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  1. Home
  2. PMI Certification
  3. CPMAI_v7 Exam
  4. PMI.CPMAI_v7.v2025-12-15.q35 Dumps
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Question 1

You are working on the data engineering pipeline for the AI project and you want to make sure to address the creation of pipelines to deal with model iteration. What part of the pipeline best deals with this step?

Correct Answer: D
CPMAI's Model Development phase includes a specialized task-Fine-Tuning / Re-training of Pre-Trained Models-which requires teams to "determine and document what approach will be used to...re-train pre- trained models." Implementing retraining pipelines ensures the model can be iteratively updated with new data and configurations in a reproducible, automated fashion .
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Question 2

Major factors for the project you are currently working on are around the training time, cost, and complexity of training your models. Which algorithm is not the best choice given these constraints?

Correct Answer: B
Neural Networks-especially deep architectures-typically require extensive computational resources, longer training times, and higher infrastructure costs compared to simpler methods. In contrast, algorithms like Naive Bayes train very quickly on large datasets, and Gaussian Mixture Models or SVMs have more moderate training complexity and infrastructure demands. Therefore, given strict constraints on training time, cost, and complexity, Neural Networks are the least suitable choice.
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Question 3

Your team is working on an image recognition system to help identify plants. They have collected a large amount of data but need to get this data labeled.
Which phase of CPMAI is this done?

Correct Answer: C
Phase III: Data Preparation includes the Data Labeling generic task group. Specifically, the Label data task covers "identifying methods for data labeling and engaging in data labeling efforts," which is essential for supervised learning workflows like image recognition.
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Question 4

Your organization wants to keep an eye on AI systems for Governance purposes. What are the most crucial things to consider? (Select all that apply.)

Correct Answer: C,D,F,G
Continuous System monitoring (C): Phase VI's "Monitoring and maintenance plan" requires teams to define
"What continuous monitoring and management approach and tools will be used for the model in this iteration?" to ensure the model continues to provide expected results in operation .
Data source identification (D): In Phase II: Data Understanding, teams must "Describe Data," including "Data source formats" and "Training data identification," to maintain visibility into where the model's inputs originate-essential for governance and troubleshooting .
Human chain of accountability (F): The "Model Governance Framework" task directs project teams to document "Determination of Governance Team," identifying members who will serve as the "owners" of the model and be responsible for its usage, soliciting feedback, and addressing concerns-establishing a clear accountability structure .
Key Performance Indicators (KPIs) (G): Domain V's "KPI Measurement" task mandates that teams "Align model performance with business key performance indicators" and implement ongoing KPI evaluation as part of quality assurance, providing the metrics by which governance bodies assess model health and business impact .
Options A, B, E, and H fall outside the core ongoing governance activities defined in CPMAI v7. Continuous monitoring of deployed models, clear data lineage, defined human accountability, and KPI tracking are the pillars of robust AI governance.
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Question 5

An inexperienced team is training a neural network model on a desktop computer and this is taking a significant amount of time. What would you recommend to them to speed up model training?

Correct Answer: B
Training deep neural networks on CPUs is very slow. CPMAI's Glossary highlights that tensor processing units (TPUs) and GPUs are specialized hardware accelerators explicitly recommended to "accelerate the training and inference of machine learning models" by parallelizing the heavy matrix operations in neural- network layers. Switching from desktop CPU training to GPU-based training can reduce training time by orders of magnitude.
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