Updated CPMAI Practice Exam Questions

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PMI CPMAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Manage AI Model Development and Evaluation16%- Address model drift, explainability, and limitations
- Monitor performance, accuracy, and reliability
- Oversee model design, training, and validation
Topic 2: Support Responsible and Trustworthy AI Efforts15%- Manage bias, risk, compliance, and societal impact
- Ensure fairness, transparency, accountability
- Establish ethical and governance frameworks
Topic 3: Operationalize AI Solution17%- Deploy AI systems into production
- Establish monitoring, maintenance, and improvement processes
- Manage change, adoption, and governance post-launch
Topic 4: Identify Data Needs26%- Define data requirements and sources
- Ensure data quality, privacy, security, and compliance
- Plan data collection, storage, and infrastructure
Topic 5: Identify Business Needs and Solutions26%- Align AI initiatives with organizational strategy
- Define requirements, scope, and success criteria
- Evaluate feasibility and value of AI solutions

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PMI Cognitive Project Management in AI (PMI-CPMAI) Sample Questions (Q68-Q73):

NEW QUESTION # 68
A government agency is using an AI system to analyze public data for policymaking decisions.
The project manager needs to address risks related to data accuracy, privacy, and misuse. What represents the highest risk to the agency?

Answer: C

Explanation:
Within PMI-CPMAI's "Support Responsible and Trustworthy AI Efforts," privacy and security are treated as core, high-severity risks because they can trigger regulatory violations, reputational damage, and harm to individuals. PMI explicitly calls out the need to establish a privacy/security plan with encryption and access controls, privacy impact assessments, and secure handling of personally identifiable information (PII) across the AI lifecycle. If user data is stored in an unsecured database, the agency faces immediate exposure to breach, unauthorized access, and misuse--risks that are typically higher impact than stale data, vendor reliance, or even lack of transparency. In PMI guidance on AI data life cycle management, prolonged retention and weak security increase breach likelihood over time, making insecure storage a critical vulnerability that undermines trust and compliance. While transparency gaps are serious (PMI also emphasizes explainability requirements and audit trails), a direct security failure that exposes user data is generally the most acute and consequential risk because it can cause harm quickly and irreversibly, and it can halt the program through legal and policy intervention.


NEW QUESTION # 69
You are working for a large multinational organization and have been assigned to a new project.
For your new ML project you need to make sure you're managing data privacy and security as you're working with sensitive customer data.
What critical security issues do you need to make sure you address (Select all that apply.):

Answer: A,B,C,D


NEW QUESTION # 70
Clean, well-labeled datasets used for machine learning are partitioned into three subsets:
Training sets, Validation sets, and Test sets. As your team is doing this, what's the best way to split up this data?

Answer: C


NEW QUESTION # 71
A company needs to launch an AI application quickly to be the first to the market. The project team has decided to use pretrained models for their current AI project iteration. What is a key result of leveraging pretrained models?

Answer: D

Explanation:
Leveraging pretrained models can reduce the overall project timeline because the team starts from an existing model that has already learned general patterns. This can shorten development, training, and experimentation time for the current AI project iteration.


NEW QUESTION # 72
A project manager is preparing a contingency plan for an AI-driven customer service platform.
They need to determine an effective strategy to handle potential system downtimes. Which strategy addresses the project manager's objective?

Answer: A

Explanation:
Implementing a manual override system ensures that critical customer queries can still be handled when the AI platform is unavailable. This provides a practical contingency path that maintains service continuity and reduces operational risk during downtime.


NEW QUESTION # 73
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