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ISTQB CT-AI Exam Overview:
| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing |
| Exam Number: | CT-AI |
| Exam Duration: | 60 minutes |
| Exam Format: | Multiple Choice |
| Real Exam Qty: | 40 |
| Certificate Validity Period: | Lifetime (no expiration) |
| Exam Price: | EUR 250 |
| Passing Score: | 65% |
| Related Certifications: | ISTQB CTAL-TTA ISTQB CTFL |
| Available Languages: | English |
| Sample Questions: |  |
| Exam Way: | Online proctored or in-person at authorized testing centers |
| Pre Condition: | ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory |
| Official Syllabus URL: | https://www.istqb.org/certifications/artificial-intelligence-testing-certification |
ISTQB CT-AI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing AI-Based Systems | 20% | - Challenges in Testing AI-Based Systems - Test Levels for AI-Based Systems - Bias and Fairness Considerations - Testing AI-Specific Quality Characteristics |
| Topic 2: AI Test Methods and Techniques | 20% | - Black-Box Testing for AI Systems - Adversarial Testing - Test Oracle Techniques for AI - Metamorphic Testing |
| Topic 3: Basics of AI | 8% | - AI Types and Techniques - Data Concepts (Training, Validation, Test Data) - Machine Learning Types (Supervised, Unsupervised, Reinforcement) - Definitions and Terminology |
| Topic 4: Practical Considerations | 12% | - Test Data Quality and Preparation - Test Environment Setup for AI - Documentation and Reporting - Automation in AI Testing |
| Topic 5: Testing AI-Specific Quality Characteristics | 20% | - Accuracy, Precision, Recall, F1-Score - Robustness Testing - Fairness and Bias Detection - Explainability and Interpretability |
| Topic 6: Neural Networks and Deep Learning | 20% | - Testing Deep Learning Models - Neural Network Architecture Basics - Model Training and Evaluation Metrics - Overfitting and Underfitting |
Common Questions About the ISTQB CT-AI Exam
What is the ISTQB Certified Tester AI Testing exam all about?
The CT-AI exam is the official ISTQB exam behind the ISTQB AI Testing certification, validating the skills measured by the ISTQB Certified Tester AI Testing credential. It sits at the Foundation level of the ISTQB certification program. It also connects to ISTQB CTFL, ISTQB CTAL-TTA, so the knowledge you build here carries over to those tracks as well.
How many questions are on the CT-AI exam, and how much time do I get?
The CT-AI exam contains 40 questions to be completed within 60 minutes. Before exam day, divide the available time by the question count to work out a comfortable per-question pace, and mark any item that eats into it so you can return later instead of getting stuck. Timed sessions in the Getcertkey test engines make that pacing automatic — run at least two full-length mock exams under the clock so time pressure never becomes the reason you drop points.
What score do I need to pass the CT-AI exam, and what does it cost?
The passing score for the CT-AI exam is 65%, and the official registration fee is EUR 250. Retakes are not discounted — every new attempt means paying the full fee again — so it pays to measure yourself before you book. Work through the 162 practice questions on Getcertkey, sit a timed practice test, and schedule your exam only when your scores are consistently comfortable. That simple habit is the cheapest exam strategy there is.
Are there any prerequisites for the CT-AI exam?
ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory Requirements can change when ISTQB revises its certification program, so confirm the current eligibility rules on the official exam page before you register.
Can I try the CT-AI practice questions before I buy?
Yes. Getcertkey provides a free CT-AI PDF demo so you can review the question style and answer quality before purchasing. Every purchase also includes 365 days of free updates — if ISTQB revises the exam during that period, the updated material reaches you at no cost. Once the free-update year ends, you can extend your update service at a 50% discount.
What if I fail the CT-AI exam, and how is my order delivered?
Every ISTQB Certified Tester AI Testing purchase on Getcertkey is covered by a 100% money-back guarantee with clear conditions: if you take the corresponding exam within 60 days of your purchase and do not pass, you can claim a full refund by submitting a scanned copy of your exam enrollment slip and your official score report as a PDF within two days of the exam date; claims are processed within seven days of submission. The guarantee does not apply to exams taken within three days of purchase, to material that was downloaded but never used in an exam attempt, or to free products and expired orders, and the candidate name must match the payer name. If you would rather not take a refund, you can instead exchange your purchase for two free exam preparation products of equal value and keep the update service on your original product.
Delivery is instant: your download is sent to your email within one minute of payment, with no limit on how many computers you may install the material on. If nothing arrives within two hours, check your spam folder and contact customer service for help.
What topics are covered in the CT-AI exam?
The ISTQB Certified Tester AI Testing exam blueprint is organized into 6 domains. The first three are:
- Basics of AI — 8% of the exam
- Testing AI-Based Systems — 20% of the exam
- Neural Networks and Deep Learning — 20% of the exam
For the complete domain-by-domain breakdown, scroll up to the full exam topics outline above and use it to plan how you distribute your study time.
ISTQB Certified Tester AI Testing Sample Questions:
Question #1
Which ONE of the following statements is true about dynamic testing for inappropriate bias?
A. Testing should never be conducted in production
B. It can be necessary to obtain additional attributes about the data being processed
C. Inappropriate bias only needs to be tested when protected characteristics such as race and gender ate present in the inputs
D. Reviewing the source of the training data can reveal inappropriate bias
Question #2
Which of the following options is an example of the concept of overfitting?
Choose ONE option (1 out of 4)
A. A model for predicting IT system failures delivers too many false-negative predictions because the failures cannot be adequately explained via the log files used for training.
B. A previously trained model for recognizing cars is adapted and extended so that it can also identify the make of the car beyond its original function.
C. A model for predicting academic performance was trained with data from students at one university.
The model shows low predictive accuracy when applied to other universities.
D. A model for the recognition of dogs was trained predominantly with pictures of dogs in parks. On pictures with other animals in parks, dogs are also falsely recognized.
Question #3
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer).
A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I.Pairwise testing of combinations
II.Testing each individual model for accuracy
III.A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
A. I and III
B. Only II
C. I and II
D. Only III
Question #4
Which statement about automation bias is correct?
Choose ONE option (1 out of 4)
A. Automation bias is tested with representative users, but human input quality is irrelevant
B. When testing AI-based systems, automation bias does not play a role in supporting test activities such as boundary value analysis
C. Automation bias particularly affects testing of autonomous systems
D. Automation bias affects the testing of AI-based systems that support users in their actions or decisions
Question #5
Which statement about using AI to analyze reported defects is MOST correct?
Choose ONE option (1 out of 4)
A. ML models can support duplicate defect identification when checking defect criticality.
B. ML models trained with critical defect tickets can identify defects that cause serious consequences.
C. ML models identify developers who should handle a defect based on ticket content.
D. ML models can identify categories for a reported defect during assignment.
Solutions:
| Question #1 Correct Answer: B | Question #2 Correct Answer: C | Question #3 Correct Answer: C | Question #4 Correct Answer: D | Question #5 Correct Answer: D |
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