AAIA practice questions
ISACA · AAIA · 200 questions
Original practice questions for the ISACA Advanced in AI Audit (AAIA) exam, covering AI governance and risk, AI operations (lifecycle, supervision, testing, threats, and incident response), and AI auditing tools and techniques — framed in civic and municipal scenarios such as city AI pilots, public-health records, permitting chatbots, 311 assistants, courts / clerk workflows, and transit / utilities operations.
This course contains the use of artificial intelligence.
About the AAIA exam
- Exam fee
- $599 USD
- Time allowed
- 2 hours 30 minutes
- Questions
- 90
- Passing score
- 450 (scaled, 200-800)
- Languages
- English, Simplified Chinese, Spanish
- Format
- Computer-based, 90 multiple-choice questions, at an authorized PSI testing center or remotely proctored (test centers only for residents of India, Mainland China and Hong Kong)
Schedule this exam The certification this earns
Exam details published by the vendor, checked 18 October 2026. Vendors change fees and formats without notice — confirm on the vendor's own page before you book.
Practice Quizzes
Test your knowledge with standard 20-question practice sets.
Quiz 1
Quiz 2
Quiz 3
Quiz 4
Quiz 5
Quiz 6
Quiz 7
Quiz 8
Quiz 9
Quiz 10
Browse by Domain
Study specific topics at your own pace.
AI Governance and Risk · 66 questions
- A city CIO asks internal audit to review a permitting chatbot design. Ordinance FAQs change often and must cite current code text. Which model approach best fits those municipal assurance needs?
- A county public-health office proposes a multimodal model that reads clinic intake forms and photos of vaccination cards. What should audit emphasize first when evaluating that design?
- A transit authority wants generative AI that drafts rider alerts. What should audit confirm before model selection proceeds?
- A housing authority considers a black-box vendor scoring model for waitlist prioritization. What explainability concern should audit raise?
- A court clerk pilot needs an AI summarizer limited to local procedural manuals, not open-web knowledge. Which requirement should audit verify is enforced?
- A water utility evaluates edge versus cloud inference for plant anomaly detection under continuity constraints. What should audit prioritize in that topology review?
- A 311 program lists latency, citation, and language-support requirements for a resident assistant model. How should audit use that list?
- A parks department wants a vision model for trail-condition photos. What should audit check before accepting the pilot metrics?
- A school district proposes student-chat tutoring AI. What special consideration should audit map before model approval?
- A municipal open-data office asks whether a generative model is needed or a rules-plus-search design suffices for structured dataset lookups. What should audit challenge?
- A city emergency-management team wants AI triage of 911 text tips. Which requirement set should audit review as mission-critical?
- A library consortium considers fine-tuning on patron chat logs. What training-data issue should audit flag?
- A building department reviews vendor claims that a plan-check AI meets local code coverage requirements. What should audit require?
- A county IT board asks audit to confirm an AI solution’s alignment before funding. What fit should audit evaluate?
- A city creates an AI steering committee but leaves permitting AI without a named business owner. What governance gap should audit highlight?
- A county adopts an AI policy binder that has never been mapped to operating procedures for 311 bots. What should audit assess?
- A housing authority AI inventory omits shadow GenAI tools caseworkers use on personal devices. What completeness issue should audit raise?
- A transit agency assigns AI ethics review only after go-live of a fare-evasion camera model. What lifecycle issue should audit cite?
- A municipal CIO AI charter lacks escalation paths when model outputs affect resident eligibility. What should audit recommend emphasizing?
- A school district AI program has no RACI for vendors, data owners, and internal audit. What should program management establish?
- A city council asks audit whether AI awareness training aligns to the published AI policy. What should audit evaluate?
- A water utility AI PMO tracks projects but not residual risk acceptance by executives. What linkage should audit seek?
- A court AI pilot has no periodic governance review cadence after initial approval. What should audit emphasize?
- A parks department delegates all AI vendor management to procurement without IT assurance input. What integration gap should audit note?
- A county AI board publishes principles but no measurable KPIs for program health. What should audit recommend?
- A city's 311 AI chatbot change board reviews prompts and model updates but never includes privacy or equity stakeholders. What should an auditor recommend?
- Several municipal departments purchase SaaS AI copilots without involving the enterprise architecture office. Which control best prevents uncontrolled AI sprawl?
- A city risk register lists permitting AI only under generic technology risk with no AI-specific scenarios. What finding should the auditor raise?
- A county public-health AI triage tool is scheduled for production, but inherent and residual risk were never documented. What should audit insist on before go-live?
- A transit predictive-maintenance model has no key risk indicators for false-negative rates that could miss safety defects. Which action best addresses the gap?
- A housing-eligibility AI control design looks strong on paper, but auditors find the control has never been tested in operation. What distinction should the report emphasize?
- A court transcription AI risk assessment scopes only the model and ignores effects on downstream clerk workflows. What should the auditor recommend?
- A water utility treats silent third-party model updates to a SCADA anomaly detector as low risk and skips re-assessment. What is the most appropriate audit response?
- A school district AI risk heat map underweights fairness risk for special-education recommendation tools. Which recommendation is most appropriate?
- Risk acceptances for a 311 chatbot are signed only by IT, not by the resident-services business owner. What should audit conclude?
- A city emergency AI dispatcher-assist tool has no documented risk appetite thresholds. What should leadership establish before wider use?
- A library recommendation AI skips threat modeling because staff say it only suggests books. How should an auditor respond?
- A county folds AI risk into an annual ERM refresh and never updates after major model-drift events. What improvement is most needed?
- A parks vision-model risk file omits privacy risk from geotagged resident-submitted trail photos. Which gap should audit highlight?
- A municipal vendor AI questionnaire is completed, but results never feed the enterprise AI risk register. What should audit recommend?
- A permitting AI ingests applicant Social Security numbers without a data-minimization review. What should the auditor recommend first?
- A public-health chatbot retains chat logs indefinitely for future training without retention rules. Which control gap is most significant?
- A housing AI shares case notes with a vendor whose subprocessor list audit cannot obtain. What is the primary privacy-governance concern?
- A transit ridership model trains on supposedly de-identified tap data that analysts show is still re-identifiable. What should audit emphasize?
- A court AI assistant can access sealed-case text without a purpose-limitation statement. What control should be required?
- A 311 GenAI tool scrapes the open-data portal plus unpublished citizen emails without classification checks. What should audit recommend?
- A school district AI uses student data for tutoring recommendations without parental notice. Which privacy-program action is most appropriate?
- A water utility AI data catalog lists datasets but does not tag which datasets train which models. What accountability gap does this create?
- A city completed a DPIA/PIA for a CRM, then added an AI scoring layer without updating the assessment. What should audit require?
- A library AI sends research-query embeddings to a cloud service without reviewing residency or encryption standards. What should the auditor recommend?
- A parks permit AI stores biometric face photos from kiosk check-in beyond the stated operational need. What should the auditor emphasize?
- A county AI program names data stewards for warehouses but assigns none to model feature stores. What gap should audit highlight?
- A municipal open-data AI remixes published datasets that include suppressed small-cell demographics. What privacy risk should the auditor flag?
- A city claims AI ethics alignment but cannot map practices to any recognized AI standard or framework. What should the auditor recommend?
- A hiring AI for city jobs shows disparate impact across demographic groups. What should audit prioritize?
- A public-health GenAI summarizer may invent citations that staff could treat as authoritative. What ethical concern should the auditor raise?
- A transit surveillance AI may perform biometric identification in public spaces subject to a local ordinance. What should the auditor verify?
- A housing chatbot disclaimer fails to meet emerging AI transparency and notice expectations for residents. What improvement should audit recommend?
- A court AI research tool may be used by self-represented litigants in ways that raise unauthorized-practice and due-process concerns. What should the auditor emphasize?
- A school district AI vendor contract ignores emerging AI intellectual-property and training-data licensing issues. What should audit recommend?
- A 311 AI policy cites GDPR-style individual rights even though the city is outside that jurisdiction. What should the auditor check?
- A water utility references NIST AI RMF language in policy but implements none of the framework functions. What finding is most appropriate?
- A parks department uses generative AI to create promotional art depicting recognizable residents without likeness or consent review. What should audit require?
- A county AI board asks whether ISO/IEC AI management standards should inform its control catalog. What is the most appropriate auditor response?
- A municipal AI trust-and-safety charter omits accessibility obligations for disabled residents. What should the auditor recommend?
- A city attorney asks internal audit to verify AI use against sector guidance for health, education, and public safety. What should audit do?
AI Operations · 92 questions
- A permitting model trains on outdated zoning tables with no AI-specific data freshness controls. What should the auditor recommend?
- A public-health AI feature store mixes research extracts with production PHI without segregation. What control gap should audit report?
- A transit ridership model lacks documented data quality thresholds before each retraining. What should the auditor require?
- A housing waitlist model uses proxy features strongly correlated with protected classes. What should audit emphasize?
- A 311 retrieval corpus contains duplicate and conflicting knowledge articles that feed a resident chatbot. What data-management action is most appropriate?
- A court AI pipeline has no lineage from source docket records to model input features. What should the auditor recommend?
- A water utility sensor stream feeding anomaly AI lacks schema-change detection. What control should be added?
- A school district tutoring AI scrapes the LMS nightly without validating consent flags on student records. What should audit require?
- A parks vision dataset is labeled by interns with no inter-rater reliability checks. What should the auditor recommend?
- A city open-data AI uses scraped PDFs without OCR quality acceptance criteria. What should the auditor recommend?
- A library GenAI index includes draft policies never approved for public guidance. What should the auditor emphasize?
- A county emergency AI fuses CAD, weather, and social tips without documented merge rules. What should the auditor recommend?
- A municipal AI team stores training snapshots indefinitely without archival classification. What should the auditor emphasize?
- A building-plan AI lacks controls ensuring only the latest code edition is in the training set. What should the auditor recommend?
- A city permitting AI jumps from demo notebook to production with no defined MLOps stages. What should the auditor recommend?
- A public-health chatbot lacks decommissioning criteria when the vaccine campaign ends. What should the auditor emphasize?
- A transit fare model skips validation environment and trains/tests only in production. What should the auditor recommend?
- A housing AI project has no documented design review against business objectives. What should the auditor recommend?
- A 311 GenAI assistant has no monitoring phase KPIs after deployment. What should the auditor emphasize?
- A court summarizer uses ad-hoc scripts instead of versioned pipelines and model registry. What should the auditor recommend?
- A water anomaly model promotion lacks gated approvals between stages. What should the auditor recommend?
- A school tutoring AI reuses a vendor PoC architecture without adapting to district controls. What should the auditor emphasize?
- A parks computer-vision project has no documented input/output specifications for each lifecycle phase. What should the auditor recommend?
- A municipal AI team cannot show which model version answered a resident complaint last month. What should the auditor emphasize?
- A county emergency AI skips human-in-the-loop design where decisions affect dispatch priority. What should the auditor recommend?
- A library chatbot lifecycle plan ignores dependency updates for the embedding model. What should the auditor emphasize?
- A city AI factory standardizes Agile for apps but has no AI-specific definition-of-done including bias checks. What should the auditor recommend?
- A permitting model is silently retrained weekly with no change tickets. What should the auditor recommend?
- A public-health prompt update to a GenAI bot bypasses the CAB because it is just wording. What should the auditor emphasize?
- A transit AI threshold for delay alerts is tuned live by an engineer without a rollback plan. What should the auditor recommend?
- A housing scoring model feature set changes after a vendor patch with no impact assessment. What should the auditor recommend?
- A 311 chatbot knowledge base publish process has no approval for AI-affecting content changes. What should the auditor emphasize?
- A court AI upgrade coincides with a major rules-of-court revision without coordinated change. What should the auditor recommend?
- A water utility has ITIL change for servers but excludes model registry promotions. What should the auditor emphasize?
- A school district enables a new GenAI plugin district-wide overnight. What should the auditor emphasize?
- A parks vision model A/B test runs in production without documented experimental change controls. What should the auditor recommend?
- A county configuration baseline for AI gateways is missing from the CMDB. What should the auditor emphasize?
- A library embedding model swap invalidates prior evaluation results, but no re-approval is triggered. What should the auditor recommend?
- A city emergency AI runbook change is made during an incident without post-incident formalization. What should the auditor emphasize?
- A municipal vendor pushes continuous model updates with no customer-visible change log. What should the auditor recommend?
- A permitting AI auto-approves low-risk permits with no human review sampling of outputs. What should the auditor emphasize?
- A public-health chatbot gives dosing guidance, and the audit finds no clinical supervision of impacts. What should the auditor recommend?
- A transit delay predictor drifts; ops dashboards show accuracy but not decision impact on riders. What should the auditor emphasize?
- A housing caseworker override of AI scores is never logged or analyzed. What should the auditor recommend?
- A 311 GenAI cites ordinances incorrectly, and no process exists to correct and learn from errors. What should the auditor emphasize?
- A court AI summary is filed without judge acknowledgment that AI assisted. What should the auditor recommend?
- A water anomaly AI pages operators constantly, creating alert fatigue with no supervision of decision quality. What should the auditor emphasize?
- A school tutoring AI steers students toward paid products with no monitoring of recommendation impacts. What should the auditor recommend?
- A parks reservation AI disproportionately denies weekend slots to certain zip codes with no equity monitoring. What should the auditor emphasize?
- A county KPI pack for AI tracks uptime but not model confidence calibration. What should the auditor recommend?
- A library chatbot escalating to humans has no SLA for when AI must defer. What should the auditor emphasize?
- A city emergency AI triage recommendation is accepted by default under time pressure. What should the auditor recommend?
- A municipal board receives AI performance slides with no narrative on residual decision risk. What should the auditor emphasize?
- A permitting chatbot UAT only checks happy-path FAQs, not adversarial or edge ordinance questions. What should the auditor recommend?
- A public-health classifier is tested on last year's strains only; the audit challenges temporal validity tests. What should the auditor emphasize?
- A transit vision model accuracy is reported without confidence intervals or class imbalance analysis. What should the auditor recommend?
- A housing scoring model lacks bias testing across protected demographic segments. What should the auditor emphasize?
- A 311 RAG assistant is never tested for hallucination against a gold-answer set. What should the auditor recommend?
- A court transcription AI WER is measured in quiet rooms, not real courtroom noise. What should the auditor emphasize?
- A city water plant plans to roll an anomaly-detection model to every treatment site after a lab pilot, with no soak or canary period. What should the AI auditor emphasize?
- A school-district tutoring assistant ships prompt and model updates without re-running its regression suite. What should the auditor recommend?
- A parks department computer-vision project uses the same images for both training and final test scoring. What testing weakness should the auditor highlight?
- A county emergency-management AI has accuracy tests but no chaos or failure-mode tests for model-service outages. What should the auditor emphasize?
- A city library GenAI red-team exercise only checks classic jailbreaks and ignores harmful civic misinformation paths. What gap should the auditor cite?
- A municipal building-plan review AI passes accuracy acceptance tests but never checks whether staff can use the AI-generated comments accessibly. What should the auditor recommend?
- A municipal vendor delivers a polished AI test report but no statement on how representative the test data is of the city's population and use cases. What should the auditor conclude?
- A city permitting chatbot can be tricked by crafted prompts into revealing internal staff notes about applications. What AI-specific threat should the auditor emphasize?
- A public-health surveillance model could be skewed if adversaries submit manipulated community symptom reports into the training pipeline. What threat should the auditor highlight?
- A transit authority camera AI can miss plates when adversarial stickers are placed on vehicles. What vulnerability class should the auditor focus on?
- A housing-authority eligibility model API has no rate limits, allowing bulk querying that could support model extraction. What should the auditor recommend?
- A 311 resident assistant sometimes returns retrieval chunks that include nonpublic resident PII in answers. What AI-specific privacy threat should the auditor cite?
- A court clerk AI plugin pipeline automatically pulls an unvetted model weight update from a third-party source. What vulnerability should the auditor emphasize?
- A water-utility SCADA machine-learning agent has standing privileges to write plant setpoints without human confirmation. What AI-related vulnerability should the auditor flag?
- A school-district GenAI tool stores student prompts containing FERPA-protected data in an unsecured vendor log. What threat should the auditor highlight?
- A parks department membership kiosk face model can be fooled by holding up a printed photo. What threat should the auditor emphasize?
- A county IT team downloads open-weight models for internal pilots without integrity verification of the files. What vulnerability should the auditor cite?
- A public library chatbot can be jailbroken into giving harmful DIY chemical advice scraped from open forums. What threat scenario should the auditor evaluate?
- A city emergency GenAI tool summarizes web tip pages and can be steered by malicious content planted on those pages. What threat should the auditor highlight?
- A municipal threat and vulnerability program catalogs traditional application flaws but ignores model drift as a failure pathway. What should the auditor recommend?
- A permitting AI suddenly begins approving prohibited land uses, and the city's incident response runbook has no AI-specific playbook. What should the auditor emphasize?
- A public-health chatbot hallucinates outbreak numbers that spread widely online, and no containment or communications steps are defined. What should the auditor recommend?
- A transit demand-forecasting model outage is handled with a generic application restart checklist that omits model version rollback. What recovery gap should the auditor cite?
- A housing-authority AI bias complaint is routed only to public relations and never to incident response or risk owners. What should the auditor recommend?
- A 311 assistant incident involves suspected leakage via prompt logs, but responders do not preserve prompts and outputs for forensics. What gap should the auditor cite?
- A court AI produces an incorrect citation that causes a filing error, but the city's incident severity matrix has no category for AI decision harm. What should the auditor recommend?
- After a spill, investigators find the water anomaly AI produced false negatives, but the post-incident review never addresses model monitoring gaps. What should the auditor emphasize?
- A school GenAI incident may require notifying parents, but the district IR plan omits education privacy notification steps. What gap should the auditor cite?
- A parks kiosk biometric breach involves model embeddings, yet IR treats the event as a routine password reset. What should the auditor emphasize?
- A county relies on a vendor-hosted AI for resident services, but the contract has no joint incident response coordination clause. What should the auditor recommend?
- After a library chatbot compromise, IR disables the bot but leaves poisoned retrieval documents online. What remediation gap should the auditor cite?
- A city emergency AI incident bridge has network and facilities leads on call but no AI subject-matter expert. What should the auditor recommend?
- Recurring municipal AI incidents are closed in problem management but never feed the city's risk register. What linkage should the auditor recommend?
AI Auditing Tools and Techniques · 42 questions
- A city audit plan treats a new permitting GenAI as a standard web app and skips AI-specific risk assessment in scoping. What should the auditor change?
- A county internal audit team is designing work on an early public-health AI pilot and must choose between assurance and advisory objectives. What planning distinction matters most?
- A transit AI audit plan is drafted without using model inventory completeness or system criticality as planning inputs. What should the auditor require?
- A housing authority AI audit timeline is set without assessing the team's need for statistical or model literacy. What planning gap should the auditor cite?
- A 311 chatbot audit design covers technical uptime but omits equity and resident-impact workstreams. What should the auditor add?
- A court AI audit plan lists fieldwork tasks but maps no criteria to AI policies, standards, or regulations. What should the auditor correct?
- A water utility SCADA machine-learning audit scope excludes the vendor-managed model pipeline with no documented justification. What should the auditor challenge?
- A school district AI audit calendar spreads effort evenly and never prioritizes high-harm student-facing systems first. What planning approach should the auditor recommend?
- A municipal chief audit executive asks whether integrating AI into the audit process itself requires changing how engagements are planned. What is the best response?
- An auditor samples only 25 permitting chatbot chats at random and ignores stratified high-risk permit types. What sampling improvement should the auditor make?
- A public-health model audit measures overall accuracy but does not sample across demographic strata. What testing gap should the auditor cite?
- A transit AI control test relies only on design walkthroughs and collects no operating effectiveness sample. What distinction should the auditor apply?
- A housing AI audit samples human overrides but never samples automated approvals that never see a human. What population flaw should the auditor correct?
- A 311 GenAI test uses only auditor-written prompts that do not reflect real resident phrasing. What testing weakness should the auditor address?
- A court AI audit sample spans a period when the model changed mid-window, muddying conclusions. What sampling consideration should the auditor apply?
- A water anomaly AI audit uses a sample too small to conclude on rare but critical false negatives. What sampling issue should the auditor raise?
- A school district AI audit relies solely on vendor SOC report sampling assertions and performs no local tests. What approach should the auditor take?
- A parks department vision AI audit reuses last year's sample design without reassessing how park-camera usage and incident types changed. What should the auditor do?
- An auditor accepts a permitting AI vendor's marketing slide deck as the sole evidence that model validation was performed. What is the primary concern?
- A public-health AI audit needs to reperform GenAI responses about clinic referrals. Which evidence type best supports that reperformance?
- A transit AI engagement collects staff interviews about monitoring but obtains no system-generated monitoring exports. What should the auditor strengthen?
- A housing authority AI audit cannot obtain training-data samples because the vendor refuses access. What should the auditor pursue next?
- A 311 chatbot audit captures chat UI screenshots but omits model version, prompt template, and retrieval metadata. What completeness issue should be raised?
- A court AI auditor observes live use of a transcript assistant but fails to secure chain-of-custody for exported transcripts used as evidence. What should be corrected?
- A water utility ML audit uses only synthetic test data, and management challenges whether results reflect production risk. How should the auditor respond?
- A municipal auditor uses an AI assistant to draft findings on a permitting AI system and must support the workpapers. What evidence discipline is required?
- An auditor's analytics on permitting AI decisions shows null feature rates spiked just before a poorly performing release. What does this illustrate?
- A public-health AI audit dashboard compares prediction distributions week-over-week to look for drift. What audit use does this support?
- Transit AI sample analytics show systematically longer predicted wait times for one corridor relative to peers with similar demand. What should the auditor conclude about the technique's value?
- A housing AI audit's computer-assisted audit techniques fail because decision logs lack join keys to case files. What prerequisite is missing?
- A 311 GenAI audit mines ticket outcomes against bot answers to measure whether responses were helpful. What analytics design does this exemplify?
- A court AI analytics project draws accuracy conclusions without documented data quality rules on the extract. What should the auditor require first?
- A water utility AI audit deploys continuous auditing scripts that watch model confidence scores for anomalies. What technique is being applied?
- A school-district AI audit open-data extract used for analytics still contains suppressed small-cell values that were supposed to protect student privacy. What should the auditor assess?
- A permitting AI audit report lists technical jargon for council readers but omits residual risk ratings. What reporting improvement is needed?
- A public-health AI engagement issues findings without actionable management action plans. What quality gap exists in the audit output?
- A transit AI report omits disclosure that vendor model internals were inaccessible during testing. What reporting element is missing?
- A housing AI audit rates the system 'satisfactory' even though fairness controls were never tested, and the report logic is weak. What should be challenged?
- A 311 AI advisory memo uses assurance-opinion wording even though the work was consulting-only. What distinction should the auditor enforce?
- A court AI audit follow-up report states monitoring gaps were closed but does not retest whether model monitoring controls now operate. What should be improved?
- A water AI audit dashboard for executives mixes verified KRIs with unaudited vendor marketing metrics. What integrity issue should be raised?
- A municipal chief audit executive uses GenAI to draft portions of an AI audit report and must follow methodology on transparency. What practice is required?
These questions are original practice material and are NOT actual exam questions or brain-dump content. All vendor marks are trademarks of their respective owners. This site is not affiliated with, endorsed by, or sponsored by ISACA.