PracticeVideosBlogFlashcardsEspañol
All Practice Exams

100+ Free Lead AI Risk Manager Practice Questions

Prepare for the PECB Certified Lead AI Risk Manager exam with instant access — no signup required.

✓ No registration✓ No credit card✓ No hidden fees✓ Start practicing immediately
Not published Pass Rate
100+ Questions
100% Free

Loading practice questions...

Same family resources

Explore More PECB Certifications

Continue into nearby exams from the same family. Each card keeps practice questions, study guides, flashcards, videos, and articles in one place.

PECB ISO/IEC 27001 Lead Implementer

Practice QuestionsStudy Guide

PECB ISO/IEC 27001 Lead Auditor

Practice QuestionsStudy Guide

PECB Certified ISO/IEC 27001 Foundation

Practice QuestionsStudy GuideFlashcards

PECB Certified ISO/IEC 42001 Lead Implementer (AI Management)

Practice QuestionsStudy GuideCheat SheetFlashcards

PECB ISO/IEC 42001 Lead Auditor (AI Management)

Practice Questions

PECB Certified ISO/IEC 27701 Lead Auditor (PIMS)

Practice Questions
2026 Statistics

Key Facts: Lead AI Risk Manager Exam

80 MCQ, 3h

Exam Format

PECB Candidate Handbook

70%

Passing Score

PECB standard (Candidate Handbook)

Open-book

Exam Type

PECB Candidate Handbook

USD 1000

Exam-Only Fee (Lead)

PECB Candidate Handbook

5 domains

Competency Domains

PECB Candidate Handbook

3 years

Credential Validity

PECB (CPD + annual fee)

PECB Certified Lead AI Risk Manager is an open-book, 80-question, 3-hour multiple-choice exam (passing score 70%) covering AI risk principles and regulations, program governance, risk identification and analysis, evaluation/treatment/monitoring, and organizational learning. Exam-only fee is USD 1000; the exam is included when taken with PECB partner training.

Sample Lead AI Risk Manager Practice Questions

Try these sample questions to test your Lead AI Risk Manager exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1An organization deploys an AI system that screens job applicants and ranks candidates for hiring decisions. Under the EU AI Act's risk-based approach, how would this system most likely be classified?
A.Minimal risk, because it only supports an internal business process
B.High risk, because it is used in employment and worker management contexts
C.Unacceptable risk, because all automated hiring tools are prohibited
D.No classification, because the EU AI Act does not cover recruitment
Explanation: The EU AI Act classifies AI systems used in employment, worker management, and access to self-employment (including recruitment and candidate screening) as high risk. High-risk systems are permitted but must meet strict requirements on data governance, transparency, human oversight, and conformity assessment.
2What term describes the level of risk an AI workflow carries before any governance checkpoints, technical safeguards, or model refinements are applied?
A.Residual risk
B.Accepted risk
C.Inherent risk
D.Transferred risk
Explanation: Inherent risk is the baseline exposure that exists in the absence of any controls or treatments. Understanding inherent risk first allows the AI risk manager to show how governance and technical measures reduce exposure toward the residual level the organization is willing to carry.
3Which set of functions forms the core of the NIST AI Risk Management Framework (AI RMF)?
A.Govern, Map, Measure, Manage
B.Identify, Protect, Detect, Respond, Recover
C.Plan, Do, Check, Act
D.Scope, Assess, Treat, Monitor, Report
Explanation: The NIST AI RMF is organized around four functions: Govern (a cross-cutting function), Map (context and risk identification), Measure (risk analysis and tracking), and Manage (prioritization and treatment). Organizations use these functions to structure trustworthy AI risk practices.
4A risk manager notes that stakeholders have limited visibility into what influences a model's decisions, making it hard to validate results or explain outcomes to clients. Which AI governance principle is most directly at stake?
A.Sustainability
B.Reliability
C.Fairness
D.Transparency
Explanation: Transparency concerns the visibility stakeholders have into how AI outputs are produced, including what data and factors influenced a decision. When teams cannot see what drives model behavior, they cannot validate results, explain outcomes, or detect emerging issues — exactly the gap described.
5After an organization applies governance controls, technical safeguards, and model recalibrations to an AI system, what does the remaining exposure represent?
A.Inherent risk
B.Residual risk
C.Appetite risk
D.Systemic risk
Explanation: Residual risk is the exposure that persists after risk treatments and controls have been implemented. Leadership must consciously decide whether to accept this remaining level, which makes quantifying residual risk essential for informed risk acceptance decisions.
6Which international standard specifies requirements for establishing, implementing, maintaining, and continually improving an AI management system (AIMS) within an organization?
A.ISO 9001
B.ISO/IEC 27001
C.ISO/IEC 42001
D.ISO 22301
Explanation: ISO/IEC 42001 is the management system standard for artificial intelligence. It specifies requirements for an AI management system that helps organizations govern AI responsibly, addressing risk, accountability, and life cycle oversight in a certifiable framework.
7A government agency proposes an AI system that assigns citizens a general-purpose 'trustworthiness score' based on their social behavior, leading to detrimental treatment in unrelated contexts. Under the EU AI Act, this practice is best described as:
A.An unacceptable-risk practice that is prohibited
B.A high-risk system requiring conformity assessment only
C.A limited-risk system requiring a transparency notice
D.A minimal-risk system with no obligations
Explanation: Social scoring by or on behalf of public authorities that leads to detrimental or unfavorable treatment in unrelated contexts is listed among the prohibited AI practices in the EU AI Act. Such practices are considered an unacceptable risk to fundamental rights and are banned rather than merely regulated.
8In the NIST AI RMF's set of trustworthiness characteristics, which one specifically addresses concerns about harmful bias and discrimination in AI systems?
A.Valid and reliable
B.Secure and resilient
C.Privacy-enhanced
D.Fair — with harmful bias managed
Explanation: The NIST AI RMF defines 'fair — with harmful bias managed' as the trustworthiness characteristic concerned with equity and bias. It recognizes that bias is not purely a technical issue and must be managed across the AI life cycle, considering context and affected communities.
9How do explainability and interpretability differ as AI trustworthiness concepts?
A.They are synonyms describing whether a model is accurate
B.Explainability applies only to regulators, while interpretability applies only to developers
C.Explainability is about providing understandable reasons for outputs to stakeholders, while interpretability is about the degree to which a human can grasp a model's internal mechanics
D.Interpretability is a legal requirement, while explainability is purely voluntary
Explanation: Interpretability refers to how far a human can understand a model's internal workings — the meaning of its parameters and structure. Explainability is broader and outward-facing: the ability to give stakeholders a meaningful account of why a particular output was produced, even for inherently opaque models.
10Why does AI risk management require approaches beyond traditional IT risk management?
A.AI systems never process sensitive data, so traditional controls do not apply
B.AI systems can exhibit emergent behavior, depend heavily on training data quality, and may be opaque, creating risks traditional IT controls were not designed to address
C.Traditional IT risk frameworks legally prohibit their use for AI
D.AI risk is purely technical, so governance processes are unnecessary
Explanation: AI introduces risk characteristics that differ from conventional software: behavior learned from data rather than explicitly programmed, potential for emergent or unexpected outputs, opacity in decision logic, and new failure modes such as bias, drift, and adversarial manipulation. These demand AI-specific identification, analysis, and governance methods layered onto enterprise risk practices.

About the Lead AI Risk Manager Exam

The PECB Certified Lead AI Risk Manager certification validates the competence to identify, assess, treat, and monitor AI-related risks and to lead an AI risk management program. The open-book exam covers five domains: AI risk principles, concepts, and regulations (including the EU AI Act and NIST AI RMF); program and governance; risk identification and analysis; evaluation, treatment, and monitoring; and organizational learning and performance improvement.

Questions

80 scored questions

Time Limit

3 hours

Passing Score

70%

Exam Fee

USD 1000 exam-only (Lead level); included when taken with PECB partner training (PECB (Professional Evaluation and Certification Board))

Lead AI Risk Manager Exam Content Outline

~20%

AI Risk Principles, Concepts, and Regulations

Interpret AI risk principles and concepts: risk levels (inherent, residual, accepted), risk appetite and tolerance, trustworthy AI characteristics, governance principles such as transparency and accountability, and regulations and frameworks including the EU AI Act (Regulation (EU) 2024/1689) risk tiers and the NIST AI RMF Govern-Map-Measure-Manage functions

~20%

AI Risk Management Program and Governance

Identify and explain the requirements for an AI risk management program and governance: policy and objectives, scope and context, risk criteria, AI system inventories, roles and accountability, governance bodies, leadership commitment, communication and consultation, and integration with enterprise risk management

~20%

AI Risk Identification and Analysis

Identify and explain AI risk identification and analysis: AI-specific risk sources (bias, drift, adversarial attacks, hallucination, privacy leakage), risk registers, identification techniques (checklists, FMEA, scenario analysis), and qualitative, semi-quantitative, and quantitative analysis methods

~20%

AI Risk Evaluation, Treatment, and Monitoring

Evaluate, monitor, and measure AI risks: comparison against risk criteria, treatment options (avoid, mitigate, transfer, accept), treatment planning and approval, control effectiveness, fairness strategies by life cycle phase, residual risk acceptance, incident response, and KPI/KRI-based monitoring

~20%

Organizational Learning and Performance Improvement

Develop, evaluate, and improve AI risk management capabilities using training and awareness, competence assessment, internal audits, management review, corrective action, lessons learned from incidents, and continual improvement cycles

How to Pass the Lead AI Risk Manager Exam

What You Need to Know

  • Passing score: 70%
  • Exam length: 80 questions
  • Time limit: 3 hours
  • Exam fee: USD 1000 exam-only (Lead level); included when taken with PECB partner training

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

Lead AI Risk Manager Study Tips from Top Performers

1Learn the EU AI Act risk tiers cold: prohibited practices (e.g. manipulative techniques, social scoring), high-risk use cases (e.g. employment, credit, critical infrastructure), transparency obligations, and minimal risk
2Memorize the NIST AI RMF structure — Govern (cross-cutting), Map, Measure, Manage — and the seven trustworthiness characteristics including 'fair with harmful bias managed'
3Master the risk level vocabulary: inherent (baseline), residual (after treatment), and accepted risk, and know who has authority to accept risk above tolerance
4Practice the full risk process sequence: establish context and criteria, identify, analyze, evaluate, treat (avoid/mitigate/transfer/accept), then monitor with KPIs and KRIs
5Because the official exam is open-book, tab and annotate your training course materials so you can locate frameworks and definitions quickly during the 3 hours
6Do not neglect Domain 5: training, competence assessment, audits, management review, corrective action, and continual improvement are fully examinable

Frequently Asked Questions

What is the PECB Lead AI Risk Manager exam format?

The exam consists of 80 multiple-choice questions with a 3-hour time limit and a 70% passing score. It mixes stand-alone questions and scenario-based questions (a scenario followed by related questions), and each question offers three options. It is open-book: candidates may use training course materials and personal notes taken during the course, via the PECB Exams app and/or printed.

How does this free practice bank relate to the official exam?

This is an independent study aid, not official PECB material. It covers the same five competency domains, but with two format differences you should know: official PECB questions have three options while our bank uses four for tougher practice, and our bank has 100 questions versus the official 80. Use it to build domain knowledge, and consult the PECB candidate handbook for the official sample questions.

Which frameworks and regulations should I know for the exam?

The PECB course is built around established AI risk frameworks, principally the NIST AI Risk Management Framework (Govern, Map, Measure, Manage functions and trustworthiness characteristics) and the EU AI Act (Regulation (EU) 2024/1689) with its prohibited, high-risk, limited/transparency, and minimal risk tiers. ISO/IEC 23894 (AI risk management guidance based on ISO 31000) and ISO/IEC 42001 (AI management systems) are also relevant.

How much does the PECB Lead AI Risk Manager exam cost?

The exam-only price for a PECB Lead-level exam is USD 1000, per the candidate handbook. Candidates who take the training course through a PECB partner have the exam (first attempt plus one retake), the certification application, and the first year of the annual maintenance fee included in the course package.

What happens if I fail the exam?

There is no limit on retakes, but you must wait 15 days after a failed first attempt before the next one. Your results email identifies the domains where you performed poorly so you can target your revision. Candidates who trained with a PECB partner can retake once for free within 12 months of course completion; exam-only candidates pay retake fees.

What are the certification requirements beyond passing the exam?

Passing the exam entitles you to apply for a credential in the Lead AI Risk Manager scheme. The full Lead credential requires five years of professional experience (at least two in AI risk management), at least 300 hours of AI risk management activities, two professional references, and signing the PECB Code of Ethics. Credentials are valid for three years and maintained through CPD hours and an annual maintenance fee.