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| Certification Vendor: | PMI (Project Management Institute) |
|---|---|
| Exam Name: | PMI Certified Professional in Managing AI |
| Exam Number: | PMI-CPMAI |
| Exam Price: | USD $520 for PMI members / USD $670 for non-members |
| Real Exam Qty: | 120 |
| Exam Duration: | 150 minutes |
| Exam Format: | Multiple Choice |
| Certificate Validity Period: | 3 years |
| Related Certifications: | PMP (Project Management Professional) PMI-ACP (Agile Certified Practitioner) PMI-PBA (Professional in Business Analysis) |
| Passing Score: | Not publicly disclosed |
| Available Languages: | English |
| Sample Questions: | PMI PMI-CPMAI Sample Questions |
| Exam Way: | Computer-based testing at PMI-authorized Pearson VUE test centers worldwide |
| Pre Condition: | No mandatory prerequisites. However, basic project management knowledge (PMP or equivalent experience) is recommended. Secondary school diploma required if pursuing PMI membership. |
| Official Syllabus URL: | https://www.pmi.org/certifications/certified-professional-managing-ai-cp-ai |
数年以来の整理と分析によって開発されたPMI-CPMAI問題集は権威的で全面的です。PMI-CPMAI問題集を利用して試験に合格できます。この問題集の合格率は高いので、多くのお客様からPMI-CPMAI問題集への好評をもらいました。PMI-CPMAI問題集のカーバー率が高いので、勉強した問題は試験に出ることが多いです。だから、弊社の提供するPMI-CPMAI問題集を暗記すれば、きっと試験に合格できます。
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質問 # 121
A healthcare organization plans to develop an AI-driven diagnostic tool. To define the required data, the project manager needs to ensure data consistency and accessibility.
Which method should the project manager use?
正解：A、B
解説：
CPMAI's Data Understanding and Data Preparation phases stress that AI success in domains like healthcare depends on robust data pipelines that ensure consistency, quality, and accessibility before modeling begins.
Guidance describes these phases as profiling and assessing data, then performing cleaning, transformation, and structuring so that data are reliable and usable by downstream models.
A data quality assessment combined with ETL (extraction, transformation, loading) processes directly supports these objectives. ETL pipelines standardize formats across disparate systems, enforce validation rules, manage missing values, harmonize coding schemes (for example, diagnosis codes), and centralize data into accessible stores. This is exactly the kind of foundational work CPMAI describes as a prerequisite to effective model development, particularly in regulated sectors such as healthcare where inconsistent or inaccessible data can have clinical and regulatory consequences.
By contrast, using NLP to standardize records (B) is a specialized technique that may help later but does not replace a systematic quality and ETL process. Integrating EHR with ML algorithms (C) and designing hybrid cloud storage (D) are more about later technical integration and infrastructure than about defining and ensuring initial data consistency and accessibility. Thus, in line with CPMAI's data-centric guidance, performing a data quality assessment with ETL processes is the correct method, making option A the best answer.
質問 # 122
An inexperienced team is training a neural network model on a desktop computer, which is taking a significant amount of time.
Which recommended approach will reduce the model training time?
正解：D
解説：
Comprehensive and Detailed Explanation From PMI Certified Professional in Managing AI Documents:
The best answer is B. Train the model on graphics processing units (GPUs).
Neural network training involves computationally intensive mathematical operations, including matrix multiplication, tensor processing, gradient calculations, and repeated weight updates.
Graphics processing units are designed to execute large numbers of parallel calculations efficiently.
Consequently, many deep learning workloads can be trained significantly faster on appropriately configured GPUs than on conventional desktop CPUs.
PMI-CPMAI recognizes that selecting suitable computational resources is an important responsibility during AI project planning and development.
The project manager should ensure that the available infrastructure supports the model ' s complexity, dataset size, training requirements, and expected iteration cycles.
Why the other options are incorrect:
- A: Multiple desktop computers may provide distributed processing capabilities, but this approach requires additional configuration and does not guarantee efficient acceleration.
- B: Correct because GPUs can accelerate neural network computations through parallel processing.
- C: Splitting data into smaller datasets does not necessarily reduce total training time and may compromise training quality if managed incorrectly.
- D: Outsourcing transfers responsibility but does not identify the appropriate computational solution.
GPU acceleration should still be assessed against memory limitations, workload characteristics, available software, and project cost. PMI ' s examination outline includes provisioning computing resources appropriate for model training.
質問 # 123
A healthcare organization is preparing training data for an AI model that predicts patient readmissions. The team discovers inconsistent coding across clinics for the same diagnosis. Which action best addresses the problem during data preparation?
正解：D
解説：
PMI-CPMAI aligns data preparation with executing data cleansing and enhancement activities so that datasets meet model and operational requirements. Inconsistent clinical coding is a data quality issue that threatens accuracy, fairness, and interpretability, because identical conditions may be represented differently across sources. The PMI-aligned response is to determine and apply the necessary transformation steps- standardizing codes to a controlled vocabulary, mapping local codes to a canonical schema, normalizing formats, and documenting rules and lineage so the process is auditable. Ignoring inconsistencies (B) increases noise and can embed systematic bias (e.g., certain clinics appearing "higher risk" due to coding artifacts).
Relying only on synthetic data (C) can reduce fidelity if the synthetic process fails to reflect true clinical distributions. Skipping validation (D) violates responsible delivery expectations because it undermines patient safety and data integrity. PMI's responsible and trustworthy framing supports disciplined data readiness work before model development proceeds.
質問 # 124
A manufacturing company is implementing an AI system to optimize production schedules. The project manager needs to gather the required data from machine sensors, production logs, and supply chain databases.
During data collection, they notice discrepancies in machine sensor data.
What should the project manager do first?
正解：D
解説：
The best answer is D. Implement a robust data validation and correction process . In PMI-CPMAI, data understanding and data preparation require the team to evaluate training data requirements, validate data quality, perform data cleansing and enhancement, and make go/no-go decisions based on whether the data is fit for model development. When discrepancies are detected during collection, the first priority is to validate the data, identify the source of the inconsistency, and correct or isolate bad records before moving further into integration or modeling.
Option A may eventually be necessary, especially when combining sensor, log, and database sources, but harmonizing formats should not come before confirming whether the sensor data is accurate and reliable.
Option B is not a first-step governance response and does not directly address the quality issue. Option C could be appropriate only if the validation process shows that the sensors themselves are faulty; replacing hardware before confirming the root cause would be premature. PMI's methodology consistently stresses data quality validation and cleansing as foundational activities in AI projects. Since the scenario explicitly mentions discrepancies, the most appropriate first action is to validate and correct the data so later integration and model-building decisions are based on trustworthy inputs.
質問 # 125
A financial services firm is operationalizing an AI-driven fraud detection system. The project manager needs to ensure the tool complies with relevant data privacy laws while providing secure data access to only authorized personnel.
What is an effective technique to address these requirements?
正解：B
解説：
In an AI-driven fraud detection context, PMI-CP/CPMAI guidance on data governance stresses that compliance with privacy laws and the principle of "least privilege" must be enforced with technical access controls as well as policies. While a data classification policy and privacy impact assessments are important, they mainly describe and analyze risks; they do not by themselves prevent unauthorized access.
An effective technique that directly addresses "secure data access to only authorized personnel" is role-based access control (RBAC). RBAC ties access rights to defined roles (e.g., fraud analyst, data scientist, auditor), ensuring that users see only the data necessary for their job and nothing more. This supports compliance with privacy regulations that require data minimization, access limitation, and accountability. It also provides an auditable structure for who can access what, which is critical during regulatory reviews or incidents.
Within AI projects, RBAC should be applied across data stores, model monitoring dashboards, and operational interfaces so that sensitive transaction and identity data are protected end to end. Therefore, among the options presented, utilizing role-based access control (RBAC) to limit data access is the most direct and effective technique to satisfy both legal compliance and secure, authorized-only access.
質問 # 126
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