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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 4	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 5	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 6	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 7	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 8	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q73-Q78):

NEW QUESTION # 73
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?

A. Testing the data pipeline for any sources for algorithmic bias.
B. Testing the distribution shift in the training data for inappropriate bias.
C. Test the model during model evaluation for data bias.
D. Check the input test data for potential sample bias.

Answer: C

Explanation:
This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.

NEW QUESTION # 74
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 testing data from a boat manufacturer's bolt longevity data
B. Selecting training data by purposely including all known faulty conditions
C. Selecting training data by purposely excluding specific faulty conditions
D. Selecting testing data from a different dataset than the training dataset

Answer: C

Explanation:
Bias in AI models often originates fromincomplete or non-representative training data. In this case, if the training datasetpurposely excludes specific faulty conditions, the machine learning model willfail to learn and detectthese conditions in real-world scenarios.
This results in:
* Sample bias, where the training data is not fully representative of all possible faulty conditions.
* Algorithmic bias, where the model prioritizes certain defect types while ignoring others.
* B. Selecting training data by purposely including all known faulty conditions# This would help reduce bias by improving model generalization.
* C. Selecting testing data from a different dataset than the training dataset# This is a good practice to evaluate model generalization but does not inherently introduce bias.
* D. Selecting testing data from a boat manufacturer's bolt longevity data# While using unrelated data can createpoor model accuracy, it does not directly introduce bias unless systematic patterns in the incorrect dataset lead to unfair decision-making.
* Section 8.3 - Testing for Algorithmic, Sample, and Inappropriate Biasstates thatsample bias can occur if the training dataset is not fully representative of the expected data space, leading to biased predictions.
Why are the other options incorrect?Reference from ISTQB Certified Tester AI Testing Study Guide:

NEW QUESTION # 75
Which ONE of the following statements BEST describes how system complexity can cause challenges when testing an AI based system?

A. Sometimes the system can only be tested as a black-box
B. Obtaining test data is harder
C. Unexpected changes in system behaviour can occur
D. It is necessary to measure how changes in the test inputs cause changes in the test outputs

Answer: A

Explanation:
The correct answer is D . The syllabus explains that AI-based systems are often used for tasks too complex for humans to perform, and this can create a test oracle problem because testers may not be able to determine expected results in the usual way. It also states that when the internal structure of an AI-based system is generated by software, the structure may be too complex for humans to understand, leading to the situation where the system can only be tested as a black box.
Option A concerns test data acquisition, which is a general AI testing challenge but not the central complexity mechanism identified in this question. Option B describes an approach used in explainability or bias-related investigation, especially model-agnostic reasoning about input-output sensitivity. Option C is more characteristic of self-learning systems, where the system may change its own behaviour over time.
Complexity primarily causes difficulty because expected behaviour, internal logic, component interactions, and test oracles become hard to understand or define.
References/topics: CT-AI Syllabus Chapter 8, Section 8.5 "Challenges Testing Complex AI-Based Systems"; Section 8.7 "Test Oracles for AI-Based Systems."
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NEW QUESTION # 76
Which statement regarding data preparation in the ML workflow is correct?

A. A key challenge in data transformation is the removal or correction of erroneous data.
B. One challenge of data gathering is obtaining high-quality data from multiple sources.
C. Sampling is so well researched that it is no longer considered risky.
D. Since data preparation is time-consuming, all steps should be automated.

Answer: B

Explanation:
The ISTQB CT-AI syllabus describes theML data preparation workflowin Section2.2 - Data Preparation. Data preparation consists ofdata gathering,cleaning, transformation, andsampling.
The syllabus emphasizes that one significant challenge duringdata gatheringis combining data frommultiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow. This aligns directly with Option C.

NEW QUESTION # 77
Which of the following options is an example of the concept of overfitting?
Choose ONE option (1 out of 4)

A. 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.
B. 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.
C. 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.
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.

Answer: B

Explanation:
The ISTQB CT-AI syllabus definesoverfittingin Section3.2 - ML Model Evaluationas a condition where an ML model learns the training data too precisely-including noise and irrelevant detail-resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. OptionAperfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities. This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .
Option B instead describessample biasor inadequate training diversity, not overfitting. Option C involves transfer learningor model extension, unrelated to overfitting. Option D indicatesinsufficient training data qualityor lack of meaningful features, but not overfitting. Only Option A reflects the syllabus definition directly: overly specialized training leading to reduced predictive performance on new data.
Thus,Ais the correct and syllabus-aligned example of overfitting.

NEW QUESTION # 78
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