CT-AI Exam Dumps - ISTQB AI Testing Questions and Answers

Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.

Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.

Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.

Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.

Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.

Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.

The question asks which test is least likely to be performed during the ML model testing phase. Let's consider each option:

Testing the accuracy of the classification model (A): Accuracy testing is a fundamental part of the ML model testing phase. It ensures that the model correctly classifies the data as intended and meets the required performance metrics.

Testing the API of the service powered by the ML model (B): Testing the API is crucial, especially if the ML model is deployed as part of a service. This ensures that the service integrates well with other systems and that the API performs as expected.

Testing the speed of the training of the model (C): This is least likely to be part of the ML model testing phase. The speed of training is more relevant during the development phase when optimizing and tuning the model. During testing, the focus is more on the model's performance and behavior rather than how quickly it was trained.

Testing the speed of the prediction by the model (D): Testing the speed of prediction is important to ensure that the model meets performance requirements in a production environment, especially for real-time applications.

The ISTQB CT-AI syllabus explains in Section1.6 – Machine Learning Approachesthatsupervised learningis appropriate when labeled data exists and the goal is to predict an output based on known historical examples. Predicting a customer’spurchase probabilityis aclassificationtask when the output corresponds to discrete categories such as“likely to purchase”vs.“not likely to purchase.”The syllabus gives similar examples in describing classification as the process of assigning instances to predefined classes based on learned patterns in labeled data. Because the retail company wants to determine whether a customer will make a purchase based on marketing actions, classification is the most appropriate choice .

Option A (regression) predicts continuous numeric values and is less suitable because the task centers oncategorical likelihood, not estimating exact monetary values. Option C (unsupervised learning) is used when labels are not available—here, the company has labeled purchase histories. Option D (reinforcement learning) requires an interactive environment with reward-driven behavior, which is not applicable to this scenario.

Thus,supervised learning (classification)is the most suitable approach according to the syllabus.

The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:

"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1)

From the confusion matrix:

True Positives (TP) = 15

False Positives (FP) = 5

False Negatives (FN) = 15

True Negatives (TN) = 65

Accuracy= (TP + TN) / Total

= (15 + 65) / 100

=80%

Precision= TP / (TP + FP)

= 15 / (15 + 5)

= 15 / 20

=75%

Section3.2 – Functional Performance Criteriain the syllabus explains accuracy and precision exactly these ways when evaluating ML classification performance.

Option B is therefore the only correct pair of values.

Bias detection at thedata levelis performed by reviewingdata acquisition and preprocessing steps, as explained in Section2.3 – Data Quality and Biasof the ISTQB CT-AI syllabus. Sample bias arises when data is distorted or when preprocessing introduces unintended shifts—for example, by filtering, normalization, or labeling steps that disproportionately affect subsets of the data. OptionBcorrectly reflects this: reviewers can identify whether preprocessing steps have altered the dataset in a way that introducessample distortions. This aligns perfectly with syllabus guidance on reviewing data pipelines for bias sources.

Option A is incorrect because algorithmic bias originates from themodel, not data collection procedures. Option C is incorrect because LIME is anexplainabilitymethod applied post-model, not in data reviews. Option D incorrectly states “algorithmic bias,” but preprocessing affectssample bias, not algorithmic bias.

Thus, OptionBcorrectly matches the syllabus’ definition of how bias can be detected during data-related reviews.

Setting model and algorithm hyperparameters is an essential step in the machine learning workflow, primarily occurring during the tuning phase.

Evaluating the model (A): This stage involves assessing the model's performance using metrics and does not typically include the setting of hyperparameters.

Deploying the model (B): Deployment is the stage where the model is put into production and used in real-world applications. Hyperparameters should already be set before this stage.

Tuning the model (C): This is the correct stage where hyperparameters are set. Tuning involves adjusting the hyperparameters to optimize the model's performance.

Data testing (D): Data testing involves ensuring the quality and integrity of the data used for training and testing the model. It does not include setting hyperparameters.

Hence, the most appropriate stage of the ML workflow to set model and algorithm hyperparameters isC. Tuning the model.

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 isCbecause clustering is an unsupervised learning technique done without prior knowledge of output classes​​.

Automation bias is defined in Section4.4 – Human Factors in AI Testingof the ISTQB CT-AI syllabus. It refers to the human tendency to overly trust, rely on, or defer to automated system outputs. The syllabus explains that this bias arises especially indecision-support systems, where humans may accept AI judgments without adequate verification. This aligns directly with Option B.

Option A is incorrect: automation biasdoesinfluence testing, especially when testers rely excessively on AI outputs. The syllabus cautions about testers adopting the same cognitive biases as end users. Option C is incorrect because autonomous systems are not the primary context; rather,systems supporting human decisionsare most impacted. Option D is incorrect because the quality of human inputmatters significantly, and poorly designed user studies can mask or distort automation bias.

Thus,Option Bis the syllabus-accurate description of automation bias.

The syllabus defines clustering as:

“Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing.”

(Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)