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>> CPMAI_v7 Original Questions <<
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NEW QUESTION # 84
Data Engineering is 80%+ of most AI projects, so building a good Data Engineering Environment is key to AI Project Success. As the manager of this project, you need to make sure you have correct staffing needs.
What's the most critical role to staff for in the Big Data / Data Engineering Environment?
Answer: A
Explanation:
CPMAI underscores that preparing and managing data pipelines is foundational: in Phase III: Data Preparation, teams "create a reusable data pipeline to collect, ingest, and prepare data for training" and for inference . Ensuring these pipelines exist and are maintained falls squarely to Data Engineering specialists.
While data scientists leverage these pipelines for modeling, the dedicated Data Engineering role is the single most critical hire to support a Big Data environment.
NEW QUESTION # 85
In the case that an algorithm you want to use isn't algorithmically explainable, AI systems should try to do the following:
Answer: C
Explanation:
Under Required AI Explainability Considerations, CPMAI mandates that when a chosen model is a "black- box" with limited native interpretability, teams must implement post-hoc interpretability techniques (e.g., feature#importance plots, surrogate models) to "interpret AI results so that cause and effect can be represented," ensuring stakeholders understand why the model makes its predictions.
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NEW QUESTION # 86
During CPMAI Phase II of your project, your team is going through their data collection needs. One team member wants to make use of pre-trained models while another member is adamantly against it.
As the project lead, what should you do?
Answer: D
Explanation:
The Pre-Trained and Third-Party Model Usage task in Phase II: Data Understanding directs teams to first assess whether external or foundation models are appropriate given the current data and objectives. If so, they should then research and select the specific pre-trained models that best align with the project's domain, performance needs, and integration constraints. This ensures suitability before committing to fine-tuning or ensemble strategies.
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NEW QUESTION # 87
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?
Answer: D
Explanation:
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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NEW QUESTION # 88
You are working with a dataset that has a high number of dimensions. You're running into issues because some dimensions don't have enough real examples to properly train the systems for predictable results. What' s your best course of action?
Answer: B
Explanation:
CPMAI's Phase II: Data Understanding includes verifying that you have sufficient data volume for each feature to support reliable model training. The learning curve concept underscores that model performance improves with additional training examples. When dimensions are under-represented, the team must source or generate more data-aiming for a minimum number of examples per feature-to avoid underfitting and ensure stable predictions.
NEW QUESTION # 89
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