CT-AI Dumps with Practice Exam Questions Answers

Adeep neural network (DNN)is a type of artificial neural network that consists of multiple layers between the input and output layers. TheISTQB Certified Tester AI Testing (CT-AI) Syllabusoutlines the following characteristics of a DNN:

Structure of a Deep Neural Network:

ADNN comprises at least three types of layers:

Input layer: Receives the input data.

Hidden layers: Perform complex feature extraction and transformations.

Output layer: Produces the final prediction or classification​.

Analysis of Answer Choices:

A (Only input and output layers)→ Incorrect, as a DNN must haveat least one hidden layer.

B (At least one internal hidden layer)→ Correct, as a neural network must havehidden layersto be considered deep.

C (Minimum of five layers required)→ Incorrect, asthere is no strict definitionthat requires at least five layers.

D (Output layer is not connected to other layers)→ Incorrect, asthe output layer must be connectedto the hidden layers​.

Thus,Option B is the correct answer, asa deep neural network must have at least one hidden layer.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 6.1 (Neural Networks and Deep Neural Networks)

ISTQB CT-AI Syllabus v1.0, Section 6.2 (Structure of Deep Neural Networks)​.

AI-based systems can sometimes behave likeblack boxes, where the internal decision-making process is unclear.Transparencyrefers to theability to inspect and understand the training data, algorithms, and decision-making processof the AI system.

Transparency ensures that testers and stakeholders can review how an AI system was trained.

Access totraining datais a key factor in transparency because it allows testers toanalyze biases, completeness, and representativenessof the dataset.

Transparency is an essential characteristic of explainable AI (XAI).

Having access to training data means that testers can investigate how data influences AI behavior.

Regulatory and ethical AI guidelines emphasize transparency.

Many AI ethics frameworks, such asGDPR and Trustworthy AI guidelines, recommend transparency to ensurefair and explainable AI decision-making.

(A) Autonomy❌

Autonomy refers to an AI system’s ability to make decisions independentlywithout human intervention. However,having access to training data does not relate to autonomy, which is more about self-learning and decision-making without human control.

(B) Explorability❌

Explorability refers to the ability to test AI systems interactivelyto understand their behavior, but it does not directly relate to accessing training data.

(D) Accessibility❌

Accessibility refers to the ease with which people can use the system, not the ability to inspect the training data.

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

Why is Option C Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option C is the correct answer, astransparency involves access to training data, allowing testers to understand AI decision-making processes.

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:

A. Feature selection

Feature selection is the process of selecting the most relevant features from the data. While important, it is not directly about handling excess data.

B. Data sampling

Data sampling involves selecting a representative subset of the data for training. When there is more data than needed, sampling can be used to create a manageable dataset that maintains the statistical properties of the full dataset.

C. Data labeling

Data labeling involves annotating data for supervised learning. It is necessary for training models but does not address the issue of having excess data.

D. Data augmentation

Data augmentation is used to increase the size of the training dataset by creating modified versions of existing data. It is useful when there is insufficient data, not when there is excess data.

Therefore, the correct answer isBbecause data sampling is the most relevant activity when dealing with an excess amount of data for training​​.

AI-based systems have specific quality characteristics, includingevolution,autonomy, andadaptability. A test objective that evaluates whether an AI systemevolvesto improve performance over time directly aligns with AI quality characteristics​.

Explanation of Answer Choices:

Option A: The robot must evolve to optimize its routing.

Correct.Evolution is an AI quality characteristic that ensures the systemlearns from past experiencesand adapts to improve efficiency​.

Option B: The robot must recharge for no more than six hours a day.

Incorrect.This is an operational constraint rather than an AI-specific quality characteristic​.

Option C: The robot must record the time of each delivery which is compiled into a report.

Incorrect.Logging data does not relate to AI quality characteristics likeadaptability or autonomy​.

Option D: The robot must complete 99.99% of its deliveries each day.

Incorrect.This is a performance target rather than an AI quality characteristic​.

ISTQB CT-AI Syllabus References:

Evolution as an AI Quality Characteristic:"Check how well the system learns from its own experience. Check how well the system copes when the profile of data changes (i.e., concept drift)"​.

Thus,Option A is the best choice as it directly tests an AI quality characteristic (evolution) in the upgraded autonomous robot.