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| Certification Vendor: | PMI |
|---|---|
| Exam Name: | PMI Certified Professional in Managing AI (CPMAI) Exam |
| Exam Number: | CPMAI |
| Exam Format: | Multiple choice |
| Available Languages: | English |
| Sample Questions: | PMI PMI-CPMAI Sample Questions |
| Pre Condition: | No formal prerequisite is publicly standardized; PMI recommends familiarity with project management and AI concepts. |
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NEW QUESTION # 118
A team needs to identify which parts of the project they are working on will require AI and which will not. In addition, they need to determine technology and data requirements.
Which method should be used?
Answer: C
Explanation:
PMI-CPMAI describes a very practical early-stage activity: breaking down a solution into components or sub- functions and then deciding which components actually require AI and which do not. This is often referred to as a components-based analysis. The idea is to decompose the overall workflow or product into units such as data ingestion, preprocessing, prediction, rule-based decisioning, user interface, reporting, and integration layers.
For each component, the team asks:
* Does this require cognitive capability (learning from data, pattern recognition, probabilistic reasoning)?
* Or can it be handled by conventional software, rules, or existing systems?At the same time, they identify technology and data requirements: data sources, data quality, storage, pipelines, compute needs, and integration points for each AI-relevant component. PMI-CPMAI ties this directly into later tasks such as technical feasibility, architecture design, and MLOps planning.
Detailed data mapping (option A) is useful but focuses mainly on information flows, not necessarily on AI vs non-AI partitioning. Technical feasibility assessment (option B) evaluates whether a proposed AI approach is realistic but presumes that the AI portions are already identified. Only components-based analysis (option C) simultaneously answers "which parts need AI, which do not, and what are the tech/data needs for each?", which matches the scenario precisely.
NEW QUESTION # 119
A telecommunications company is adopting an AI-based customer service chatbot. They are concerned about potential quality issues affecting customer satisfaction.
What should the project manager do?
Answer: C
Explanation:
From a PMI-CPMAI perspective, concerns about quality and customer satisfaction must be addressed first at the planning level, not only reactively once the chatbot is live. For AI-enabled services such as a customer service chatbot, the project manager is expected to define a formal quality management approach that covers:
what "quality" means for this AI system (e.g., accuracy of responses, relevance, tone, response time), how it will be measured, and which controls and tests will be applied throughout the lifecycle.
A comprehensive quality assurance (QA) plan typically includes: clearly defined quality criteria and success metrics, test strategies (unit tests, conversation flow tests, usability tests, bias checks), acceptance thresholds, evaluation datasets, user journey scenarios, procedures for handling low-confidence outputs, and mechanisms for ongoing monitoring once in production. PMI-CPMAI guidance on AI lifecycle management stresses that these elements must be designed before wide rollout so that risks to customer experience are proactively controlled rather than discovered ad hoc.
Actions like beta testing, setting up monitoring teams, or doing regular performance reviews are valuable, but they are individual techniques that should exist inside an overarching QA framework. The best initial step that a project manager should take, given generalized concern about potential quality issues, is therefore to develop a comprehensive quality assurance plan for the chatbot.
NEW QUESTION # 120
A government project plans to implement an AI-based fraud detection system and the project team needs to define the success criteria. They identified potential improvements in detection accuracy, reduction in investigation time, and cost savings as key performance indicators (KPIs). However, they are unsure how to effectively quantify these KPIs.
Which two approaches should be used? (Choose 2)
Answer: B,E
Explanation:
For an AI-based fraud detection system, PMI-CPMAI-aligned guidance on benefits realization and performance management stresses that success metrics must be quantified against a clear baseline and monitored continuously over time. To properly define and measure KPIs such as detection accuracy, reduced investigation time, and cost savings, the project team should first establish a baseline using historical data comparisons (D). That means analyzing historical fraud cases, prior detection rates, average investigation duration, and historical financial losses to understand "pre-AI" performance. This provides a reference point against which improvements can be measured in a verifiable way.
In addition, PMI-CPMAI emphasizes continuous performance monitoring (B) as part of AI lifecycle governance. Fraud patterns, transaction volumes, and user behavior evolve, so model performance relative to KPIs must be tracked on an ongoing basis using dashboards and periodic evaluations. This supports early detection of performance degradation, allows recalibration of thresholds, and validates that business benefits (e.g., decreased losses, reduced workload) are being sustained.
Relying only on qualitative feedback, random benchmarks, or purely theoretical targets does not meet PMI-CPMAI expectations for evidence-based measurement and governance. Therefore, the two appropriate approaches are: implementing a continuous performance monitoring system (B) and establishing a baseline using historical data comparisons (D).
NEW QUESTION # 121
An aerospace engineering firm is developing a machine learning model to predict component failures. The project manager needs help to ensure the training data is representative of real-world scenarios. Which method will meet the project manager's objective?
Answer: B
Explanation:
PMI's CPMAI/PMI-CPMAI guidance emphasizes that, in the Data Understanding and Data Preparation phases, the team must identify appropriate datasets, evaluate training data requirements, validate "ground truth" quality, and explicitly assess data representativeness and potential bias issues before moving forward.
Using historical data from multiple sources best supports representativeness because it increases coverage across operating conditions, environments, and failure modes that occur in real deployments (different fleets, sensors, maintenance practices, and duty cycles). This directly aligns with PMI's expectation that the project manager ensures readiness of data for model development through quality checks and representativeness assessments as part of go/no-go decisioning. In contrast, relying solely on synthetic data can reduce fidelity and distort real-world distributions if not carefully validated; competitor data often has ownership and fit-for- purpose limitations; and real-time monitoring is useful operationally but does not inherently make the training dataset representative. Therefore, aggregating and reconciling multi-source historical data is the most PMI- aligned method to meet the objective of representative training data prior to model development and evaluation.
NEW QUESTION # 122
A project team is overseeing the data evaluation for an AI model predicting customer churn. They observed that the model ' s predictions are biased toward a particular class.
What is an effective technique to mitigate this bias?
Answer: A
Explanation:
The best answer is A. Using synthetic data generation . PMI's CPMAI exam outline explicitly includes supervising data augmentation and synthetic data generation as part of managing AI data preparation, and it also highlights the need to address bias, validate data preprocessing results, and ensure the data is suitable before and during model development. When predictions are biased toward a particular class, that usually points to an imbalance or under-representation problem in the training data. Synthetic data generation is an effective mitigation technique because it can increase representation for the weaker class and improve model learning across the full population.
Option B, stratified sampling, is useful for preserving class proportions in train-test splits and for evaluation discipline, but it does not directly correct a class imbalance problem as effectively as targeted synthetic augmentation. Option C affects optimization efficiency, not fairness or class representation. Option D may tune performance, but hyperparameter changes do not address the root issue if the data itself is skewed. PMI's materials also note that trustworthy AI requires active management of bias, risk, and compliance gaps , which supports selecting a data-centric mitigation approach rather than relying only on model tuning.
NEW QUESTION # 123
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