ISACA · AI-Fundamentals
Validates foundational knowledge of artificial intelligence, covering AI concepts, principles, potential uses, essential algorithms and software for AI applications, and AI-associated risks and ethical requirements.
Practice Questions
600
≈ 4 practice exams
Duration
120 minutes
Passing Score
65%
Difficulty
FoundationalLast Updated
Feb 2026
Prepare for the ISACA AI Fundamentals Certificate with practice questions covering AI concepts, machine learning basics, ethics, governance, risk, controls, and common business applications. The content is designed for learners who need a clear foundation before working with AI assurance or governance topics.
Use each explanation to connect terminology with practical controls and organizational responsibilities. That approach is especially useful for ISACA-style questions, where the best answer often reflects risk awareness, accountability, and responsible use rather than technical novelty.
The ISACA Artificial Intelligence Fundamentals Certificate validates foundational knowledge of artificial intelligence, covering core AI concepts, principles, practical applications, essential algorithms, and the risks and ethical considerations that accompany AI adoption. The credential is designed to help professionals navigate the rapidly evolving AI landscape by building a solid understanding of technologies such as machine learning, neural networks, large language models, computer vision, robotic process automation (RPA), and generative AI. It bridges conceptual understanding with applied knowledge, ensuring candidates can identify AI use cases, understand how AI tools and algorithms function, and align AI practices with governance and regulatory frameworks.
As part of ISACA's expanding AI credentialing ecosystem, the AI Fundamentals Certificate serves as a foundational entry point into more advanced ISACA AI credentials, including the Advanced in AI Audit (AAIA) and the Advanced in AI Security Management (AAISM). The certificate is globally recognized and backed by ISACA's reputation as a trusted authority in IT governance, risk, and security — an organization with over 185,000 members across more than 190 countries.
This certificate is well-suited for students, recent graduates, and early-career professionals who are new to AI and want to establish a verifiable baseline of AI knowledge. It is equally valuable for experienced IT professionals, auditors, risk managers, compliance officers, and business analysts who need to understand AI concepts and their organizational implications without necessarily working in a technical AI role.
Professionals seeking to transition into AI-adjacent roles — such as AI governance, IT audit with an AI focus, or risk and compliance in organizations adopting AI — will find this credential a practical starting point. Teams and organizations looking to upskill staff on AI fundamentals and demonstrate collective AI competency to stakeholders will also benefit from this certificate.
There are no formal prerequisites for the ISACA AI Fundamentals Certificate. Registration is open on a continuous basis with no eligibility restrictions, and candidates can schedule their exam as early as 48 hours after payment of registration fees.
While no prior AI or IT experience is required, candidates will benefit from basic familiarity with IT concepts and business processes. ISACA recommends using its official study guide and the self-guided online course — which includes performance-based labs covering topics such as machine learning models, security implementations of AI, and robotic process automation — to build the foundational knowledge needed to pass the exam.
The exam is a computer-based, remotely proctored, multiple-choice assessment consisting of 60 questions, with a time limit of 120 minutes. It is delivered online through ISACA's remote proctoring platform and can be scheduled at any time, providing candidates with scheduling flexibility. No in-person testing center is required.
The passing score is 65% (39 out of 60 questions correct). The exam registration fee is US $120 for ISACA members and US $144 for non-members. Eligibility established at registration is valid for twelve months, and candidates may schedule their testing appointment up to 90 days in advance.
The ISACA AI Fundamentals Certificate positions holders as credibly literate in AI at a time when organizations across every sector are integrating AI into operations, governance, and risk management. It provides a competitive edge for roles such as IT auditor, risk manager, compliance analyst, AI governance consultant, and business analyst — particularly as organizations seek professionals who can evaluate AI systems, identify associated risks, and ensure responsible AI deployment. The certificate also serves as a stepping stone to advanced ISACA AI credentials such as the AAIA (Advanced in AI Audit) and AAISM (Advanced in AI Security Management).
Certified professionals consistently earn salary premiums over non-certified peers. ISACA's research indicates that certified IT professionals earn an average of 15% more, and certified auditors can command 10–20% higher compensation than non-certified counterparts according to industry salary guides. ISACA's broader credentialing community of 185,000+ professionals spans more than 190 countries, providing global recognition and networking value for this foundational AI credential.
5 sample questions with answers and explanations. The full bank has 600 questions, enough for 4 full-length practice exams.
Preview — answers shown1. A computer vision system must distinguish between different instances of the same object class in an image, such as identifying and separately labeling each individual car in a parking lot photograph. Which task and technique is required? (Select one!)
Explanation
Instance segmentation assigning unique identifiers to each individual car is correct because instance segmentation distinguishes between separate instances of the same class by creating unique pixel-level masks for each object. This allows the system to identify car A, car B, car C as distinct entities even though they belong to the same class. Image classification only assigns one label to the whole image. Semantic segmentation labels all pixels belonging to the car class identically without distinguishing individual cars. Object detection provides bounding boxes but not pixel-level segmentation, and typically identifies regions rather than separating touching or overlapping objects of the same class.
2. A European financial services company plans to deploy an AI system that uses machine learning to evaluate mortgage applications and make automated lending decisions with minimal human oversight. Under the EU AI Act that came into force in August 2024, how would this system be classified and what requirements would apply? (Select one!)
Explanation
Under the EU AI Act, AI systems used for creditworthiness evaluation and credit scoring are explicitly classified as high-risk because they significantly impact access to essential services and fundamental rights. High-risk systems must implement comprehensive requirements including risk management systems throughout the lifecycle, robust data governance practices, detailed technical documentation and record-keeping, transparency and explainability standards, accuracy and robustness thresholds, cybersecurity measures, conformity assessments, and EU database registration. Limited-risk classification applies to systems like chatbots requiring only transparency obligations. The system is not prohibited, as the EU AI Act prohibits practices like social scoring by governments and certain biometric uses, not creditworthiness assessment. Minimal-risk classification would apply to applications with negligible impact, which does not describe mortgage lending decisions.
3. A chatbot development team is implementing Natural Language Processing for customer service. The system must identify user intent from queries, extract specific entities like dates and product names, maintain conversation state across multiple exchanges, and generate contextually appropriate responses. Which two components are essential for this conversational AI implementation? (Select two!)
Multiple correct answersExplanation
Intent classification determines what the user wants to accomplish, which is fundamental for routing queries and generating appropriate responses. Dialog management maintains conversation state across multiple turns and controls conversation flow, enabling coherent multi-exchange interactions. These two components form the core of conversational AI systems. While sentiment analysis can enhance chatbot responses, it is not essential for basic functionality. Machine translation is only necessary for multilingual support, not core conversational capability. Topic modeling analyzes document collections for themes rather than enabling real-time conversation.
4. An organization implements AI-powered employee performance evaluation using machine learning models trained on historical performance reviews. The system must comply with the EU AI Act. Which risk category does this AI system fall under, and what is the primary compliance obligation? (Select one!)
Explanation
Employment-related AI systems, including performance evaluation and worker management, are explicitly classified as high-risk under Annex III of the EU AI Act. High-risk systems must undergo conformity assessment, obtain CE marking, register in EU databases, implement risk management systems, maintain technical documentation, and ensure human oversight before deployment. Limited risk systems only require transparency, which is insufficient for employment AI. Minimal risk applies to low-impact applications like AI-enabled video games. The system is not prohibited (unacceptable risk) as emotion recognition prohibitions apply specifically to workplace emotion recognition for non-safety purposes, not performance evaluation based on work output.
5. An AI audit team is reviewing an organization's machine learning development process. They discover that the data science team uses the test dataset multiple times during hyperparameter tuning to compare different model configurations and select the best-performing variant. What fundamental principle violation has occurred? (Select one!)
Explanation
The test set must remain completely isolated until final evaluation to provide an unbiased assessment of model performance on truly unseen data. Using the test set during hyperparameter tuning causes information leakage, allowing indirect optimization on test data and producing overly optimistic performance estimates that will not generalize to production. The validation set should be used for hyperparameter tuning and model selection decisions. Cross-validation technique selection is separate from the test set contamination issue. Insufficient training data and inadequate feature engineering are different problems unrelated to improper test set usage.
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