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
Oct 2026
The ISACA Artificial Intelligence Fundamentals Certificate exam runs 60 multiple-choice questions in 2 hours, delivered online with remote proctoring, and requires 65 percent to pass, which works out to about 39 correct answers. Content splits evenly across two domains: AI Concepts (50 percent) covers machine learning paradigms, neural networks, large language models, generative AI, computer vision, and robotic process automation, while AI Implementations (50 percent) tests essential algorithms, real-world use cases, and the risk, ethics, and governance considerations behind responsible AI deployment. This 600-question practice bank mirrors that even split.
Registration costs $120 for ISACA members and $144 for non-members, is open continuously with no prerequisites, and lets you schedule a testing appointment as early as 48 hours after payment. Because this is an ISACA certificate rather than a certification, there is no three-year renewal cycle or annual maintenance fee of the kind CISA and CISM holders carry, so the goal is simply to pass once. If you miss the 65 percent mark, ISACA's certificate exam policy allows up to three retakes within 12 months of your first attempt, paying the exam fee each time.
AI Fundamentals is also the open door into ISACA's AI credential stack, because every credential above it demands something you may not have yet: Advanced in AI Audit (AAIA) requires an active CISA, CIA, or CPA, Advanced in AI Risk (AAIR) requires CISA, CISM, CRISC, or an equivalent, and Advanced in AI Security Management (AAISM) requires an active CISM or CISSP. If AI audit, risk, or security management is your target, this certificate builds the shared vocabulary those exams assume, and CertCompanion carries practice banks for AAIA, AAIR, and AAISM when you are ready to step up.
Benchmark yourself with the 30 free questions, then work the full 600-question bank in short timed sessions until your accuracy holds above 65 percent in both domains. Read the explanations even on questions you answer correctly: ISACA-style items reward answers that reflect accountability, risk awareness, and responsible use over technical novelty, and that judgment is exactly what the explanations train.
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.
The AI Fundamentals Certificate is a foundational entry point into ISACA's broader AI credentialing ecosystem, which also includes advanced credentials focused on AI audit, security management, and risk.
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. No in-person testing center is required.
The passing score is 65% (39 out of 60 questions). The exam registration fee is US $120 for ISACA members and US $144 for non-members. Eligibility established at registration is valid for six months, appointments are available up to 90 days in advance, and candidates can schedule as early as 48 hours after payment. ISACA allows four attempts within a rolling 12-month period; after a failed first attempt, the waits are 30, 90, and 90 days before subsequent attempts, with the full exam fee due each time.
The ISACA AI Fundamentals Certificate can demonstrate baseline AI literacy for professionals working around technology risk, audit, compliance, security, data, and governance. It is best treated as evidence of foundational knowledge rather than proof of hands-on model-building experience. The certificate can also provide useful preparation vocabulary for ISACA's advanced AI credentials in audit, security management, and risk.
5 sample questions with answers and explanations. The full bank has 600 questions, enough for 4 full-length practice exams.
Preview — answers shown1. An AI audit reveals that a company's machine learning models lack documentation of data origins, transformations, and processing history. Which data governance concept is missing? (Select one!)
Explanation
Data lineage provides complete documentation of data origins, all transformations applied, movement through systems, and processing history throughout the entire lifecycle. This is essential for AI governance, auditing, and regulatory compliance. Data anonymization addresses privacy protection by removing identifiable information. Data encryption addresses security in transit and at rest. Data backup addresses disaster recovery and business continuity, not documentation of data history and transformations.
2. According to COBIT's seven enablers for AI governance, an organization needs to establish clear accountability hierarchies for AI oversight and decision-making. Which enabler does this represent? (Select one!)
Explanation
Organizational Structures is the COBIT enabler that addresses clear accountability hierarchies for AI oversight and decision-making. This enabler defines roles, responsibilities, and reporting structures governing who has authority over AI systems. Processes refer to structured workflows guiding AI activities. Principles, Policies, and Procedures provide documented guidelines for ethical use and transparency. Culture, Ethics, and Behavior focus on organizational values fostering responsible AI adoption. Establishing accountability hierarchies is fundamentally about organizational structure design.
3. According to the NIST AI Risk Management Framework, an organization implementing AI systems must perform four core functions. Which sequence correctly represents the recommended order of these functions? (Select one!)
Explanation
The NIST AI RMF specifies Govern as the foundational function establishing accountability and culture, followed by Map to identify context and risks, then Measure to assess and track risks quantitatively, and finally Manage to allocate resources and implement risk treatments. Governance must be established first to provide the framework for all subsequent risk management activities. Starting with Map or Measure without governance foundations would lack proper organizational accountability and oversight structures.
4. A text classification model must assign news articles to predefined categories. The output layer must produce a probability distribution where all class probabilities sum to 1.0. Which activation function should be used in the output layer? (Select one!)
Explanation
Softmax function is correct because it is specifically designed for multi-class classification problems and produces a probability distribution where all output values are between 0 and 1 and sum to exactly 1.0. Softmax transforms raw output scores into normalized probabilities across all classes. ReLU is used in hidden layers, not output layers, and outputs can exceed 1.0. Sigmoid outputs values between 0 and 1 but is designed for binary classification, and multiple sigmoid outputs do not sum to 1.0. Tanh outputs values between -1 and 1 and does not produce probability distributions.
5. A multinational corporation evaluates AI governance frameworks and needs to comply with international standards adopted by 47 countries including all G20 nations. Which framework should they prioritize? (Select one!)
Explanation
The OECD AI Principles have been adopted by 47 countries including all OECD members and have been incorporated into G20 declarations, making them the most internationally recognized framework. They provide five value-based principles for trustworthy AI. The NIST AI RMF is US-focused voluntary guidance, not internationally adopted. The EU AI Act is binding law within the EU but not adopted by 47 countries globally. Canada's Directive applies specifically to Canadian federal government systems.
US $120 for ISACA members and $144 for non-members. Registration is open continuously, and you can schedule a remotely proctored testing appointment as early as 48 hours after payment.
60 multiple-choice questions with a 2-hour time limit. The exam is computer-based and delivered online with remote proctoring, so no test-center visit is required.
65 percent, which works out to roughly 39 of 60 questions. It is a straight percentage threshold, not a scaled score like ISACA uses for its advanced certifications.
No. Like ISACA's other fundamentals certificates, it is earned once, with no CPE renewal cycle or annual maintenance fee. That separates it from ISACA certifications such as CISA and CISM, which must be renewed every three years with ongoing CPE.
None. Anyone can register at any time. That makes it the entry point into ISACA's AI credentials, since the advanced tier all requires prior certifications: AAIA needs CISA, CIA, or CPA, AAIR needs CISA, CISM, CRISC, or equivalent, and AAISM needs an active CISM or CISSP.
Two equally weighted domains: AI Concepts (50%), spanning machine learning, neural networks, LLMs, generative AI, computer vision, and RPA, and AI Implementations (50%), covering algorithms, use cases, and AI risk, ethics, and governance.
It depends on your track. Auditors move toward AAIA (Advanced in AI Audit), risk professionals toward AAIR (Advanced in AI Risk), and security managers toward AAISM (Advanced in AI Security Management). All three require an existing credential such as CISA, CISM, CRISC, or CISSP, so many candidates pair AI Fundamentals with one of those core certifications first.
No. Dumps recycle unverified and outdated content, and using leaked exam material violates ISACA's exam candidate agreement. Practice questions with explanations that connect AI terminology to governance and risk judgment prepare you for how ISACA actually frames its questions.
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