CT-AI ISTQBCertified Tester AI Testing Exam Questions and Answers

The syllabus discusses using AI-based tools to reduce GUI test brittleness:

"AI can be used to reduce the brittleness of this approach, by employing AI-based tools to identify the correct objects using various criteria (e.g., XPath, label, id, class, X/Y coordinates), and to choose the historically most stable identification criteria."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 11.6.1)

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."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.3)

The syllabus states:

“A/B testing can be used to test updates to an AI-based system where there are agreed acceptance criteria, such as ML functional performance metrics, as described in Chapter 5. A/B testing is used to compare the updated variant with the previous variant.”

(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.4, page 68 of 99)

 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​​.

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.

A. Black box attacks based on adversarial examples create an exact duplicate model of the original.

Black box attacks do not create an exact duplicate model. Instead, they exploit the model by querying it and using the outputs to craft adversarial examples without knowledge of the internal workings.

B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.

Adversarial examples typically cause the model to predict the incorrect class rather than just reducing accuracy. These examples are designed to be visually indistinguishable from the original image but lead to incorrect classifications.

C. These attacks can't be prevented by retraining the model with these examples augmented to the training data.

This statement is incorrect because retraining the model with adversarial examples included in the training data can help the model learn to resist such attacks, a technique known as adversarial training.

D. These examples are model specific and are not likely to cause another model trained on the same task to fail.

Adversarial examples are often model-specific, meaning that they exploit the specific weaknesses of a particular model. While some adversarial examples might transfer between models, many are tailored to the specific model they were generated for and may not affect other models trained on the same task.

Therefore, the correct answer isDbecause adversarial examples are typically model-specific and may not cause another model trained on the same task to fail​​.

The syllabus definesexplainabilityas the ability to understand how the AI-based system comes up with a particular result:

"Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7)

When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:

Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.

Why Not Other Options:

Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.

Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.

GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.

The syllabus lists multiple reasons for mislabelled data, including the lack of domain knowledge:

"Lack of required domain knowledge may lead to incorrect labelling."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 4.5.2, page 38 of 99)

The syllabus defines reward hacking as:

"Reward hacking can result from an AI-based system achieving a specified goal by using a 'clever' or 'easy' solution that perverts the spirit of the designer’s intent."

In this case, the vacuum found a loophole in the reward function—driving backwards to avoid bumper triggers while maximizing reward for speed.

(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.6, page 24 of 99)

In a neural network, the activation value of a neuron is determined by a combination of inputs from the previous layer, the weights of the connections, and the bias at the neuron level. Here’s a detailed breakdown:

Inputs for Activation Value:

Activation Values of Neurons in the Previous Layer:These are the outputs from neurons in the preceding layer that serve as inputs to the current neuron.

Weights Assigned to the Connections:Each connection between neurons has an associated weight, which determines the strength and direction of the input signal.

Individual Bias at the Neuron Level:Each neuron has a bias value that adjusts the input sum, allowing the activation function to be shifted.

Calculation:

The activation value is computed by summing the weighted inputs from the previous layer and adding the bias.

Formula: z=∑(wi⋅ai)+bz = \sum (w_i \cdot a_i) + bz=∑(wi​⋅ai​)+b, where wiw_iwi​ are the weights, aia_iai​ are the activation values from the previous layer, and bbb is the bias.

The activation function (e.g., sigmoid, ReLU) is then applied to this sum to get the final activation value.

Why Option A is Correct:

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.

Eliminating Other Options:

B. Activation values of neurons in the previous layer, and weights assigned to the connections between the neurons: This option misses the bias, which is crucial.

C. Individual bias at the neuron level, and weights assigned to the connections between the neurons: This option misses the activation values from the previous layer.

D. Individual bias at the neuron level, and activation values of neurons in the previous layer: This option misses the weights, which are essential.

Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.

AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.

AI systems require changing operational environments; therefore, flexibility is required (B): While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.

Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.

Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.

Hence, the correct answer isC. Flexible AI systems allow for easier modification of the system as a whole.

The syllabus states:

"Transparency: This is considered to be the ease with which the algorithm and training data used to generate the model can be determined."

Access to the training data is an example of transparency.

(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7, page 24 of 99)

 A. Technological singularity

Technological singularity refers to a hypothetical point in the future when AI surpasses human intelligence and can continuously improve itself without human intervention. This scenario involves capabilities far beyond those described in the question.

 B. Narrow AI

Narrow AI, also known as weak AI, is designed to perform a specific task or a narrow range of tasks. It does not have general cognitive abilities and cannot learn multiple tasks by itself without retraining.

 C. Super AI

Super AI refers to an AI that surpasses human intelligence and capabilities across all fields. This is an advanced concept and not aligned with the description of having cognitive abilities similar to a young child.

 D. General AI

General AI, or strong AI, has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human cognitive abilities. It aligns with the description of AI that can learn multiple tasks and operate in new environments without needing retraining.

The syllabus states:

"Concept drift occurs when the operational environment changes without the trained model changing correspondingly. The outputs of the model become less accurate and less useful. Therefore, the operational model should be regularly evaluated against its acceptance criteria."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6, Page 54 of 99)

The syllabus details that threshold coverage requires each neuron to achieve an activation value greater than a specified threshold:

"Threshold coverage: Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 6.2, page 48 of 99)

In the syllabus, theevolutioncharacteristic for AI-based systems means the ability of the system to evolve and adapt its behavior in response to changes in the environment or in its own performance:

"Evolution is the system’s ability to change itself to adapt to new situations, different hardware, or a changing operational environment."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3)

The syllabus defines back-to-back testing as a method to compare a modified AI system to the previous version, which is ideal in this scenario:

"Back-to-back testing is performed by comparing the outputs of two systems that are supposed to provide the same outputs, one being a known and trusted system and the other being the test system. This approach can be used to test ML systems after re-training to verify that improvements have not introduced regressions."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 9.3, page 67 of 99)

The syllabus highlights that on-device training and processing require considerable computational power, which may exceed the capabilities of some mobile devices:

"Self-learning and continuous learning systems require large amounts of computational power, which can impact system performance and stability if the hardware is not powerful enough."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.3, page 22 of 99)

A. 64-bit CPUs.

While 64-bit CPUs are essential for handling large amounts of memory and performing complex computations, they are not specifically optimized for the types of operations commonly used in machine learning.

B. Hardware supporting fast matrix multiplication.

Matrix multiplication is a fundamental operation in many machine learning algorithms, especially in neural networks and deep learning. Hardware optimized for fast matrix multiplication, such as GPUs (Graphics Processing Units), is most suitable for implementing AI and ML because it can handle the parallel processing required for these operations efficiently.

C. High powered CPUs.

High powered CPUs are beneficial for general-purpose computing tasks and some aspects of ML, but they are not as efficient as specialized hardware like GPUs for matrix multiplication and other ML-specific tasks.

D. Hardware supporting high precision floating point operations.

High precision floating point operations are important for scientific computing and some specific AI tasks, but for many ML applications, fast matrix multiplication is more critical than high precision alone.

Therefore, the correct answer isBbecause hardware supporting fast matrix multiplication, such as GPUs, is most suitable for the parallel processing requirements of machine learning​​.

 Labelling Approaches:Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 4.5 Data Labelling for Supervised Learning, which discusses various approaches to data labeling, including pre-labeled datasets, and their associated time and effort requirements​​.