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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Manage AI Model Development and Evaluation	16%	- Monitor performance, accuracy, and reliability - Address model drift, explainability, and limitations - Oversee model design, training, and validation
Topic 2: Identify Data Needs	26%	- Define data requirements and sources - Plan data collection, storage, and infrastructure - Ensure data quality, privacy, security, and compliance
Topic 3: Identify Business Needs and Solutions	26%	- Evaluate feasibility and value of AI solutions - Define requirements, scope, and success criteria - Align AI initiatives with organizational strategy
Topic 4: Support Responsible and Trustworthy AI Efforts	15%	- Ensure fairness, transparency, accountability - Manage bias, risk, compliance, and societal impact - Establish ethical and governance frameworks
Topic 5: Operationalize AI Solution	17%	- Establish monitoring, maintenance, and improvement processes - Deploy AI systems into production - Manage change, adoption, and governance post-launch

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q249-Q254):

NEW QUESTION # 249
You're testing your model and it is overly sensitive to the fluctuations of data and having trouble generalizing. What type of problem is this?

A. You are underfitting the data
B. You have selected the wrong algorithm
C. You have selected the wrong data
D. You are overfitting the data

Answer: D

NEW QUESTION # 250
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. 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.
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. Have one team build all models in-house and the other team use pre-trained models and see which team's models perform better.
D. Evaluate your data and use only what you have and build all models in house.

Answer: B

NEW QUESTION # 251
You're running an AI project and want to speed up training of the model so that you can complete your current CPMAI iteration within the two-week timeframe the team has set. What's one approach to speed up model training?

A. Use or extend a pre-trained model
B. Use cloud-based technology
C. Use data from a previous project
D. Use Brute force method

Answer: A

Explanation:
Using or extending a pre-trained model (transfer learning) significantly speeds up training by leveraging existing learned features, reducing the need to train from scratch.

NEW QUESTION # 252
You're working on a computer vision application and realize that you do not have enough real world data for the project. You need additional data created to support your training needs.
Specifically, the images you need are of people in different poses. What is the best way to obtain this data?

A. Make use of this data from surveillance footage
B. Make use of Synthetic Training Data
C. Make use of this data by having employees pose in the positions required
D. Make use of data from different departments

Answer: B

Explanation:
Synthetic training data can be generated to create diverse, labeled images of people in various poses, effectively augmenting real-world data without privacy or logistical concerns.

NEW QUESTION # 253
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. Data Acquisition / Ingest / Capture
B. ELT Pipeline
C. Retraining Pipelines
D. Feature Engineering

Answer: C

NEW QUESTION # 254
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