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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 3	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 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	systems from those required for conventional systems.
Topic 6	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 7	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 8	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 9	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 10	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 11	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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?
SELECT ONE OPTION

A. Identifying the relationship between developers and the modules developed by them.
B. Search of similar code based on natural language processing.
C. Using a classification model to predict the presence of a defect by using code quality metrics as the input data.
D. Clustering of similar code modules to predict based on similarity.

Answer: C

Explanation:
Defect prediction models aim to identify parts of the software that are likely to contain defects by analyzing historical data and code quality metrics. The primary goal is to use this predictive information to allocate testing and maintenance resources effectively. Let's break down why option D is the correct choice:
Understanding Classification Models:
Classification models are a type of supervised learning algorithm used to categorize or classify data into predefined classes or labels. In the context of defect prediction, the classification model would classify parts of the code as either "defective" or "non-defective" based on the input features.
Input Data - Code Quality Metrics:
The input data for these classification models typically includes various code quality metrics such as cyclomatic complexity, lines of code, number of methods, depth of inheritance, coupling between objects, etc.
These metrics help the model learn patterns associated with defects.
Historical Data:
Historical versions of the code along with their defect records provide the labeled data needed for training the classification model. By analyzing this historical data, the model can learn which metrics are indicative of defects.
Why Option D is Correct:
Option D specifies using a classification model to predict the presence of defects by using code quality metrics as input data. This accurately describes the process of defect prediction using historical bug data and quality metrics.
Eliminating Other Options:
A). Identifying the relationship between developers and the modules developed by them: This does not directly involve predicting defects based on code quality metrics and historical data.
B). Search of similar code based on natural language processing: While useful for other purposes, this method does not describe defect prediction using classification models and code metrics.
C). Clustering of similar code modules to predict based on similarity: Clustering is an unsupervised learning technique and does not directly align with the supervised learning approach typically used in defect prediction models.
References:
ISTQB CT-AI Syllabus, Section 9.5, Metamorphic Testing (MT), describes various testing techniques including classification models for defect prediction.
"Using AI for Defect Prediction" (ISTQB CT-AI Syllabus, Section 11.5.1).

NEW QUESTION # 109
A software component uses machine learning to recognize the digits from a scan of handwritten numbers. In the scenario above, which type of Machine Learning (ML) is this an example of?
SELECT ONE OPTION

A. Classification
B. Regression
C. Clustering
D. Reinforcement learning

Answer: A

Explanation:
Recognizing digits from a scan of handwritten numbers using machine learning is an example of classification. Here's a breakdown:
* Classification: This type of machine learning involves categorizing input data into predefined classes.
In this scenario, the input data (handwritten digits) are classified into one of the 10 digit classes (0-9).
* Why Not Other Options:
* Reinforcement Learning: This involves learning by interacting with an environment to achieve a goal, which does not fit the problem of recognizing digits.
* Regression: This is used for predicting continuous values, not discrete categories like digit recognition.
* Clustering: This involves grouping similar data points together without predefined classes, which is not the case here.
References:The explanation is based on the definitions of different machine learning types as outlined in the ISTQB CT-AI syllabus, specifically under supervised learning and classification.

NEW QUESTION # 110
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?

A. Search engines
B. Genetic algorithms
C. Case control structures
D. Procedural programming

Answer: B

Explanation:
Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.

NEW QUESTION # 111
Which of the following statements about the structure and function of neural networks is true?

A. A single-layer perceptron is NOT a neural network
B. The input layer of a deep neural network must have at least as many neurons as its output layer
C. The bias of a neuron is determined by the activation values of the neurons in the previous layer
D. Training a neural network only changes the values of the weights at the connections between neurons

Answer: D

Explanation:
Section1.7 - Neural Networksof the ISTQB CT-AI syllabus explains that neural networks consist of neurons connected by weighted links. During training,learning occurs by adjusting the weights on these connections. This is the essence of gradient descent and backpropagation. Option B correctly states this behavior: only the weights are modified, not the activation functions, neuron counts, or architectural structure.

NEW QUESTION # 112
Which ONE of the following statements is true about dynamic testing for inappropriate bias?

A. It can be necessary to obtain additional attributes about the data being processed
B. Testing should never be conducted in production
C. Inappropriate bias only needs to be tested when protected characteristics such as race and gender ate present in the inputs
D. Reviewing the source of the training data can reveal inappropriate bias

Answer: A

Explanation:
The correct answer is A . Dynamic testing for inappropriate bias may require testers to obtain additional attributes about the input data so that outcomes can be analyzed across relevant groups. The CT-AI syllabus explains that testers may need additional information about attributes potentially related to bias and then correlate those attributes with system results. This is particularly important where bias is based on "hidden" variables that are not explicitly present in the input data but are inferred by the algorithm.
Option B is partly relevant to static testing or review activity, but the question asks specifically about dynamic testing . Option C is incorrect because inappropriate bias may be present even when protected attributes are not explicit model inputs; the model may infer them indirectly from proxy variables. Option D is also incorrect because the syllabus states that measuring how changes in inputs affect outputs for bias-related groups may be performed in production as well as before release. Therefore, obtaining and correlating additional attributes is a valid and often necessary dynamic testing activity.
References/topics: CT-AI Syllabus Chapter 8, Section 8.3 "Testing for Algorithmic, Sample and Inappropriate Bias."
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NEW QUESTION # 113
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