For most IT workers, wanting the CT-AI certification is an ordinary ambition. GuideTorrent turns that ambition into a plan with 162 practice questions for ISTQB Certified Tester AI Testing, mapped to the current objectives.
ISTQB CT-AI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Test Levels and Machine Learning Systems | 15% | - Test strategies for AI projects - Unit, integration, system, and acceptance testing for ML - Testing across ML lifecycle stages |
| Topic 2: Input Data Testing for Machine Learning Systems | 20% | - Testing for bias, representativeness, and completeness - Label verification and ground truth assessment - Data quality attributes - Validation of data pipelines and preprocessing |
| Topic 3: Testing Generative AI and Large Language Models | 15% | - Specific risks and test approaches for LLMs - Evaluation of generative AI outputs - Generative AI fundamentals |
| Topic 4: Introduction to Testing AI-Based Systems | 10% | - AI-specific quality characteristics - Challenges in testing AI-based systems - Characteristics of AI and ML systems |
| Topic 5: Machine Learning Development Testing | 10% | - Regression testing for retrained models - Testing ML development workflows - Testing MLOps and deployment pipelines |
| Topic 6: Test Approaches and Techniques for AI Systems | 10% | - Exploratory testing and red teaming - Test environment and tool considerations - A/B testing and back-to-back testing - Risk-based testing |
| Topic 7: Model Testing for Machine Learning Systems | 20% | - Metamorphic and statistical testing - Adversarial and robustness testing - Explainability and transparency testing - Drift detection and monitoring - Model performance and accuracy testing |
Common Questions About ISTQB Certified Tester AI Testing
Must hold ISTQB Certified Tester Foundation Level (CTFL) certification
ISTQB Official Registration: register for CT-AI
iSQI Exam Registration: register for CT-AI
Pearson VUE: register for CT-AI
The ISTQB Certified Tester AI Testing bank is maintained by a team of IT specialists who research the CT-AI questions in depth and organize the verified answers into an effective study sequence. Their ongoing work is what keeps the material professional rather than generic.
- Testing Generative AI and Large Language Models (15%)
- Test Levels and Machine Learning Systems (15%)
- Input Data Testing for Machine Learning Systems (20%)
Let the topic weights guide how you divide your study hours.
The online engine simulates the formal CT-AI test, lets you set your own time limit, and points out your mistakes after each session. Because it runs in a browser, you can practice anywhere and anytime, whether you are waiting for someone or sitting on a bus, and make real use of spare minutes.
The package includes 162 practice questions for the CT-AI exam, all verified by our IT specialists. A free demo is available so you can review the style before purchasing.
ISTQB Accredited Training Providers: Visit official page
CT-AI Syllabus v2.0: Visit official page
The passing score for CT-AI is 65% (29/44 points for v2.0; 31/47 points for v1.0), and the official exam fee is €180 - €250 (varies by region and provider). Keeping these figures in view helps you set a realistic target for your practice sessions.
You will face 40 questions in 60 (75 for non-native language) minutes on the real CT-AI exam. Rehearsing under the same limits on the GuideTorrent engine turns time management into a trained reflex.
ISTQB Certified Tester AI Testing Sample Questions:
"BioSearch" is creating an Al model used for predicting cancer occurrence via examining X-Ray images. The accuracy of the model in isolation has been found to be good. However, the users of the model started complaining of the poor quality of results, especially inability to detect real cancer cases, when put to practice in the diagnosis lab, leading to stopping of the usage of the model.
A testing expert was called in to find the deficiencies in the test planning which led to the above scenario.
Which ONE of the following options would you expect to MOST likely be the reason to be discovered by the test expert?
SELECT ONE OPTION
- A. The input data has not been tested for quality prior to use for testing.
- B. A lack of focus on choosing the right functional-performance metrics.
- C. A lack of similarity between the training and testing data.
- D. A lack of focus on non-functional requirements testing.
Correct Answer: C 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION
- A. Clustering requires you to know the classes.
- B. Clustering is done without prior knowledge of output classes.
- C. Clustering is supervised learning.
- D. Clustering is classification of a continuous quantity.
Correct Answer: B 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options.
When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market, the only response from the automated system is, "I don't understand your question." This AI system should be categorized as?
- A. Super AI
- B. General AI
- C. Conventional AI
- D. Narrow AI
Correct Answer: C 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION
- A. The quality of the labeling
- B. Biased data
- C. The number of classes
- D. The data pipeline
Correct Answer: C 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real- world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
SELECT ONE OPTION
- A. The difficulty of defining criteria for improvement before the model can be accepted.
- B. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.
- C. The fast pace of change did not allow sufficient time for testing.
- D. There was an algorithmic bias in the Al system.
Correct Answer: A 🗳️
Explanation: Only visible for GuideTorrent members. You can sign-up / login (it's free).
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