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
Sep 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.
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. An insurance company develops a fraud detection system using historical claims data. During model evaluation, they calculate a confusion matrix showing: True Positives = 450, False Positives = 150, True Negatives = 8,900, False Negatives = 500. Management asks which metric best represents the system's ability to identify fraudulent claims without missing actual fraud cases. Which metric should the team report? (Select one!)
Explanation
Recall (also called sensitivity or true positive rate) measures the proportion of actual fraudulent claims that the system successfully identifies: TP / (TP + FN) = 450 / (450 + 500) = 47.4%. This directly answers management's question about not missing actual fraud cases. High recall means few false negatives, indicating the system catches most fraud. Accuracy would be misleading here due to the highly imbalanced dataset (9,050 legitimate claims vs 950 fraudulent claims); a naive model predicting all claims as legitimate would achieve 90.5% accuracy while catching zero fraud. Precision measures what percentage of fraud predictions are correct (how many flagged claims are actually fraudulent), but does not indicate whether fraud cases are being missed. Specificity measures the ability to correctly identify legitimate claims, which is less critical than catching fraud in this business context where missing fraud is costlier than false alarms.
2. A manufacturing company wants to automate invoice processing where invoices arrive in various formats (PDFs, emails, scanned images) with inconsistent layouts. The system must extract vendor names, amounts, and dates regardless of document structure. Which approach should the company use? (Select one!)
Explanation
AI with NLP and computer vision can handle unstructured data and varying document formats by learning patterns and adapting to different layouts. RPA requires structured data and predefined rules, making it unsuitable for inconsistent invoice formats. RPA bots would break when encountering new layouts or formats. While invoice processing is repetitive, the unstructured nature of the input data requires AI's cognitive capabilities to interpret and extract information from varying formats. Rule-based systems with fixed field positions fail when document layouts change.
3. A recommendation system uses collaborative filtering to suggest movies to users based on viewing patterns of similar users. The system must group users into segments with similar preferences. Which unsupervised learning algorithm is most appropriate? (Select one!)
Explanation
K-Means clustering is ideal for grouping users with similar viewing preferences into segments without predefined labels. It partitions users into k clusters by minimizing the variance within each cluster, identifying users with similar tastes. Since the number of user segments may be determined through business requirements or elbow method analysis, K-Means provides an efficient solution. Logistic regression is supervised classification requiring labeled data and predefined categories. Linear regression predicts continuous values, not group memberships. Principal Component Analysis reduces dimensionality but does not perform clustering—it creates uncorrelated feature combinations but does not assign users to groups.
4. A machine learning team is evaluating a clustering algorithm that has grouped customer data without requiring predefined labels. The algorithm automatically discovered that customers naturally fall into five distinct segments based on purchasing behavior. The team did not specify the number of clusters in advance. Which clustering algorithm was most likely used? (Select one!)
Explanation
Hierarchical clustering is the most likely algorithm because it does not require specifying the number of clusters in advance and builds a dendrogram showing the hierarchical relationship between clusters. The team can then cut the dendrogram at an appropriate level to obtain the desired number of clusters. K-means requires specifying k (the number of clusters) before running the algorithm. DBSCAN also does not require specifying cluster count but is specifically designed for density-based clustering and identifying arbitrary shapes, which is not mentioned in the scenario. PCA is not a clustering algorithm but rather a dimensionality reduction technique. The key distinguishing factor is that hierarchical clustering discovers the natural cluster structure without requiring the number of clusters as input.
5. A computer vision application must identify and separately count each vehicle in parking lot images. Some images contain overlapping cars. Which segmentation approach should the application use? (Select one!)
Explanation
Instance segmentation assigns unique identifiers to each separate object instance, enabling the system to distinguish and count individual vehicles even when they overlap or touch. This provides pixel-level masks for each distinct vehicle. Semantic segmentation would label all vehicle pixels with the same class, making it impossible to separate and count individual cars. Edge detection identifies boundaries but does not provide object-level separation or counting capabilities. Object detection with bounding boxes can locate vehicles but struggles with overlapping objects where boxes would intersect, and does not provide the pixel-precise delineation needed for accurate counting in complex scenarios.
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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