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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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
  • 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 6
  • 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.
Topic 7
  • systems from those required for conventional systems.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q94-Q99):

NEW QUESTION # 94
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using.
What should be the next step to progress along the machine learning workflow?

Answer: D

Explanation:
Themachine learning (ML) workflowfollows a structured sequence of steps. Once stakeholders have agreed on theobjectives, business priorities, and the framework/model selection, the next logical step is to prepare and pre-process the databefore training the model.
* Data Preparationis crucial becausemachine learning models rely heavily on the quality of input data. Poor data can result in biased, inaccurate, or unreliable models.
* The process involvesdata acquisition, cleaning, transformation, augmentation, and feature engineering.
* Preparing the dataensures it is in the right format, free from errors, and representative of the problem domain, leading to better generalization in training.
* A (Tune the ML Algorithm):Hyperparameter tuning occursafter the model has been trainedand evaluated.
* C (Agree on Acceptance Criteria):Acceptance criteria should already have been defined in theinitial objective-setting phasebefore framework and model selection.
* D (Evaluate the Framework and Model):The selection of the framework and ML model has already been completed. The next step isdata preparation, not reevaluation.
* ISTQB CT-AI Syllabus (Section 3.2: ML Workflow - Data Preparation Phase)
* "Data preparation comprises data acquisition, pre-processing, and feature engineering.
Exploratory data analysis (EDA) may be performed alongside these activities".
* "The data used to train, tune, and test the model must be representative of the operational data that will be used by the model".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the model selection is complete, thenext step in the ML workflow is to prepare and pre-process the datato ensure it is ready for training and testing. Thus, thecorrect answer is B.


NEW QUESTION # 95
While measuring the test coverage of a neural network, a test engineer wants to measure the number of neurons that have each output two activation function results with a minimum difference between the two results of 0.5. Which ONE of the below coverage measures would achieve that goal?

Answer: A

Explanation:
The correct answer is B. Value change coverage . The CT-AI syllabus defines value-change coverage as requiring test cases to cause each neuron to achieve two activation values , where the difference between the two values exceeds a chosen value.
The question states that the test engineer wants to count neurons that have produced two activation function results with a minimum difference of 0.5 . This is precisely the value-change coverage concept: the selected threshold is 0.5, and a neuron is covered when its activation values vary by at least that threshold. Option A, neuron coverage, only requires neurons to achieve an activation value greater than zero; it does not assess the magnitude of change between two outputs. Option C, sign-change coverage, requires neurons to achieve both positive and negative activation values, which focuses on sign rather than numerical distance. Option D is incorrect because the syllabus explicitly includes a coverage criterion matching the described objective.
References/topics: CT-AI Syllabus Chapter 6, Section 6.2 "Coverage Measures for Neural Networks."
=========


NEW QUESTION # 96
An engine manufacturing facility wants to apply machine learning to detect faulty bolts. Which of the following would result in bias in the model?

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 # 97
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

Answer: B

Explanation:
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
A . Clustering is classification of a continuous quantity.
This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.
B . Clustering is supervised learning.
This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.
C . Clustering is done without prior knowledge of output classes.
This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.
D . Clustering requires you to know the classes.
This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.
Therefore, the correct answer is C because clustering is an unsupervised learning technique done without prior knowledge of output classes.


NEW QUESTION # 98
Which challenge to testing self-learning systems puts you at risk of a data attack?

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. Option D - Unexpected changes corresponds directly to this syllabus-defined risk.


NEW QUESTION # 99
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