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PMI Certified Professional in Managing AI Sample Questions (Q93-Q98):

NEW QUESTION # 93
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. Proceed with development despite data issues
B. Verify data quality and stakeholder alignment
C. Launch the AI project without further assessment
D. Focus solely on technology upgrades, not data

Answer: B

Explanation:
In PMI-CPMAI-aligned practice, a go/no-go assessment is a formal checkpoint where technology, data, governance, risk, and stakeholder factors are evaluated against predefined criteria. If this assessment uncovers that multiple technology and data factors are insufficient, the appropriate response is not to proceed, but to pause and address those deficiencies. The project manager's role is to coordinate further analysis of data readiness (availability, quality, completeness, relevance) and verify that stakeholder expectations and commitments are still aligned with the AI initiative's constraints and risks.
Option A-verify data quality and stakeholder alignment-captures this corrective step. It reflects the PMI principle that AI projects must be based on trustworthy data and shared understanding; otherwise, model outcomes may be unreliable, non-compliant, or misaligned with business value. Options B, C, and D effectively ignore or downplay the red flags discovered in the assessment, which violates disciplined, risk-aware AI governance. Proceeding despite known gaps, focusing only on technology while neglecting data, or launching without further assessment directly contradicts structured go/no-go decision logic and could expose the organization to operational, ethical, or regulatory failure.
Therefore, the appropriate action after an unfavorable go/no-go outcome is to re-verify and remediate data quality issues and ensure stakeholder alignment (option A).

NEW QUESTION # 94
During the initial phase of an AI project, the team is assessing project success criteria. The project manager discovers that the project may be violating some compliance rules.
What problem describes the issue the project team is facing?

A. Failure to identify applicable data regulations early on
B. Lack of clarity on the project's business objective
C. Inadequate separation of cognitive and noncognitive software
D. Absence of a clear AI go/no-go assessment

Answer: A

Explanation:
In the PMI-CPMAI view of AI project governance, one of the earliest and most critical responsibilities in the lifecycle is the identification of all applicable legal, regulatory, and policy requirements, especially those related to data usage, storage, transfer, and retention. When a project reaches the stage of defining success criteria and only then discovers that it may be violating compliance rules, this is characterized as a failure to identify data and AI-related regulations early in the project.
PMI-CPMAI stresses that regulatory scoping must be done in the initiation and planning phases, before detailed design and implementation, because regulations fundamentally constrain what data can be used, how it can be processed, and which AI techniques are permissible. Missing this step leads to rework, redesign, and in some cases project stoppage. It is not primarily a problem of unclear business objectives, nor of separating cognitive vs noncognitive components, nor simply a missing go/no-go gate. Instead, the core issue is that the team did not perform a sufficiently thorough regulatory and compliance assessment at the outset, so non-compliant practices surfaced only later. Hence, the problem is best described as failure to identify applicable data regulations early on.

NEW QUESTION # 95
A financial services firm is integrating AI to enhance fraud detection. To oversee data evaluation, the project manager needs to ensure the integrity and accuracy of input data, including transaction histories and customer profiles.
Which method provides the results that address the requirements?

A. Applying a visualization generator to create data flow diagrams
B. Utilizing a prompt pattern to guide the AI model's training process
C. Implementing alternative approaches to process data differently
D. Using a fact checklist to systematically verify data sources

Answer: D

Explanation:
In AI initiatives for financial fraud detection, PMI-style AI data governance emphasizes that the integrity, provenance, and reliability of input data must be established before modeling. Transaction histories and customer profiles are high-risk, regulated data, so the project manager is expected to apply structured, repeatable verification methods rather than ad hoc checks. A fact checklist to systematically verify data sources directly supports this requirement. Such a checklist typically includes validation of data origin (systems of record), timeliness, completeness, consistency across systems, documentation of transformations, and confirmation that data has not been tampered with in transit or storage.
Within an AI governance framework, these checklists form part of data control evidence, supporting auditability and regulatory compliance. They also help uncover misalignments such as missing transaction fields, inconsistent customer IDs, or unexplained gaps in history-all of which can materially degrade model accuracy and fairness. In contrast, prompt patterns (option A) address LLM behavior rather than data integrity; alternative processing approaches (option C) do not ensure correctness of the underlying data; and visualization of data flows (option D) helps understanding architecture but does not validate the truthfulness or accuracy of the data itself. Therefore, using a fact checklist to systematically verify data sources is the method that best addresses the need to ensure data integrity and accuracy.

NEW QUESTION # 96
A hospital system has been using a chatbot and has received complaints from end users. The end users believe they are speaking to a person but are frustrated when answers do not make sense.
To help ensure end users know that they are engaging with an AI chatbot, what should be considered to support transparency?

A. Operationalize advanced algorithms
B. Use of interpretable AI models
C. Disclosure notice with each use
D. Inclusion of diverse data sets

Answer: C

Explanation:
Responsible and transparent AI-key themes in PMI-CPMAI-require that end users understand when they are interacting with an AI system rather than a human. In this scenario, end users mistakenly believe they are chatting with a person and become frustrated when responses are nonsensical. PMI-style responsible AI and ethics guidance emphasizes clear disclosure, user awareness, and expectation management as essential controls to protect trust and reduce harm.
The most direct way to support transparency here is a disclosure notice with each use (option C), for example a visible label or brief statement indicating "You are interacting with an AI-powered chatbot." This can appear at session start, in the chat header, or near the input box and may be reinforced periodically.
Inclusion of diverse datasets (option A) and interpretable models (option D) are important for fairness and explainability but do not solve the misunderstanding about the chatbot's identity. Operationalizing advanced algorithms (option B) might improve answer quality, but again, it does not address the core transparency issue. Therefore, to ensure users know they are engaging with an AI chatbot, the system should present a clear disclosure notice with each use.

NEW QUESTION # 97
A government agency is operationalizing a new AI tool for predictive policing. The project manager needs to identify data subject matter experts (SMEs) to ensure data quality and relevance. The project team has access to historical crime data, socioeconomic data, and real-time incident reports.
Which method will help in determining the data SMEs for this project?

A. Reviewing certifications in advanced data analytics and machine learning
B. Conducting workshops to assess knowledge in real-time incident data processing
C. Identifying individuals who have worked on similar AI tools in policing
D. Evaluating the team's familiarity with historical crime and socioeconomic data

Answer: D

Explanation:
In CPMAI's Data Understanding phase, the methodology emphasizes identifying data sources, ownership, quality, and the people who truly understand those data assets. Data subject matter experts (SMEs) are not defined purely by generic analytics skills or by having worked on AI before; they are defined by deep familiarity with the specific datasets and domain context that drive the AI solution.
For predictive policing, the key datasets are historical crime data, socioeconomic data, and real-time incident reports. CPMAI guidance stresses that teams must understand how these datasets are generated, what biases they may contain, their limitations, and how they relate to the real-world processes they represent. Therefore, the best way to identify appropriate data SMEs is to evaluate who on the team (or in the wider organization) already has strong familiarity with these concrete data sources, their structures, and usage history.
Options focusing on prior AI tools, workshops on a single data stream, or generic analytics certifications do not guarantee deep, source-specific knowledge. Aligning with CPMAI's data-centric approach, evaluating the team's familiarity with historical crime and socioeconomic data is the most appropriate method, making option C correct.

NEW QUESTION # 98
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