How to Hire an AI Governance Specialist

• aProvider obligations always remain with the original foundation model developer, regardless of how extensively a downstream company modifies or rebrands the system
• bThey sit with the cloud provider operating the inference endpoint, since the regulation assigns duties to whoever runs the compute
• cDerivatives of open-weight models are exempt from provider obligations under the Act's open-source carve-outs
• dYour company takes on provider obligations if it substantially modifies the model or places the system on the market under its own name; the upstream lab keeps duties only for the base model itselfCorrect

Explanation: The Act allocates obligations down the value chain: substantial modification or rebranding a system under your own name makes you a provider with conformity, risk-management, and documentation duties. Responsibility does not default to the upstream lab or the cloud host, and there is no blanket exemption for fine-tuned open models.

Why it matters: Evaluates practical understanding of Responsible AI concepts and problem solving for AI Governance Specialist at the mid-level experience tier.

• aRoute every decision past a reviewer who has about four seconds per case and no visibility into why the model decided as it did
• bHave reviewers spot-check a random 0.1% of decisions quarterly and file a summary report with the compliance team
• cConfigure the model to email a weekly accuracy digest to a compliance mailbox that someone reads before the annual audit
• dGive trained reviewers the model's key drivers per decision, route edge cases and high-impact outcomes to them with real authority and sufficient time to reverse decisions, and monitor override rates as an oversight-health signalCorrect

Explanation: Meaningful oversight needs competence, information, time, and authority: reviewers see why the model decided, receive the uncertain and high-stakes cases, can change outcomes, and have their override behavior monitored. Four-second glances, tiny retrospective samples, and unread email digests are oversight theater that regulators increasingly call out.

Why it matters: Evaluates practical understanding of Responsible AI concepts and problem solving for AI Governance Specialist at the mid-level experience tier.

• aDeleting the model weights at shutdown so storage and GPU quota can be reclaimed for the next project immediately
• bAnnouncing the retirement externally as a transparency gesture, so customers and regulators see the lifecycle being managed
• cKeeping the endpoint alive indefinitely in a low-traffic mode in case an unknown consumer still depends on it
• dRetaining the final model version, validation records, and decision logs for the legally required retention period, notifying downstream consumers, and verifying via traffic logs that no hidden integration still calls the endpointCorrect

Explanation: Decommissioning is a governance event: auditors and litigants can demand evidence about historical decisions years later, and undiscovered integrations cause outages when the endpoint dies. Immediate deletion destroys audit evidence, external announcements are not the substance, and zombie endpoints kept 'just in case' create unmonitored risk.

Why it matters: Evaluates practical understanding of Responsible AI concepts and problem solving for AI Governance Specialist at the mid-level experience tier.

• aThe vendor's product deck plus a written assurance that their internal team has reviewed the model for bias
• bThe vendor's SOC 2 Type II report, since a strong security posture is a reliable proxy for fairness and validity
• cValidation evidence on populations similar to your applicant pool, adverse-impact analyses with methodology, model and data documentation, contractual audit and incident-notification rights, and a shadow pilot on your own historical data before go-liveCorrect
• dMinimal diligence if the vendor is large and widely adopted, since market penetration is itself evidence the model has survived scrutiny

Explanation: High-risk uses demand substantive evidence: validation on your population, adverse-impact methodology, documentation, contractual audit and notification rights, and your own pilot. Marketing decks and brand size are not evidence, and SOC 2 covers security controls rather than fairness or validity for employment decisions.

Why it matters: Evaluates practical understanding of Responsible AI concepts and problem solving for AI Governance Specialist at the mid-level experience tier.

• aA documented AI policy with defined roles and accountability, per-system risk assessment records, internal audits, management reviews, and corrective-action evidence forming a plan-do-check-act loopCorrect
• bVisible commitment artifacts — an AI ethics statement on the website and principles posters in the engineering areas
• cOutsourcing consequential AI decisions to an external consultancy so that accountability sits with certified specialists
• dCertification of each model's accuracy on an external public benchmark before it is admitted to the registry

Explanation: ISO 42001 is a management-system standard like ISO 27001: auditors look for policy, accountability, risk processes, internal audit, management review, and continual-improvement evidence. Posters and web statements are not a system, accountability cannot be outsourced, and leaderboard scores say nothing about management processes.

Why it matters: Evaluates practical understanding of Responsible AI concepts and problem solving for AI Governance Specialist at the mid-level experience tier.

• aNo obligations attach until a market surveillance authority opens an investigation into the specific deployment
• bA transparency notice on the careers page stating that AI is used in screening, comparable to a cookie banner
• cA risk management system, data-governance controls over training and validation data, technical documentation, automatic logging, human-oversight measures, conformity assessment, EU database registration, and post-market monitoring with serious-incident reportingCorrect
• dA one-time self-certification published at launch, with no continuing duties unless the system is substantially rebuilt

Explanation: High-risk systems carry the full stack: risk management, data governance, documentation, logging, oversight, conformity assessment, registration, and post-market monitoring with incident reporting. Obligations apply before any investigation, a banner is not a conformity assessment, and compliance is a continuing duty rather than a one-time claim.

Why it matters: Evaluates practical understanding of Responsible AI concepts and problem solving for AI Governance Specialist at the senior experience tier.

• aIt proves component evaluation is worthless, so the fix is to abandon automated evaluation in favor of manual review of outputs
• bIt shows system-level risk emerges from composition even when components pass unit evals — errors cascade and correlate — so governance must require end-to-end evaluation, integration-level monitoring, and a named owner for the composite systemCorrect
• cIt establishes that the LLM vendor carries sole liability for any error that surfaces anywhere in the chain
• dIt shows model chaining is inherently non-compliant, so the architecture should be prohibited by policy going forward

Explanation: Component metrics do not compose: errors cascade and interact, so assurance must live at the system level with clear ownership of the composite. The lesson is to add integration evals rather than abandon evaluation, liability cannot be waved upstream, and chaining is common practice that needs governance rather than prohibition.

Why it matters: Evaluates practical understanding of Responsible AI concepts and problem solving for AI Governance Specialist at the senior experience tier.

• aKeep the blanket ban and escalate consequences up to termination, since deterrence is the only lever that changes behavior
• bAccept the usage as proof the tools are valuable and low-risk, and formalize nothing until an incident forces the issue
• cRequire legal pre-approval of every employee prompt above a minimal length, enforced through the corporate proxy
• dPublish a followable acceptable-use policy, provide a sanctioned enterprise alternative with data protections, detect flows to unsanctioned tools via DLP with in-the-moment coaching, and reserve discipline for deliberate exfiltrationCorrect

Explanation: Bans without alternatives breed stealth usage, so effective programs pair policy and monitoring with a provisioned, safe tool and graduated enforcement. Pure punishment drives usage underground, ignoring the issue leaks data, and legal pre-approval of every prompt does not scale.

Why it matters: Evaluates practical understanding of Responsible AI concepts and problem solving for AI Governance Specialist at the senior experience tier.

• aShip it: approval accuracy is the governing metric, and compliant-looking reason codes can be generated from a static lookup table
• bTreat it as a documented trade-off under constraint: adverse-action reasons must reflect the model's actual decision logic, so constrain the model or add a validated, stability-tested explanation layer, and record the trade-off for model risk management before any approvalCorrect
• cReject the deep model outright, since financial regulators prohibit non-interpretable models in credit decisioning
• dShip it only for loans below a small-dollar threshold, where adverse-action rules can be treated as not applying

Explanation: The decision is a documented trade-off under regulatory constraint: reason codes must be accurate to the model's actual logic, so unstable explanations block launch until fixed. Accuracy does not override law, regulators permit ML with appropriate validation rather than banning it, and exempting small loans is still unlawful lending practice.

Why it matters: Evaluates practical understanding of Responsible AI concepts and problem solving for AI Governance Specialist at the senior experience tier.

• aNothing mandatory: incident reporting is voluntary industry best practice under the Act
• bReport only if the incident becomes public, since the obligation is triggered by reputational exposure
• cInclude the incident in an annual aggregate summary submitted alongside the post-market monitoring report
• dReport to the market surveillance authority without undue delay once a causal link is established, within strict ceilings — 2 days for critical-infrastructure disruption, 10 days for a death, 15 days otherwise — then investigate and take corrective actionCorrect

Explanation: The Act imposes mandatory, deadline-driven serious-incident reporting to market surveillance authorities, with ceilings tied to severity (2 days for critical infrastructure, 10 for death, 15 otherwise), plus investigation and corrective duties. It is neither voluntary, media-triggered, nor annual-aggregate.

Why it matters: Evaluates practical understanding of Responsible AI concepts and problem solving for AI Governance Specialist at the senior experience tier.