QUESTION: 6

A consulting firm is preparing data for an AI-driven customer segmentation model. They need to verify data quality before data preparation.

What should the project manager do first?

Assess data completeness.
Implement data enhancement.
Conduct data cleaning.
Apply data labeling techniques.

Answer(s): A

Explanation:

Before any data preparation or modeling, PMI-CPstyle guidance on AI initiatives emphasizes data quality assessment as the first critical activity. Quality must be evaluated before cleaning, enrichment, or labeling so that the team clearly understands the condition of the raw data and the scope of remediation needed. One of the primary quality dimensions to check early is completeness--whether required fields are present, whether key attributes are missing, and whether coverage is sufficient across the population of customers for meaningful segmentation.

If completeness issues are severe, downstream activities such as data cleaning, enhancement, and modeling may propagate bias or produce unstable segments. By systematically assessing data completeness first, the project manager enables the team to: (1) quantify gaps, (2) decide whether to obtain additional data, and (3) prioritize subsequent cleaning and enrichment steps. Data enhancement (option B) and cleaning (option C) are important, but they are remedial actions that should be guided by the initial quality assessment. Data labeling (option D) is more relevant for supervised learning use cases than for unsupervised customer segmentation. Therefore, to verify data quality prior to preparation, the project manager should first assess data completeness.

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QUESTION: 7

An organization is planning their digital transformation initiatives by building an AI solution to focus on data-collection needs. The goal is to reduce the manual handling of data.

Which approach should be prioritized to achieve the objective?

Outsourcing data-processing tasks to third-party vendors
Implementing intelligent systems that can autonomously process and analyze data
Enhancing the current database infrastructure to handle larger volumes of data
Upgrading cloud storage solutions for better data management

Answer(s): B

Explanation:

In PMI-CPaligned AI program guidance, when an organization's goal is to reduce manual handling of data, the focus is on automation of data intake, processing, and basic analysis rather than simply scaling storage or outsourcing tasks. The most appropriate strategy is to implement intelligent systems that can autonomously process and analyze data. Such systems may include automated data pipelines, intelligent document processing, and AI-driven extraction and transformation services that remove repetitive manual steps.

Option B directly addresses this by creating an AI solution that can ingest, validate, structure, and summarize data with minimal human intervention. This not only reduces manual workloads but also shortens cycle times, improves consistency, and lowers the risk of human error. Outsourcing data- processing tasks (option A) still relies on human labor, just in another organization, and does not achieve true digital transformation. Enhancing database infrastructure (option C) or upgrading cloud storage (option D) improves capacity and reliability, but does not inherently reduce manual handling--they are enabling technologies, not automation mechanisms.

From an AI management perspective, a transformation initiative should prioritize intelligent automation of the data lifecycle, and that is best captured by implementing systems that autonomously process and analyze data as described in option B.

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QUESTION: 8

After implementing an iteration of an Al solution, the project manager realizes that the system is not scalable due to high maintenance requirements.
What is an effective way to address this issue?

Switch to a rule-based system to reduce maintenance complexity.
Incorporate a generative Al approach to streamline model updates.
Adopt a modular architecture to isolate different system components.
Utilize cloud-based solutions to enhance maintenance scalability.

Answer(s): C

Explanation:

When an AI solution is described as "not scalable due to high maintenance requirements," PMI-style AI governance and lifecycle guidance points toward architectural refactoring rather than simply changing technologies or deployment environments. High maintenance often stems from tight coupling, monolithic design, and lack of clear separation between data, model, business logic, and interface layers.

Adopting a modular architecture to isolate different system components (option C) directly addresses this problem. In a modular or microservice-oriented design, each component--data ingestion, feature engineering, model training, model serving, monitoring, etc.--is separated behind clear interfaces. This makes it much easier to update or replace one part of the system without impacting the whole, which reduces maintenance overhead and improves scalability over time. It also supports independent deployment, targeted testing, and selective scaling of the components that receive the heaviest load.

Switching to a rule-based system (option A) typically increases maintenance complexity in dynamic environments. Incorporating generative AI (option B) may change the modeling approach but does not inherently solve structural maintenance issues. Utilizing cloud-based solutions (option D) helps with infrastructure scalability but does not fix architectural coupling. Therefore, the most effective way to address non-scalability caused by high maintenance requirements is to adopt a modular architecture.

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QUESTION: 9

A project manager is preparing a contingency plan for an Al-driven customer service platform. They need to determine an effective strategy to handle potential system downtimes.

Which strategy addresses the project manager's objective?

Creating a robust customer service logging system to quickly identify and resolve issues
Implementing a manual override system for critical customer queries
Developing an automated fallback chatbot with limited capabilities
Providing extensive training to customer service representatives on handling Al failures

Answer(s): C

Explanation:

PMI-CPoriented AI risk and resilience practices emphasize continuity of service and graceful degradation when AI systems fail or are temporarily unavailable. For an AI-driven customer service platform, the contingency plan should ensure that customers still receive some level of assistance even when the main AI system is down. An automated fallback chatbot with limited capabilities (option C) embodies this principle by providing a simplified yet always-available channel.

Such a fallback system might offer only basic FAQs, simple intent handling, or routing to human agents, but it maintains a consistent experience and avoids a complete service outage. This is a classic "fail-soft" or "degraded mode" strategy often highlighted in AI operations and MLOps guidance: if the primary model or service is unavailable, the system automatically switches to a simpler, more reliable backup.

Logging systems (option A) are important for diagnosis but do not directly serve customers during downtime. Manual override for critical queries (option B) and extensive staff training (option D) are valuable complementary controls, yet they are human-dependent and slower to activate. PMI-style AI contingency planning stresses automated, pre-defined fallback paths wherever possible. Hence,

developing an automated fallback chatbot with limited capabilities best addresses the objective of handling potential system downtimes.

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QUESTION: 10

During the evaluation of an AI solution, the project team notices an unexpected decline in model performance. The model was previously achieving high accuracy but has recently shown increased error rates.

Which action will identify the cause of the performance decline?

Reviewing recent changes made to the model's architecture and parameters
Checking for issues in the data preprocessing pipeline that may have introduced noise
Increasing the amount of regularization to prevent overfitting
Analyzing the distribution of real-world data for potential shifts

Answer(s): D

Explanation:

In the PMI-CP in Managing AI guidance, monitoring and diagnosing AI model performance is framed as a lifecycle responsibility, not a one-time task.
When a model that previously performed well suddenly shows increased error rates, PMI emphasizes first checking for data drift and concept drift--that is, changes in the distribution or meaning of the real-world input data compared with the data the model was trained and validated on. The material explains that teams should "systematically compare current production data distributions with training and validation distributions to detect shifts that may degrade model performance, even when the model architecture has not changed." This is because many performance issues in production are driven not by the model code itself, but by changes in user behavior, population characteristics, upstream systems, or environmental conditions. By analyzing the distribution of real-world data for potential shifts, the project team can determine whether the cause is data drift, data quality issues, or a change in the underlying patterns the model is supposed to learn. Only once this is understood should they proceed to architectural changes, hyperparameter tuning, or retraining strategies. Therefore, the action that best identifies the root cause of the performance decline is to analyze the distribution of real-world data for potential shifts.

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