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CT-AI: Testing Machine Learning and Generative AI Systems

CT-AI is now on version 2.0, and that version change materially reshapes what candidates should study. ISTQB’s current Certified Tester AI Testing certification focuses on testing AI-based systems: data, machine-learning models, neural networks, AI-specific quality characteristics, test levels for ML systems, and generative-AI behavior. The old v1.0 remains temporarily available, but its English exams and training end on April 21, 2027 and non-English availability ends on October 21, 2027.

Version 2.0 contains 40 questions worth 44 points, requires 29 points to pass, and has a 60-minute base duration. ISTQB Foundation Level remains a prerequisite. The most important preparation decision is therefore version control: candidates starting now should study v2.0 unless their exam provider has explicitly scheduled them on the retiring v1.0 route. The ISTQB certification family lists both versions separately during the transition.

Version 2.0 narrows the scope to testing AI-based systems

The first CT-AI syllabus mixed two directions: testing systems that contain AI and using AI to perform testing. Version 2.0 deliberately focuses on the first direction. Topics about AI-assisted test-case generation, defect prediction, or using AI to optimize regression suites have moved out of the core CT-AI scope. That makes the new syllabus more coherent around the lifecycle and quality risks of AI-enabled products.

The distinction also prevents confusion with CT-GenAI, which teaches how generative AI and large language models can support testing work. CT-AI asks, “How do we test an AI system?” CT-GenAI asks, “How do we use generative AI in the testing process?” A candidate who keeps that boundary clear will find both certification maps easier to understand.

AI quality characteristics require acceptance criteria beyond accuracy

Traditional software can often be judged against deterministic outputs. AI systems may be probabilistic, data-dependent, and sensitive to distributions that change over time. CT-AI v2.0 therefore includes AI-specific quality characteristics and acceptance criteria. Model accuracy can matter, but so can robustness, fairness, explainability, privacy, safety, transparency, and the way performance varies across meaningful subgroups or operating conditions.

Good criteria need to be measurable and connected to risk. A global accuracy number can hide unacceptable behavior for a small but important population. A model may perform well on a benchmark yet fail when inputs drift away from the training distribution. Testing must therefore examine not only “does the model score highly?” but “does it behave acceptably for the decisions this product is allowed to make?”

Data is part of the executable behavior of a machine-learning system

In conventional software, test data drives the code. In machine learning, data also shapes the model itself. CT-AI v2.0 includes data for machine learning and input-data testing because missing values, class imbalance, leakage, labeling errors, unrepresentative samples, duplicates, or biased data can create defects that look like model problems later.

Testers need to ask where data came from, how it was transformed, which populations it represents, and whether training, validation, test, and production data are being kept conceptually separate. The integrity of machine-learning pipelines also matters because data and model artifacts move through systems that can introduce security, provenance, or reproducibility risks. The test strategy should treat the data pipeline as part of the product, not as an invisible preprocessing step.

Data leakage is a particularly important source of misleading confidence. Leakage occurs when training or evaluation data contains information that would not legitimately be available when the model makes a real prediction, or when test data has influenced model development. The resulting metric can look excellent while production performance is poor. Candidates should understand why dataset separation and disciplined evaluation are quality controls, not merely data-science conventions.

The test strategy must also consider hostile or simply unusual data. Poisoned training data can alter learned behavior, while adversarial inputs can exploit model sensitivity at inference time. Even without an attacker, a small change in lighting, wording, sensor quality, class distribution, or upstream preprocessing can shift results. Testing should therefore include data-quality controls, representative boundary cases, robustness experiments, and explicit monitoring assumptions. The important question is not whether the model performs well on one frozen validation set, but whether its behavior remains acceptable when the conditions around the data change.

Model testing depends on metrics that match the decision problem

CT-AI expects candidates to calculate and interpret machine-learning functional-performance metrics for classification. That requires more than knowing metric names. Accuracy, precision, recall, F-scores, confusion matrices, and related measures emphasize different types of error. Their usefulness depends on the business consequence of false positives and false negatives and on the prevalence of the classes being predicted.

A fraud-detection model and a recommendation model may both be classifiers, but the cost of an error is not comparable. Candidates should practice translating a scenario into the metric that best exposes the risk. They should also recognize that one metric rarely provides a complete picture. Thresholds, class balance, subgroup performance, and changes over time can all affect interpretation.

Neural networks add opacity and sensitivity to the test problem

The syllabus introduces neural-network concepts because modern AI systems often contain behavior that cannot be traced through a small set of human-readable rules. Testers do not need to become research scientists, but they do need enough understanding to reason about training behavior, inputs, outputs, model structure, and why apparently small changes can produce different predictions.

This changes test design. Exact expected outputs may not always be appropriate, so testers may use invariants, tolerances, metamorphic relationships, statistical checks, adversarial inputs, or comparisons against reference behavior. The objective is still evidence, but the oracle problem is different from a deterministic calculation where one input always has one correct output.

Generative AI and LLMs bring non-determinism into ordinary product testing

Version 2.0 explicitly covers testing generative AI and large language models. These systems can produce fluent responses that are factually wrong, insecure, biased, inconsistent, or poorly grounded. A test plan therefore needs more than a handful of “good prompts.” It should consider task success, harmful outputs, prompt sensitivity, hallucination, retrieval quality, policy compliance, privacy, jailbreak resistance, and behavior across different contexts.

Exploratory testing and red-team-style thinking become especially useful because failures may emerge from combinations the development team did not enumerate. At the same time, results need structure: prompts, model versions, parameters, retrieval sources, and evaluation criteria should be recorded well enough to reproduce and compare findings. Non-determinism makes evidence discipline more important, not less.

For LLM-based features, an expected result may be a property rather than one exact sentence. A tester may need to evaluate groundedness, refusal behavior, relevance, format compliance, toxicity, privacy leakage, and consistency across repeated runs. Test oracles can combine deterministic checks, reference facts, rubric-based evaluation, human review, and carefully governed model-assisted scoring. Because each oracle has limitations, strong evidence comes from using several complementary methods and recording enough context—model version, prompt, parameters, retrieval state, and test data—to make an unexpected outcome investigable.

ML systems introduce test levels that do not map neatly to classic software

CT-AI v2.0 highlights test levels specific to machine-learning systems and separates input-data testing, model testing, and ML development testing. This helps candidates reason about where a defect originates. A poor outcome could come from corrupt input data, a weak model, a transformation bug, an integration problem, or a mismatch between the trained model and the production environment.

Testing at multiple levels makes diagnosis easier. Data checks can fail before model execution. Model-level evaluation can distinguish ML quality from application integration. End-to-end testing can then examine the full user or business outcome. Candidates should be able to explain why relying only on system-level tests makes AI defects difficult to localize.

AI systems also complicate the test oracle. For generated text, recommendations, rankings, or probabilistic predictions, there may be many acceptable outputs rather than one exact answer. Teams may need reference sets, rubric-based evaluation, statistical thresholds, metamorphic relationships, human assessment, or model-based evaluators with their own controls. The important testing question becomes whether the oracle is reliable enough to support the risk decision, not whether every output can be compared with a single golden string.

Reproducibility and environment control are essential testability concerns

AI development introduces many versioned artifacts: datasets, feature transformations, model code, hyperparameters, checkpoints, prompts, retrieval indexes, evaluation sets, and serving configurations. If those artifacts are not traceable, a team may be unable to explain why a result changed or to reproduce a failure discovered in testing.

Testers should therefore think about observability and configuration control as part of the quality strategy. A defect report for an AI system may need the input, model version, data version, inference settings, environment, and relevant context. This is not administrative overhead; it is what turns a surprising output into evidence that engineering can investigate.

Study v2.0 with hands-on experiments, not only definitions

ISTQB explicitly gives version 2.0 a stronger practical orientation. Candidates should work through simple machine-learning examples, inspect confusion matrices, change thresholds, test data quality, compare outputs across input changes, and experiment with generative-AI behavior. The ISTQB Foundation Level prerequisite supplies the testing vocabulary, but AI-specific reasoning develops through observation and experimentation.

Preparation should also preserve the version boundary. Do not spend large amounts of time on v1.0 material about using AI to generate tests if the target is v2.0; that direction now belongs more naturally to CT-GenAI. Build a study map around data, model quality, AI-specific acceptance criteria, ML test levels, and modern generative-AI risks. That produces a cleaner understanding of what CT-AI v2.0 is designed to certify.

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