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PMI Certified Professional in Managing AI Sample Questions (Q46-Q51):

NEW QUESTION # 46
A project team is working on an AI project that requires strict adherence to data privacy regulations. The team is in the initial stages of data collection and aggregation.
Which task will help to ensure regulatory compliance?

A. Developing a comprehensive data risk management plan
B. Implementing advanced encryption for all data transactions
C. Obtaining verbal commitments from stakeholders regarding data usage
D. Conducting a thorough data audit to identify sensitive information

Answer: D

Explanation:
In the PMI-CPMAI perspective on responsible AI and data governance, regulatory compliance starts with knowing exactly what data you have and how sensitive it is. Before you can design controls, encryption schemes, or risk plans, you must first perform a data audit and classification to identify personal, sensitive, and regulated data elements, as well as their sources, flows, and storage locations. This aligns with the guidance that early in the AI lifecycle, project teams should create a clear data inventory and mapping to understand which datasets fall under privacy regulations (such as health, financial, or personally identifiable information).
By conducting a thorough data audit to identify sensitive information, the project team can determine which regulations apply, what consent or legal basis is required, and where to apply specific safeguards (access controls, anonymization, retention limits, etc.). Encryption and broader risk management plans are important, but they are secondary steps that rely on the foundational insight gained from the audit. Verbal commitments from stakeholders have no formal regulatory standing. Therefore, in the initial stages of data collection and aggregation, the task that most directly supports regulatory compliance is a thorough data audit to identify sensitive information.

NEW QUESTION # 47
A project manager is considering different project management approaches for an AI solution deployment. They need to ensure the approach allows for iterative improvements and accommodates changing requirements.
Which approach is effective in this situation?

A. Incremental
B. Adaptive/agile
C. Hybrid
D. Predictive

Answer: B

Explanation:
PMI-CPMAI emphasizes that AI projects typically involve uncertainty, experimentation, and evolving requirements. Data can change, model behavior must be tuned, and stakeholders may refine success criteria as they see early results. Because of this, PMI frames AI work as well-suited to adaptive/agile approaches that support short iterations, continuous learning, and rapid feedback loops.
In an adaptive/agile approach, the team plans in smaller increments, regularly reprioritizes the backlog, and refines scope based on empirical evidence from model experiments and pilots. This allows them to update features, retrain models, and adjust data or architecture as new insights are gained. PMI-CPMAI links this directly to AI lifecycles, where experimentation, evaluation, and deployment are repeated cycles rather than one-off phases.
Predictive approaches are more rigid and assume stable, knowable requirements upfront, which is rarely realistic for AI behavior and data-driven insights. Incremental and hybrid can add some flexibility, but adaptive/agile is the explicit choice in PMI's guidance when iterative improvement and changing requirements are primary concerns. Therefore, the most effective approach for an AI solution deployment in this context is adaptive/agile.

NEW QUESTION # 48
An AI project team in the healthcare sector is tasked with developing a predictive model for patient readmissions. They need to gather required data from various sources, including electronic health records (EHR), patient surveys, and clinical notes. The team is evaluating which technique will help to ensure the data is comprehensive and reliable.
What is an effective technique the project team should use?

A. Utilizing real-time data integration from EHR systems to ensure data freshness
B. Implementing data augmentation techniques to enhance dataset diversity
C. Using federated learning to train models across decentralized data sources without centralizing data
D. Employing natural language processing (NLP) to extract relevant data from clinical notes

Answer: D

Explanation:
In the PMI-CPMAI body of knowledge, healthcare AI initiatives are repeatedly framed as data-intensive efforts that must integrate heterogeneous sources such as EHRs, patient-reported outcomes, and unstructured clinical narratives. The guidance stresses that "unstructured sources, including physician notes and narrative reports, often contain critical clinical context that will not appear in structured fields," and that project teams must use techniques that can reliably extract this information into analysis-ready form to achieve completeness and reliability of the dataset. This is where natural language processing (NLP) is highlighted as a key enabler: by systematically parsing and extracting diagnoses, treatments, comorbidities, timelines, and outcomes from free-text clinical notes, NLP makes these rich but messy data usable alongside structured EHR fields and survey data.
PMI-CPMAI also emphasizes that simply adding more data or distributing training (such as data augmentation or federated learning) does not guarantee that the underlying data are comprehensive; what matters is that all relevant signals are captured and normalized across modalities. NLP directly supports this by converting unstructured text into standardized features, reducing omissions and manual abstraction errors. Real-time EHR integration improves freshness, but not necessarily coverage across all sources. Therefore, to ensure the data is comprehensive and reliable for a readmission prediction model, employing NLP to extract relevant data from clinical notes is the most effective technique among the options.

NEW QUESTION # 49
An IT services company is working on a project to develop an AI-based customer support system. During data preparation, the project manager needs to clean and transform customer interaction logs.
What is an effective technique to handle any missing data?

A. Fill missing values with zeros without analysis
B. Ignore missing data if it seems insignificant
C. Remove records with missing values if minimal
D. Duplicate existing data to fill in missing gaps

Answer: C

Explanation:
In PMI-aligned AI data management practices, handling missing data is approached from a risk, quality, and fitness-for-use perspective. Before model development, the project manager must ensure that the dataset is not only complete enough, but also representative and unbiased for the intended AI use case. When the portion of missing data is minimal and not systematically biased, a common, acceptable mitigation is to remove those records so that the remaining dataset maintains integrity and consistency while avoiding the introduction of artificial or misleading values.
Options B and C (duplicating data or blindly filling zeros) can create serious distortions in the underlying data distribution, leading to biased model behavior, degraded performance, and weaker generalization, which contradicts responsible AI practices highlighted in PMI-style guidance. Simply ignoring missing data (option A) without a structured strategy or analysis is also discouraged, as it hides potential data quality issues and can propagate errors downstream.
Therefore, in line with good AI data preparation practice, when missingness is genuinely limited and not concentrated in critical attributes, removing records with missing values if minimal (option D) is the most effective and responsible approach among the given choices.

NEW QUESTION # 50
A team is running a forecasting project and wants to use previous user data to better predict future outcomes. However, the team does not have access to all the data they need.
Which action should the project manager take?

A. Move forward cautiously with the understanding that there may be a need for a pause mid-project
B. Move forward in order to remain on schedule with the project
C. Move forward while anticipating data access is given when needed. An iterative approach provides the ability to return to steps as needed later on
D. Do not move forward until access is given to all the necessary data

Answer: C

Explanation:
CPMAI explicitly frames AI and forecasting projects as iterative and incremental, not rigid, one-shot efforts. The methodology allows teams to progress through phases with the understanding that they may loop back when new data or insights become available. In a forecasting project where not all desired historical user data is accessible yet, the recommended approach is to move forward with what is available, while planning and documenting assumptions about missing data and potential impacts.
PMI/CPMAI guidance stresses that waiting for "perfect" data can stall value delivery and increase project risk. Instead, early iterations using partial but representative data help validate the problem framing, test pipelines, and surface data-access issues early, while governance and data owners work on unlocking additional datasets. The key is to acknowledge explicitly that the project is iterative: you may return to earlier data understanding and preparation steps as new data becomes available. This is exactly what option B describes-moving forward while anticipating additional access and leveraging an iterative lifecycle to revisit earlier steps-rather than freezing the project (C) or blindly pressing ahead without a plan (A or D).

NEW QUESTION # 51
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PMI Certified Professional in Managing AI Pass4sure Test - PMI-CPMAI Pdf Vce & PMI-CPMAI Latest Reviews

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PMI Certified Professional in Managing AI Sample Questions (Q46-Q51):

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