Domain 1 of 6

Failure Modes in Deployed Systems

The failure modes a product team meets in a shipped system. Hallucination, bias, injection, misuse and the quiet one, which is people trusting output further than it deserves.

Concepts

~17%

Of the exam

Practice questions

Concepts in this domain

01How AI features failThe ways an AI feature goes wrong in production, ordered by how often a product team actually meets them, with the cause behind each and the control that has a chance against it.02Over reliance and automation biasWhy people accept output they should have checked, why it gets worse as the system gets better, and what a product can do about it.03What generative AI is good and bad atThe capabilities worth building on, the failure modes that are properties of the technology and will outlast any model, and how to tell a suitable problem from an unsuitable one.04Responsible AI in practiceBias, fairness, robustness, safety and veracity treated as things that can be measured and acted on, plus the legal exposure that arrives with generative output.05The OWASP LLM Top 10The industry's shared list of what goes wrong in applications built on large language models, what the 2026 edition reordered and why, and what the list cannot say about a particular system.

Try a question from this domain

A support assistant has been live for four months. Usage is steady, no incidents have been raised, and nobody has measured answer quality since launch. The team reports the feature as healthy. What is the strongest objection?

AFour months is too short a period to draw any conclusion about quality.
BUsage should be falling if the feature is working, since it resolves issues.
CQuality cannot be measured after launch without a control group.
DThe absence of complaints is weak evidence, because most people who receive a poor answer stop using the feature rather than reporting it.

15 questions on this domain.

One per page, with a worked explanation.

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