technique to be used
B. It does not reference the applicable regulatory framework
C. It does not identify the responsible business owner
D. It is not measurable and cannot support a go/no-go decision

Answer: D

Explanation:
Success criteria must be specific and measurable so that model evaluation can determine objectively whether the business objective is met. Qualitative statements provide no threshold against which results can be assessed at phase gates.

NEW QUESTION # 27
A project team has a modest dataset and is concerned that a single train-test split may produce an unreliable performance estimate.
What approach should the project manager recommend?

A. Apply k-fold cross-validation
B. Repeat training with different random seeds
C. Increase the proportion of data allocated to testing
D. Use the full dataset for both training and evaluation

Answer: A

Explanation:
K-fold cross-validation partitions the data into k subsets and rotates which subset serves as the validation set, averaging results across folds. This produces a more stable performance estimate and reduces the influence of any single partition.

NEW QUESTION # 28
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