How to Pass the ISTQB CT-AI Exam: a Study Plan
A practical 4-week plan to pass the ISTQB CT-AI (v2.0) exam: prerequisites, exam format, the topics that trip people up, and exam-day tactics.
To pass the ISTQB CT-AI exam, hold a valid CTFL certificate first, then study the current CT-AI v2.0 syllabus (about 19.5 hours of material), practise ML-metric calculations and generative-AI testing scenarios, and rehearse with full mock exams. The exam is 40 questions, 47 points, a 65% (31-point) pass mark, and 60 minutes (75 minutes if English is not your native language). With a focused four-week plan, that is very achievable.
What you need before you start
CT-AI is a Specialist certification, not an entry point. You must already hold the ISTQB Certified Tester Foundation Level (CTFL) — it is a formal prerequisite to register. ISTQB also recommends at least six months of practical experience in testing, software development, or data science. You do not need to be a programmer, but you should be comfortable with basic testing vocabulary. If you are still deciding whether the certification is right for you, start with our ISTQB CT-AI certification guide.
Know the CT-AI v2.0 exam format
The exam is closed-book and multiple-choice. Learn these numbers cold so nothing surprises you on the day:
40 questions worth 47 points in total (a mix of 1-point and 2-point items).
Pass mark 65% — you need at least 31 of the 47 points.
60 minutes, extended to 75 minutes if you sit the exam in a non-native language.
Questions map to cognitive levels K1–K3, so expect definitions, comparisons, and applied scenarios — not just recall.
v2.0 reorganised the syllabus into seven chapters around data, ML model, and system-level testing, and cut the standalone "Testing with AI" block (now part of CT-GenAI). For a full breakdown, see CT-AI v2.0 vs v1.0: what changed.
A 4-week study plan
Week 1 — Foundations and quality characteristics
Read chapters 1–2 of the syllabus: what makes AI-based systems different (non-determinism, self-learning, the data dependency), and the quality characteristics that matter — flexibility, autonomy, evolution, bias, transparency and safety. Build a one-page glossary in your own words. Anchor unfamiliar terms against the official ISTQB glossary.
Week 2 — ML metrics and the confusion matrix
This is where most people lose points. Learn to read a confusion matrix and compute precision, recall and F1 by hand. Understand the accuracy paradox on imbalanced data, when to favour recall over precision (and vice-versa), and the basics of RMSE vs MAE for regression. Practise five to ten calculation problems until they feel routine.
Week 3 — ML testing levels, GenAI and red teaming
Cover the two ML-specific levels new to v2.0 — input data testing and ML model testing — plus deployment testing (data drift, canary releases, monitoring). Then work through generative-AI content: LLM-specific challenges, prompt injection, hallucination, RAG evaluation (groundedness, faithfulness), and red teaming. Learn the black-box test techniques for AI: metamorphic testing, back-to-back testing, and adversarial examples.
Week 4 — Mock exams and gap-filling
Sit full 40-question mocks under timed conditions. After each attempt, review every question — including the ones you got right — and read the rationale. Log your weak topics and re-read only those syllabus sections. Aim to score 80%+ on mocks consistently before booking the real exam. You can practise with our free ISTQB CT-AI mock exams.
Topics that trip candidates up
Metric maths under time pressure. Two-point calculation questions reward the people who practised precision/recall by hand.
Confusing testing AI with testing using AI. CT-AI is about testing AI systems; the "testing with AI" material moved to CT-GenAI.
Data-related defects. Know target/data leakage, label quality, and bias arising from historical data.
AI-specific techniques. Metamorphic and back-to-back testing exist because AI outputs often have no single expected result.
Exam-day tactics
With 60 minutes for 40 questions you have about 90 seconds each. Answer the easy recall items first, flag calculation questions, and return to them. Never leave a question blank — there is no negative marking. Watch for "select two" multiple-response items and count your selections. Read qualifier words such as least, best, and not carefully; they change the correct answer.
Confirm the current exam rules and syllabus version on the official ISTQB CT-AI certification page before you book. Then treat mock exams as your final rehearsal — original questions with a written rationale for every answer teach far more than memorising a dump.
Frequently asked
Yes. The ISTQB Certified Tester Foundation Level (CTFL) is a formal prerequisite for CT-AI v2.0. You must hold a valid CTFL certificate to register for the exam.
The CT-AI exam has 40 multiple-choice questions worth 47 points in total. You need at least 65% (31 points) to pass, within 60 minutes (75 if English is not your native language).
Accredited v2.0 training is about 19.5 hours of instruction. Most candidates add two to four weeks of self-study and mock exams on top of that.
No. CT-AI is conceptual and requires no programming. You do need to understand ML metrics such as precision, recall and F1, and be able to read a confusion matrix.
Study v2.0. It is the current syllabus (released April 2026); v1.0 is being retired (English exams end 21 April 2027). New exams follow v2.0.
Mock exams are essential for timing and format, but pair them with the official syllabus. Understand the reasoning behind each answer rather than memorising questions.
Part of the ExamCaliber editorial team. Every ExamCaliber question and rationale is written and reviewed by hand against the current syllabus — never scraped from exam dumps.