Technology Project Management Flashcards
7 cards from real CAIC practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Technology Project Management flashcards as text
Which AI project risk is best mitigated by establishing a model monitoring pipeline post-deployment?
Answer: Model performance drift over time
Model drift occurs when real-world data distributions shift, degrading accuracy, so continuous monitoring detects and triggers retraining.
In an AI project, a 'proof of concept' (PoC) phase primarily serves to:
Answer: Validate technical feasibility before full investment
A PoC tests whether the AI approach can solve the problem at small scale before committing full resources.
A project manager notices that AI model training jobs are consuming 3x the estimated GPU hours. The BEST immediate action is to:
Answer: Profile training bottlenecks and optimize hyperparameters or data pipelines
Profiling identifies the root cause of compute overrun so targeted optimizations can bring costs back in line.
Which methodology is MOST commonly adapted for AI/ML projects due to its iterative experimentation cycles?
Answer: Agile/Scrum
Agile's short sprints align with the experimental, iterative nature of model development and evaluation.
When managing a cross-functional AI team, which communication artifact best keeps data scientists, engineers, and business stakeholders aligned?
Answer: A model card documenting model capabilities and limitations
Model cards provide a standardized, accessible summary that bridges technical and business audiences.
A stakeholder requests a new AI feature mid-sprint. Following agile best practices, the project manager should:
Answer: Add it to the product backlog and prioritize it for a future sprint
Agile practice protects sprint goals while capturing new requests in the backlog for proper prioritization.
Which metric is MOST useful for tracking whether an AI project's iterative model improvements are delivering business value?
Answer: Model accuracy improvement vs. baseline business KPI change
Linking model accuracy gains to business KPI movement confirms technical progress translates to real-world value.