Software testing

ISTQB CT-AI v2.0 Practice Tests: AI Systems Testing

300 questions, four focused banks and two 40-question mocks covering data, GenAI, model testing and deployment

Software TestingAmerican English6 practice tests

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ISTQB CT-AI v2.0 Practice Tests: AI Systems Testing

Overview

What you’ll practice

Questions and explanations: American English

Exam referenceSoftware testing
Course version published
Review official exam information

Check the provider’s current objectives before booking your exam. This is independent practice material.

  • Distinguish AI technologies and evaluate model, hardware and hosting choices against the intended use.
  • Translate AI quality and safety requirements into observable acceptance conditions.
  • Interpret ML workflows, data partitions, preprocessing and neural-network coverage evidence.
  • Calculate and select accuracy, precision, recall and F1 using the correct populations and denominators.
  • Evaluate input-data usefulness, bias, representativeness, constraints and label correctness.
  • Select test oracles and evidence for adaptive, probabilistic and generative AI systems.
  • Choose controlled red-team inputs, adversarial tests and valid metamorphic follow-ups.
  • Interpret drift, fitting, model comparisons and deployment recovery evidence.

More about this course

This course contains the use of artificial intelligence.

An AI system can achieve a good aggregate score and still fail a required subgroup, mishandle a transformed input, leak information through retrieved content, or behave differently after deployment. These practice tests help you decide what the evidence actually establishes and which test addresses the remaining risk.

The course targets the ISTQB Certified Tester AI Testing (CT-AI) v2.0 syllabus. Its focus is testing AI-based systems: their input data, models, generated outputs, surrounding applications and deployment behavior.

A structured practice path

  • Foundations and AI quality — 50 questions: technology choices, quality characteristics, safety and acceptance criteria.
  • ML, metrics and input data — 70 questions: data allocation, preprocessing, confusion matrices, neural coverage, bias, representativeness, constraints and labels.
  • Test strategy and GenAI — 50 questions: adaptive behavior, statistical evidence, oracles, generated-output evaluation, controlled red teaming, test levels and risk.
  • Model and deployment testing — 50 questions: adversarial and metamorphic testing, drift, fitting, A/B and back-to-back comparisons, development tools and deployment recovery.
  • Two mixed mocks — 40 questions each, 60 minutes each: seven-chapter coverage with 36 K2 and four K3 questions in each mock, following the current official exam structure.

Each question includes an answer explanation, feedback on every option and a short study pointer. Scenarios ask you to interpret observations, apply stated constraints, calculate classification metrics and distinguish plausible but insufficient evidence. The questions are original practice material; they are not recalled exam questions.

Use the explanations to improve your decisions

Start with a focused bank in Practice mode, working in sessions of 15–25 questions. Read feedback for wrong and guessed answers, identify the condition you missed, and revisit that syllabus section. Reserve the mixed mocks for a timed check after topic practice. The longer time allowances on focused banks are author-selected study settings.

The official exam uses 40 questions, 44 available points and a 29-point pass requirement. This course uses a 75% equal-weight practice threshold; it is a study target and does not reproduce the official weighted scoring. Results here cannot guarantee an exam pass.

This is a practice-question course. It does not include video lectures or hosted model laboratories. AI assisted the creation of questions, explanations and the cover background. This independently produced course is not affiliated with or endorsed by ISTQB. ISTQB and CT-AI are trademarks of their respective owner.

Practice sets

6 practice tests. 300 questions.

01

Foundations and AI Quality | Focused Practice

50 questions100 minutes

02

ML, Metrics and Input Data | Focused Practice

70 questions140 minutes

03

Test Strategy and GenAI | Focused Practice

50 questions100 minutes

04

Model and Deployment Testing | Focused Practice

50 questions100 minutes

05

Mixed Mock 1 | 40 Questions | 60 Minutes

40 questions60 minutes

06

Mixed Mock 2 | 40 Questions | 60 Minutes

40 questions60 minutes

FREE COURSE PREVIEW

Try it. Then understand it.

Three original single-answer questions from this course. Your answers stay in this page and are cleared when you leave.

Choose one answer1 of 3

A fraud detector learns boundaries from labeled transactions. A tax calculator applies an explicit rate table. Which test-design distinction follows?

Before you begin

  • Familiarity with Foundation Level software testing concepts; CTFL certification is a prerequisite for the official CT-AI exam.
  • Basic arithmetic for confusion-matrix metrics and stated data constraints.
  • Ability to read technical English and access the current CT-AI v2.0 syllabus for further study.

Who this is for

  • Testers preparing for the ISTQB CT-AI v2.0 examination.
  • QA professionals moving into testing AI-based applications, data pipelines and model behavior.
  • Test analysts who want practice interpreting ML and GenAI test evidence.

Questions

Before you take the next step.

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Does this include the official certification exam?

No. This is independent preparation material. Certification registration, exam fees and credentials are managed by the exam provider.

Can I switch the course language?

Check the course language label. Available language editions are listed on each course page and have separate Udemy links. The website language switch changes navigation, not course content.

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