信用のあるPMI CPMAI認定試験の問題集

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PMI CPMAI Exam Syllabus Topics:

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

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PMI Cognitive Project Management in AI (PMI-CPMAI) 認定 CPMAI 試験問題 (Q244-Q249):

質問 # 244
Doctors have been utilizing a sophisticated AI-driven cognitive solution to help with diagnosing illnesses. The AI system is integrated with several medical databases. This allowed the AI system to learn from new patient data and adapt to the latest medical knowledge and practices. The final project report indicated that the AI model had degraded over time which impacted reliability and effectiveness. The AI system must comply with healthcare regulations from various countries.
What is the likely cause for the degradation issue?

A. Inadequate initial model validation
B. Data drift affecting model precision
C. Impact of data drift on model accuracy
D. Changes in business model requirements

正解：C

解説：
Data drift occurs when the characteristics of real-world patient data or medical knowledge change over time, causing the model's learned patterns to become less reliable. In a healthcare diagnostic system that continuously adapts to new data and changing medical practices, drift can reduce overall model accuracy and degrade performance.

質問 # 245
You're looking to take an image and have a Generative AI solution generate additional content beyond the bounds of the current image size. What Generative AI approach can you use?

A. Use of inpainting to replace image components
B. Use of super-resolution to enhance the existing image
C. Use of Generative Outpainting
D. Prompt engineering for new image generation

正解：C

解説：
Generative Outpainting is a technique that extends the content of an existing image beyond its original boundaries, creating new image areas that blend seamlessly with the original.

質問 # 246
Before fully deploying a new recommendation algorithm, the team wants to evaluate its performance against the existing system using real users. The goal is to measure impact on engagement and business metrics. What technique should be used?

A. Cross-validation
B. A/B testing
C. Data cleaning
D. Feature engineering

正解：B

解説：
A/B testing compares performance in real-world conditions.

質問 # 247
For AI projects the code and systems don't matter as much as the data. In fact, big data is what's powering much of this latest wave of AI. What's most important for your company to consider around data?

A. Collect enormous amounts of data - the more data the better.
B. Because of almost-infinite storage and compute power, collect as much data as possible and deal with organizing it later.
C. Have team members that have experience, understanding of tools, and the ability to deal with massive volumes of data.
D. Understanding which algorithms are best for your data needs.

正解：C

解説：
Having team members with the experience and skills to manage, process, and analyze massive volumes of data is crucial for leveraging big data effectively in AI projects. Proper expertise ensures data is handled correctly to drive meaningful insights and model performance.

質問 # 248
A project manager is evaluating the readiness of the team for an AI operationalization project.
They have identified a lack of experience with AI and data knowledge. If this issue is neglected, what is the outcome?

A. The project will face significant delays due to learning curves.
B. The project will achieve its objectives with external consultant help.
C. The project will proceed smoothly with existing knowledge.
D. The project will require frequent adjustments to the AI models.

正解：A

解説：
A lack of AI and data knowledge creates skill gaps that slow down planning, data preparation, model validation, deployment, and troubleshooting. If not addressed, the team will likely experience delays as they learn required concepts and practices during execution.

質問 # 249
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