CT-AI Reliable Real Exam - CT-AI Sample Questions Pdf

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ISTQB CT-AI Exam Overview:

Certification Vendor:	ISTQB
Exam Name:	ISTQB Certified Tester - AI Testing Exam
Exam Number:	CT-AI
Exam Format:	Multiple-choice questions, 1-2 points per question
Exam Duration:	60 (75 for non-native language)
Exam Price:	€180 - €250 (varies by region and provider)
Passing Score:	65% (29/44 points for v2.0; 31/47 points for v1.0)
Real Exam Qty:	40
Related Certifications:	ISTQB Certified Tester Testing with Generative AI (CT-GenAI) ISTQB Certified Tester Foundation Level (CTFL)
Available Languages:	Spanish, Chinese, English, Korean, Portuguese, Japanese, French, German
Certificate Validity Period:	Valid indefinitely (no expiration)
Recommended Training:	CT-AI Syllabus v2.0 ISTQB Accredited Training Providers
Exam Registration:	iSQI Exam Registration Pearson VUE ISTQB Official Registration
Sample Questions:	ISTQB CT-AI Sample Questions
Exam Way:	Online remote proctored / Onsite test center
Pre Condition:	Must hold ISTQB Certified Tester Foundation Level (CTFL) certification
Official Syllabus URL:	https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 2	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 3	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 4	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 5	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 6	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 7	systems from those required for conventional systems.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which two test procedures are BEST suited for CleverPropose system testing?
Choose TWO options (2 out of 5)

A. Adversarial testing
B. Exploratory data analysis
C. Pairwise testing
D. Back-to-back testing
E. Metamorphic testing

Answer: D,E

Explanation:
The ISTQB CT-AI syllabus explains that AI-based decision-support systems benefit strongly fromback-to- back testingandmetamorphic testingwhen oracle problems exist or when limited regression tests are available. In this scenario, CleverPropose replaces an older advisory system.Back-to-back testing(Option A) is ideal because the outputs of the existing conventional system can serve as areference, enabling comparison against the new AI system. This is exactly what the syllabus recommends when AI is replacing a traditional deterministic system.
Metamorphic testing(Option C) is also appropriate, as stated in Section4.6 - Metamorphic Relations. With limited regression tests and complex decision logic, testers can define metamorphic relations such as "if customer income increases, risk rating should not worsen." These relations allow validation even when exact expected outputs are unavailable.
Exploratory data analysis (Option D) is not a system testing technique. Pairwise testing (Option E) is not well suited for complex AI-based financial advice systems. Adversarial testing (Option B) is more relevant for security-critical or robustness evaluation, not primary system testing for advisory tools.
Thus,A and Care the correct and syllabus-supported choices.

NEW QUESTION # 14
Which challenge to testing self-learning systems puts you at risk of a data attack?

A. Inadequate specification of the operating environment
B. Unexpected changes
C. Complex test environment
D. Insufficient testing time

Answer: B

Explanation:
The ISTQB CT-AI syllabus describes thatself-learning systems continuously adjust their behaviorduring operation as new data arrives. Section4.1 - Challenges of Testing AI-Based Systemshighlights that such systems are vulnerable todata attacks, particularly through adversarial inputs, poisoning, or malicious drift. The risk arises because unexpected changes in the input distribution may alter the learned model in harmful ways. Option D - Unexpected changes corresponds directly to this syllabus-defined risk.

NEW QUESTION # 15
A beer company is trying to understand how much recognition its logo has in the market. It plans to do that by monitoring images on various social media platforms using a pre-trained neural network for logo detection. This particular model has been trained by looking for words, as well as matching colors on social media images. The company logo has a big word across the middle with a bold blue and magenta border. Which associated risk is most likely to occur when using this pre-trained model?

A. Improper data preparation
B. There is no risk, as the model has already been trained
C. Insufficient function; the model was not trained to check for colors or words
D. Inherited bias: the model could have inherited unknown defects

Answer: D

Explanation:
According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case.
Specifically, the syllabus states:
"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process."

NEW QUESTION # 16
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.
SELECT ONE OPTION

A. Testing for security
B. Testing for bias
C. Testing for accuracy
D. Testing for concept drift

Answer: D

Explanation:
* Type of Testing for Stock-Price Prediction Models: Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive losses in coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 7.6 Testing for Concept Drift, which explains the need to test for concept drift in models that might be affected by changing external factors.

NEW QUESTION # 17
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?

A. Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate
B. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data
C. Run the test several times to ensure that the AI always returns the same correct test result
D. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data

Answer: A

Explanation:
The syllabus states:
"When testing probabilistic and non-deterministic systems, the same input may produce different outputs.
Tests need to be run several times to produce statistically valid test results, ensuring that an appropriate number of answers are accurate." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.4, page 58 of 99)

NEW QUESTION # 18
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