ISTQB CT-AI Reliable Exam Topics | Valid CT-AI Test Prep

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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	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 3	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 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	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 6	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 7	systems from those required for conventional systems.
Topic 8	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 9	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 10	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 11	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

>> ISTQB CT-AI Reliable Exam Topics <<

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q130-Q135):

NEW QUESTION # 130
The activation value output for a neuron in a neural network is obtained by applying computation to the neuron.
Which ONE of the following options BEST describes the inputs used to compute the activation value?

A. Individual bias at the neuron level, and activation values of neurons in the previous layer.
B. Individual bias at the neuron level, activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.
C. Individual bias at the neuron level, and weights assigned to the connections between the neurons.
D. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons.

Answer: B

Explanation:
Option A correctly identifies all components involved in computing the activation value: the individual bias, the activation values of the previous layer, and the weights of the connections.

NEW QUESTION # 131
Which challenge to testing self-learning systems puts you at risk of a data attack?
Choose ONE option (1 out of 4)

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

Answer: D

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. OptionD - Unexpected changescorresponds directly to this syllabus-defined risk.
Option A refers to system specification issues but does not relate to data attacks. Option B discusses environment complexity, which makes testing difficult but is not tied to adversarial threats. Option C (insufficient testing time) affects quality but does not specifically increase vulnerability to malicious data manipulation.
Unexpected changes-including data drift, poisoned samples, or maliciously constructed training data-pose the greatest risk. When a self-learning system adapts to altered data patterns, it may unknowingly learn incorrect associations, causing model degradation or manipulation. Therefore,Option Dcorrectly identifies the challenge that increases exposure to data attacks.

NEW QUESTION # 132
Which characteristic of AI-based systems makes it difficult to ensure they are safe (e.g., not harming humans)?
Choose ONE option (1 out of 4)

A. Interpretability
B. Determinism
C. Complexity
D. Robustness

Answer: C

Explanation:
The ISTQB CT-AI syllabus lists several characteristics that make it difficult to ensure safety in AI-based systems. Section2.8 - Safety and AIexplicitly names the characteristics that complicate safety assurance:
complexity,non-determinism,probabilistic behavior,self-learning,lack of transparency, andlack of robustness.
Among these,complexityis a core challenge because modern AI systems-particularly those using deep learning-have highly non-linear behavior, large numbers of parameters, and intricate interactions that are hard to predict.
Option B (Complexity) directly aligns with the syllabus and is therefore correct.
Option A (Determinism) is the opposite of AI behavior; AI is oftennon-deterministic, and determinism doesnotmake systems unsafe. Option C (Interpretability) does impact trust and explainability, but the syllabus positions it as a transparency challenge, not the primary difficulty in ensuring safety. Option D (Robustness) is a desired quality, not a reason safety is hard; alackof robustness would be a challenge, not robustness itself.
Thus,complexitybest reflects the syllabus' explicit safety-related difficulty.

NEW QUESTION # 133
Which of the following is a dataset issue that can be resolved using pre-processing?

A. Insufficient data
B. Wanted outliers
C. Invalid data
D. Numbers stored as strings

Answer: D

Explanation:
The syllabus describes that data pre-processing includes cleaning (e.g., fixing or removing invalid data) and transforming data (e.g., changing data types such as numbers stored as strings).
"Transformation: The format of the given data is changed... converting categorical data into numerical data, changing image formats..." (Reference: ISTQB CT-AI Syllabus v1.0, Section 4.1.1, Page 34 of 99)

NEW QUESTION # 134
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

A. Selecting training data by purposely excluding specific faulty conditions
B. Selecting training data by purposely including all known faulty conditions
C. Selecting testing data from a different dataset than the training dataset
D. Selecting testing data from a boat manufacturer's bolt longevity data

Answer: A

Explanation:
The syllabus defines bias as:
"Bias is the systematic difference in treatment of certain objects, people or groups in comparison to others." It also discusses:
"Sample bias can occur if the data used for training the model does not represent the operational environment, or if some relevant faulty conditions are excluded deliberately."

NEW QUESTION # 135
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