CPMAI考試大綱 & CPMAI熱門考題

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PMI CPMAI Exam Syllabus Topics:

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

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最新的 CPMAI CPMAI 免費考試真題 (Q29-Q34):

問題 #29
Creating machine learning models can be complicated. Your team wants to use tools called Automated Machine Learning (AutoML) to simplify the process. You know of another team that has used AutoML tools and it's saved the team a lot of time. However, what's the one area you should not have the AutoML tool help with?

A. Automatic model assessment
B. Automatic hyperparameter tuning
C. Automatic algorithm selection
D. Iterative modeling and evaluation
E. Automatic model selection

答案：D

解題說明：
Iterative modeling and evaluation require human judgment and domain expertise to interpret results and make strategic decisions, which AutoML tools cannot fully automate effectively.

問題 #30
Your model is going to be used for continuous monitoring of machinery, with need for continuous, instant model predictions. What's the most appropriate Model Operationalization approach?

A. Batch prediction
B. Real-time prediction
C. Web service / Microservice
D. Stream learning

答案：B

問題 #31
You have been brought on to manage a recognition project, specifically an image recognition project, for an Autonomous Retail application. You know that you need to make sure you have sufficient data for this project. What's the best way to approach this?

A. Take inventory of all data your company has and use the relevant data
B. Take all the data your company has as well as purchase additional external data
C. Take all the existing data you have and apply it to this project
D. Take inventory of all data your team has and use the relevant data

答案：A

解題說明：
Taking inventory of all company data and selecting relevant data ensures you use appropriate, high-quality datasets tailored to the project needs without unnecessary or irrelevant data.

問題 #32
Recently your company has been getting a large number of spam emails and some employees have been clicking on these suspicious emails causing a headache for IT. The head of IT wants to create a more robust spam filter and your team has been tasked with this project. What type of algorithm would you select for this problem?

A. Regression
B. Binary (or Binomial) Classification
C. Multiclass Classification
D. Clustering

答案：B

問題 #33
An AI project team has completed an AI go/no-go assessment. They have discovered several technology and data factors to be insufficient. Which action should occur?

A. Verify data quality and stakeholder alignment.
B. Launch the AI project without further assessment.
C. Focus solely on technology upgrades, not data.
D. Proceed with development despite data issues.

答案：A

解題說明：
When a go/no-go assessment shows insufficient technology and data factors, the team should not proceed until key readiness concerns are addressed. Verifying data quality and stakeholder alignment helps confirm whether the project can meet business goals, technical needs, and implementation expectations before development continues.

問題 #34
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