CAIC Software Development Lifecycle 5

Question 1

Which metric is MOST appropriate for tracking the health of a deployed AI model over time?

Accepted Answer

Model performance drift relative to baseline

Answer

Tracking performance drift against a baseline detects degradation due to concept or data drift, triggering retraining when thresholds are breached.

Question 2

A team is conducting a 'post-mortem' after an AI model caused a significant error in production. What SDLC artifact is MOST useful as input to this review?

Accepted Answer

Model monitoring logs and prediction audit trail

Answer

Monitoring logs and audit trails provide the factual record of model inputs, outputs, and decisions needed to diagnose root causes in a post-mortem.

Question 3

When transitioning an AI project from development to production, which risk is MOST often underestimated?

Accepted Answer

Operational infrastructure and model serving latency requirements

Answer

Teams often underestimate the engineering work needed to serve models reliably at scale, including latency, availability, and integration requirements.

Question 4

In Kanban applied to an AI team, Work In Progress (WIP) limits PRIMARILY help by:

Accepted Answer

Reducing bottlenecks and improving flow of tasks through the pipeline

Answer

WIP limits force the team to finish in-progress work before starting new items, surfacing bottlenecks and improving overall throughput.

Question 5

Which approach BEST handles changing business requirements mid-project in an AI SDLC?

Accepted Answer

Use iterative sprints with regular backlog refinement to adapt incrementally

Answer

Iterative development with regular backlog refinement allows teams to incorporate changing requirements without derailing the entire project.

(CAIC) Certified AI Consultant Practice Test

(CAIC) Certified AI Consultant Practice Test

CAIC Software Development Lifecycle 5