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.

Reference: This aligns with the methodology discussed in the syllabus under the section on using AI

for generating test cases from textual requirements​​.

​ A. This statement is incorrect. Current AI is true AI and there is no reason to believe that this fact

will change over time.

AI is an evolving field, and the definition of what constitutes AI can change as technology advances.

​ B. This statement is correct. In general, what is considered AI today may change over time.

The term AI is dynamic and has evolved over the years. What is considered AI today might be viewed

as standard computing in the future. Historically, as technologies become mainstream, they often

cease to be considered "AI".

​ C. This statement is incorrect. What is considered AI today will continue to be AI even as technology

evolves and changes.

This perspective does not account for the historical evolution of the definition of AI . As new

technologies emerge, the boundaries of AI shift.

​ D. This statement is correct. In general, today the term AI is utilized incorrectly.

While some may argue this, it is not a universal truth. The term AI encompasses a broad range of

technologies and applications, and its usage is generally consistent with current technological

capabilities.

Back-to-back testing is a method where the same set of tests are run on multiple implementations of

the system to compare their outputs. This type of testing is typically used to ensure consistency and

correctness by comparing the outputs of different implementations under identical conditions. Let's

analyze the options given:

A . Comparison of the results of a current neural network model ML model implemented in platform

A (for example Pytorch) with a similar neural network model ML model implemented in platform B

(for example Tensorflow), for the same data.

This option describes a scenario where two different implementations of the same type of model are

being compared using the same dataset. This is a typical back-to-back testing situation.

B . Comparison of the results of a home-grown neural network model ML model with results in a

neural network model implemented in a standard implementation (for example Pytorch) for the

same data.

This option involves comparing a custom implementation with a standard implementation, which is

also a typical back-to-back testing scenario to validate the custom model against a known

benchmark.

C . Comparison of the results of a neural network ML model with a current decision tree ML model

for the same data.

This option involves comparing two different types of models (a neural network and a decision tree).

This is not a typical scenario for back-to-back testing because the models are inherently different and

would not be expected to produce identical results even on the same data.

D . Comparison of the results of the current neural network ML model on the current data set with a

slightly modified data set.

This option involves comparing the outputs of the same model on slightly different datasets. This

could be seen as a form of robustness testing or sensitivity analysis, but not typical back-to-back

testing as it doesn’t involve comparing multiple implementations.

Based on this analysis, option C is the one that describes a situation of back-to-back testing the least

because it compares two fundamentally different models, which is not the intent of back-to-back

testing.

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 is D because adversarial examples are typically model-specific and may

not cause another model trained on the same task to fail​​.

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.

Reference:

ISTQB CT-AI Syllabus, Section 6.1, Neural Networks, discusses the components and functioning of

neurons in a neural network.

"Neural Network Activation Functions" (ISTQB CT-AI Syllabus, Section 6.1.1).