CPMAI認證 - CPMAI软件版

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PMI CPMAI Exam Syllabus Topics:

Section	Weight	Objectives
Trustworthy AI	9%	- Ethical considerations and bias - Transparency and explainability - Privacy and security
Machine Learning	13%	- Supervised, Unsupervised, and Reinforcement Learning - Algorithms and models (e.g., NLP, Computer Vision) - Deep Learning and Neural Networks
Data for AI	13%	- DataOps concepts - Data strategy and governance - Data preparation and preprocessing
AI Fundamentals	16%	- Types of AI and Machine Learning - AI capabilities and limitations - Concepts and terminology of Artificial Intelligence
CPMAI Methodology	41%	- Phase II: Data Identification & Curation 1. Identify data needs 2. Assess data readiness and quality - Phase V: Operationalization 1. Plan for deployment and integration 2. Define success criteria and monitoring - Phase III: Algorithm Selection & Model Training 1. Oversee AI/ML model technique selection 2. Manage model training and configuration - Phase I: Problem Identification 1. Identify business needs and solutions 2. Evaluate initial AI feasibility - Phase VI: Iteration & Monitoring 1. Continuous monitoring and iteration 2. Retraining and model drift management - Phase IV: Model Evaluation 1. Evaluate model performance against metrics 2. Oversee AI/ML model QA/QC
Managing AI	8%	- Managing AI project teams and resources - Risk management in AI projects - Stakeholder management

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最新的 CPMAI CPMAI 免費考試真題 (Q172-Q177):

問題 #172
A financial services firm is building an AI model to detect fraudulent transactions. Identifying and validating data sources is critical to the model's success. What is an effective method that helps to ensure data accuracy?

A. Employing a federated database system for decentralized data access
B. Setting up a batch processing system for data cleansing
C. Utilizing data lineage tools to track data origin and transformations
D. Implementing a blockchain-based ledger for transaction data

答案：C

解題說明：
Utilizing data lineage tools helps verify where the fraud detection data came from, how it was transformed, and whether it remained accurate and trustworthy throughout the data pipeline. This supports reliable validation of data sources before the model is trained or operationalized.

問題 #173
Recently, you implemented an augmented intelligence application at work to help employees do their job better. However, employees have been resistant to this change and aren't using the application as expected. What could have been done better to get the team to feel comfortable with this technology and use it? (Choose all that apply.)

A. Have the team that built the technology to relay to employees this tool is to augment, and not replace their jobs.
B. Have upper management relay to employees this tool is to augment, and not replace their jobs.
C. Ask end users what information and technology they need to help them do their job better and build the tool to help with these pain points.
D. Provide training for everyone to have all employees feel more comfortable using the technology even if they aren't using the technology yet.

答案：B,C,D

解題說明：
Engaging end users to address their needs, having upper management clearly communicate the augmentation purpose, and providing training all help build trust and adoption of new AI tools among employees.

問題 #174
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

答案：C

解題說明：
The critical error was not practicing model versioning and keeping all versions of the model.
Overwriting the original model without retaining previous versions prevents rollback to a known good model when performance issues arise. Proper versioning enables teams to track, compare, and revert to earlier models if needed.

問題 #175
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. Use an AutoML solution
C. Outsource the project ASAP
D. Put the project on hold until the resources needed become available

答案：B

解題說明：
AutoML tools automate algorithm selection and hyperparameter tuning, enabling less experienced teams to develop models efficiently without waiting for specialized data science resources.

問題 #176
A healthcare organization is evaluating a diagnostic screening model. Clinical stakeholders state that missing a positive case has far more serious consequences than a false alarm.
Which evaluation metric should be prioritized?

A. Specificity
B. Recall
C. Overall accuracy
D. Precision

答案：B

解題說明：
Recall measures the proportion of actual positive cases the model correctly identifies, so maximizing it minimizes false negatives. When a missed positive carries severe consequences, recall is prioritized even at the cost of additional false positives.

問題 #177
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