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The current CT-AI exam is based on ISTQB Certified Tester AI Testing version 2.0, released in 2026. The update is substantial. Version 2.0 reorganizes the qualification around the machine-learning lifecycle, with dedicated attention to input data, model behavior, AI-specific quality characteristics, and system-level testing. It also adds explicit treatment of generative AI and large language models while removing the older “using AI for testing” material from the core scope.
The version 2.0 exam keeps a 40-question, 60-minute format. The May 2026 ISTQB exam-structure table lists 44 possible points and a passing score of 29. CTFL remains the prerequisite. Candidates already deep into version 1.0 preparation still have a transition window: English v1.0 training and exams are available until April 21, 2027, and non-English versions until October 21, 2027. Existing v1.0 certificates remain valid.
The scope boundary with CT-GenAI is especially important. CT-AI is about testing AI-based systems, including ML and GenAI systems. CT-GenAI is about applying generative AI to software testing work. Keeping those two directions separate prevents a large amount of study confusion.
Traditional software often supports crisp expectations: for a given input, the correct output is known. AI systems may produce probabilistic or non-deterministic results, and acceptable behavior can be statistical rather than exact. The tester therefore needs ways to reason about distributions, error rates, confidence, thresholds, and variability instead of assuming one deterministic oracle.
This changes how failures are interpreted. A single surprising prediction may be an expected outlier, a data problem, a model weakness, or evidence of an unacceptable risk. Tests need enough repetitions and representative conditions to support a conclusion. The testing strategy must also define which kinds of errors matter most because false positives and false negatives can have very different consequences.
Machine-learning behavior is shaped by the data used to train, validate, and operate the model. Data testing therefore examines completeness, correctness, representativeness, labeling quality, leakage, bias, transformations, schema changes, and pipeline integrity. A model can be implemented perfectly and still perform poorly because the data process is wrong.
Understanding how teams build and train machine-learning models helps testers place those checks at the right points. Training data, validation data, test data, and production input streams serve different purposes. Testers should be alert to overlap that inflates evaluation results, transformations that behave differently in production, and slices of the population that are too rare to influence aggregate metrics but important enough to deserve explicit testing.
Accuracy is often an inadequate summary. Classification systems may require precision, recall, F-scores, ROC-related measures, or class-specific error analysis. Regression models need measures suited to the scale and distribution of the target. The tester needs to understand what each metric reveals and, just as importantly, what it can hide.
A fraud model that misses rare but expensive cases can have attractive aggregate accuracy while failing the business purpose. A medical triage model may treat false negatives as far more serious than false positives. Testing becomes meaningful when thresholds and evaluation measures are chosen in light of those consequences rather than selected because they are common in model-development tooling.
CT-AI v2.0 aligns with emerging quality models such as ISO/IEC 25059. Characteristics such as transparency, explainability, robustness, controllability, and fairness influence whether an AI system is acceptable even when its headline model metric looks strong. These qualities are often contextual and may require a combination of quantitative and qualitative evidence.
For example, explainability may be required so a human can understand why a high-impact decision was made. Robustness may involve behavior under perturbation, distribution shift, or adversarial input. Fairness can require comparing performance across relevant groups rather than evaluating only the overall population. The tester needs to identify which characteristic matters, how it can be observed, and what threshold or acceptance argument is appropriate.
A locked model does not change its learned parameters during operation, so a tested artifact can be tied to a specific version. An adaptive system changes after deployment, which complicates traceability and can invalidate earlier evidence. Testing must account for the mechanism of change, the data that drives it, the controls around updates, and how the organization detects degradation.
Even a locked model can experience changing real-world conditions. Concept drift and data drift can reduce performance without any code change. Monitoring is therefore part of assurance. Production metrics, drift indicators, feedback quality, model versioning, and rollback mechanisms help connect pre-release testing to continued operational confidence.
Large language models can produce fluent but incorrect output, expose sensitive information, follow malicious instructions, or respond inconsistently to small prompt changes. Version 2.0 explicitly includes testing of generative AI and techniques such as exploratory testing and red teaming. These approaches are valuable because not every risky behavior can be captured by a fixed set of expected answers.
A GenAI test strategy can include task-quality rubrics, factuality checks, safety-policy challenges, prompt-injection scenarios, context-boundary tests, privacy probes, and repeatability analysis. The important point is to define the intended use and unacceptable behavior first. Without that context, a large collection of clever prompts produces anecdotes rather than evidence about product risk.
Testing can occur at several levels: data processing components, model-training code, the trained model, serving infrastructure, and the complete AI-enabled product. Problems that look like “model errors” may actually come from preprocessing, feature calculation, retrieval, post-processing, permissions, or user-interface logic. The strategy needs enough separation to identify which layer failed.
Back-to-back testing, metamorphic testing, adversarial testing, A/B testing, and statistical comparisons each answer different questions. Selection should be driven by the test objective. A technique is useful when it exposes a failure mode or supports a decision, not because it appears in a catalog of AI-testing methods.
CT-AI asks how to test systems that contain AI. CT-GenAI, by contrast, examines how generative AI can assist testing activities such as analysis, design, automation, reporting, and improvement. A tester may need both skill sets, but the risks are different: one concerns the quality of an AI product; the other concerns the quality and governance of AI-assisted testing work.
The separation also clarifies why CT-AI v2.0 removed the old “AI for testing” content. The new syllabus can go deeper into AI-system behavior, data quality, model testing, and GenAI-system risks without trying to cover productivity use cases at the same time.
A useful path starts with Foundation Level testing concepts, then traces one AI feature from data acquisition through model training, evaluation, integration, deployment, and monitoring. At each stage, identify the failure modes, evidence, quality characteristics, and test techniques that make sense. The ISTQB family provides the scheme context, but CT-AI becomes practical only when those ideas are connected to an actual lifecycle.
Candidates should practice interpreting metrics, recognizing data leakage and bias risks, distinguishing model from system defects, reasoning about probabilistic outcomes, and designing tests for generative behavior. Version 2.0 is less about memorizing AI vocabulary and more about understanding how testing changes when data and learned behavior become first-class parts of the software system.
When reviewing an AI feature, candidates should also ask what would trigger retesting. A new training dataset, feature pipeline, model family, threshold, prompt template, retrieval source, or serving component can change behavior even if the user interface is untouched. Defining those change triggers is part of maintaining trustworthy evidence across an AI lifecycle and prevents teams from relying indefinitely on results produced by an older model configuration.
AI systems often lack simple test oracles. For a deterministic calculation, an expected value can be stated exactly; for classification, ranking, generation, or recommendation, acceptable behavior may need statistical thresholds, reference sets, expert judgment, invariants, or metamorphic relations. A useful test design states which oracle is being used and why it is credible. Otherwise teams can collect large quantities of outputs without a defensible rule for deciding whether observed variation represents normal uncertainty or a quality failure.
Robustness testing should probe more than average-case accuracy. Distribution shifts, malformed inputs, adversarial perturbations, rare subgroups, missing values, unusual combinations, and boundary conditions can reveal brittle learned behavior. The purpose is not to prove that an AI component is immune to every unexpected input. It is to discover where performance degrades sharply, whether the system fails safely, and whether controls such as confidence thresholds, fallback logic, human escalation, or input validation behave as intended.
Operational observability closes the gap between model evaluation and product assurance. Teams need enough telemetry to identify which model and data versions produced an outcome, detect drift, monitor quality indicators, investigate harmful behavior, and correlate AI failures with surrounding software or infrastructure. Logging must be designed with privacy and security in mind, especially when prompts or features contain personal or sensitive data. Good observability makes production incidents diagnosable without turning monitoring into an uncontrolled data-collection system.
AI assurance also benefits from explicit escalation paths. Some outputs can be handled automatically, while others should be routed to a human when confidence is low, consequences are high, or the input falls outside the domain represented in training and evaluation data. Tests should exercise those boundaries deliberately. A well-designed fallback is part of system behavior, so evidence about when it activates and what the human receives belongs in the overall AI test strategy.
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