CT-AI Certified Tester AI Testing Exam Questions and Answers

When verifyingautonomous AI-based systems, a critical aspect is ensuring that they maintain an appropriate level of autonomy whileonly requesting human intervention when necessary. If an AI system unnecessarily asks for human input, it defeats the purpose of autonomy and can:

Slow down operations.

Reduce trust in the system.

Indicate improper confidence thresholds in decision-making.

This is particularly crucial inautonomous vehicles, AI-driven financial trading, and robotic process automation, where excessive human intervention would hinder performance.

A. Test cases to verify that the system automatically confirms the correct classification of training data→ This is relevant for verifying training consistency but not for autonomy validation.

B. Test cases to detect the system appropriately automating its data input→ While relevant, data automation does not directly address the verification of autonomy.

D. Test cases to verify that the system automatically suppresses invalid output data→ This focuses on output filtering rather than decision-making autonomy.

Why are the other options incorrect?Thus, the mostcritical test casefor verifyingautonomous AI-based systemsis ensuring that itdoes not unnecessarily request human intervention.

Section 8.2 - Testing Autonomous AI-Based Systemsstates that it is crucial to testwhether the system requests human intervention only when necessaryand does not disrupt autonomy​.

Reference from ISTQB Certified Tester AI Testing Study Guide:

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​​.

Handlingtest datain AI-based systems presents numerous challenges, particularly in terms ofdata privacy and confidentiality. AI models often require vast amounts of training data, some of which may containpersonal, sensitive, or confidential information. Ensuringcompliance with data protection laws (e.g., GDPR, CCPA)and implementingsecure data-handling practicesis a major challenge in AI testing.

Data Privacy Regulations

AI-based systems frequently process personal data, such as images, names, and transaction details, leading toprivacy concerns.

Compliance with regulations such asGDPR (General Data Protection Regulation)andCCPA (California Consumer Privacy Act)requiresproper anonymization, encryption, or redactionof sensitive data before using it for testing.

Data Security Challenges

AI models mayleak confidential informationif proper security measures are not in place.

Protectingtraining and test data from unauthorized accessis crucial to maintainingtrust and compliance.

Legal and Ethical Considerations

Organizations mustobtain legal approvalbefore using certain datasets, especially those containinghealth records, financial data, or personally identifiable information (PII).

Testers may need toemploy synthetic dataordata maskingtechniques to minimize exposure risks.

(B) Output data or intermediate data❌

While analyzing output data is important, it does notpose a significant challengecompared to handlingpersonal or confidential test data.

(C) Video frame speed or aspect ratio❌

These aretechnical challengesin processing AI models but do not fall underdata privacy or ethical considerations.

(D) Data frameworks or machine learning frameworks❌

Choosing an appropriateML framework (e.g., TensorFlow, PyTorch)is important, but it is nota major challenge related to test data handling.

Handling personal or confidential data is a critical challenge in AI testing"Personal or otherwise confidential data may need special techniques for sanitization, encryption, or redaction.Legal approval for use may also be required."​

Why is Option A Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, asdata privacy and confidentiality are major challenges when handling test data for AI-based systems.

Data mislabeling occurs for several reasons, which can significantly impact the performance of machine learning (ML) models, especially in supervised learning. According to the ISTQB Certified Tester AI Testing (CT-AI) syllabus, mislabeling of data can be caused by the following factors:

Random errors by annotators– Mistakes made due to accidental misclassification.

Systemic errors– Errors introduced by incorrect labeling instructions or poor training of annotators.

Deliberate errors– Errors introduced intentionally by malicious data annotators.

Translation errors– Occur when correctly labeled data in one language is incorrectly translated into another language.

Subjectivity in labeling– Some labeling tasks require subjective judgment, leading to inconsistencies between different annotators.

Lack of domain knowledge– If annotators do not have sufficient expertise in the domain, they may label data incorrectly due to misunderstanding the context.

Complex classification tasks– The more complex the task, the higher the probability of labeling mistakes​.

Among the answer choices provided, "Lack of domain knowledge" (Option A) is the best answer because expertise is essential to accurately labeling data in complex domains such as medical, legal, or engineering fields.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 4.5.2 (Mislabeled Data in Datasets)

ISTQB CT-AI Syllabus v1.0, Section 4.3 (Dataset Quality Issues)

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.

References:

ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.

Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.

The problem described in the question is a classic case ofconcept drift. Concept drift occurs when the relationship between input variables and the output variable changes over time, leading to a decline in model accuracy​.

In this scenario, theaverage passenger and baggage weightsused in the model changed due to seasonal variations, but the model was not updated accordingly. This resulted in inaccurate predictions for fuel requirements in the winter season. This is an example ofseasonal drift, where model behavior changes periodically due to recurring trends (e.g., higher luggage weights in winter compared to summer).

To prevent such problems:

Themodel should be regularly testedfor concept drift against agreed ML functional performance criteria​.

Exploratory Data Analysis (EDA)should be performed periodically to detect gradual changes in input distributions.

Retraining of the modelwith updated training data should be done to maintain accuracy.

If drift is detected, mitigation techniques such asincremental learning, retraining with new data, or adjusting model parametersshould be employed.

Option B (Easing the performance standard instead of addressing drift): Lowering the performance standard is not a solution; it only masks the problem without fixing it. Instead, regular testing and retraining should be used to handle drift properly​.

Option C (Corruption and reloading the model): Model corruption is unrelated to this issue. Corruption refers to accidental or malicious damage to the model or data, whereas this case is due to a changing data environment​.

Option D (Lack of transparency): Transparency refers to how understandable the model’s decisions are, but the problem here is a change in data distributions, making drift the primary concern​.

ISTQB CT-AI Syllabus (Section 7.6: Testing for Concept Drift)

"The operational environment can change over time without the trained model changing correspondingly. This phenomenon is known as concept drift and typically causes the outputs of the model to become increasingly less accurate and less useful."​

"Systems that may be prone to concept drift should be regularly tested against their agreed ML functional performance criteria to ensure that any occurrences of concept drift are detected soon enough for the problem to be mitigated."​

ISTQB CT-AI Syllabus (Section 7.7: Selecting a Test Approach for an ML System)

"If concept drift is detected, it may be mitigated by retraining the system with up-to-date training data followed by confirmation testing, regression testing, and possibly A/B testing where the updated system must outperform the original system."​

Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the question describes a situation whereseasonal variations affected input data distributions, the correct answer isA: The model suffers from drift and therefore should be regularly tested to ensure that any occurrences of drift are detected soon enough for the problem to be mitigated.

The problem describes aclassification modelthat predicts whether a patient is at risk for a stroke. The confusion matrix is provided, and the testers have calculated a performance metric as2/3 or 0.6667.

From theISTQB Certified Tester AI Testing (CT-AI) Syllabus, the definitions of functional performance metrics from a confusion matrix include:

Accuracy:

Accuracy=TP+TNTP+TN+FP+FNAccuracy = \frac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN​

Measures the proportion of correctly classified instances(both true positives and true negatives)over the total dataset.

If the value is0.6667, it suggests that the metric includesboth correct positive and negative classifications, aligning with accuracy​.

Precision:

Precision=TPTP+FPPrecision = \frac{TP}{TP + FP}Precision=TP+FPTP​

Measures how manypredicted positive caseswere actually positive.

Doesnotmatch the given calculation.

Recall (Sensitivity):

Recall=TPTP+FNRecall = \frac{TP}{TP + FN}Recall=TP+FNTP​

Measures how manyactual positiveswere correctly identified.

Doesnotmatch the 0.6667 value.

F1-Score:

F1=2×Precision×RecallPrecision+RecallF1 = 2 \times \frac{Precision \times Recall}{Precision + Recall}F1=2×Precision+RecallPrecision×Recall​

A balance between precision and recall.

The formula isdifferent from the provided calculation.

Since the formula foraccuracymatches the calculated value of0.6667, the best answer isD. Accuracy​.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 5.1 (Confusion Matrix and Functional Performance Metrics)

ISTQB CT-AI Syllabus v1.0, Section 5.4 (Selecting ML Functional Performance Metrics)​

The process of developing a machine learning model typically involves the use of three types of datasets:

Training Data:This is used to train the model, i.e., to learn the patterns and relationships in the data.

Validation Data:This is used to tune the model’s hyperparameters and to prevent overfitting during the training process.

Test Data:This is used to evaluate the final model’s performance and to estimate how it will perform on unseen data.

Let's analyze each option:

A. Training data - validation data - test data

This option correctly includes all three types of datasets used in the process of creating and validating a model. The training data is used for learning, validation data for tuning, and test data for final evaluation.

B. Training data - validation data

This option misses the test data, which is crucial for evaluating the model’s performance on unseen data after the training and validation phases.

C. Training data - test data

This option misses the validation data, which is important for tuning the model and preventing overfitting during training.

D. Validation data - test data

This option misses the training data, which is essential for the initial learning phase of the model.

Therefore, the correct answer isAbecause it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data​​.

Exploratory Data Analysis (EDA) is an essential technique for examining datasets to uncover patterns, trends, and anomalies, including outliers. In this case, the attacker manipulates the spam filter by injecting emails with red flags and masking them as internal company emails. The primary goal of EDA here is to detect these adversarial modifications.

Detecting Outliers:

EDA techniques such as statistical analysis, clustering, and visualization can reveal patterns in email metadata (e.g., sender details, email content, frequency).

Outlier detection methods like Z-score, IQR (Interquartile Range), or machine learning-based anomaly detection can identify emails that significantly deviate from typical internal communications.

Identifying Distribution Shifts:

By analyzing the frequency and characteristics of emails flagged as spam, testers can detect if the attack has introduced unusual patterns.

If a surge of internal emails is suddenly classified as spam, EDA can help verify whether these classifications are consistent with historical data.

Feature Analysis for Adversarial Patterns:

EDA enables visualization techniques such as scatter plots or histograms to distinguish normal emails from manipulated ones.

Examining email metadata (e.g., changes in headers, unusual wording in email bodies) can reveal adversarial tactics.

Counteracting Adversarial Attacks:

Once anomalies are identified, the spam filter’s detection rules can be improved by retraining the model on corrected datasets.

The adversarial examples can be added to the training data to enhance the robustness of the filter against future attacks.

Exploratory Data Analysis (EDA) is used to detect outliers and adversarial attacks."EDA is where data are examined for patterns, relationships, trends, and outliers. It involves the interactive, hypothesis-driven exploration of data."​

EDA can identify poisoned or manipulated data by detecting anomalies and distribution shifts."Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers."​

EDA helps validate ML models and detect potential vulnerabilities."The use of exploratory techniques, primarily driven by data visualization, can help validate the ML algorithm being used, identify changes that result in efficient models, and leverage domain expertise."​

References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as EDA is specifically useful for detecting outliers, which can help identify manipulated spam emails.

AI-based tools can significantly optimize regression test suites by analyzing historical data, past test results, associated defects, and changes made to the software. These tools prioritize and select the most relevant test cases based on previous defect patterns and frequently failing features, which helps in reducing the test execution time while maintaining effectiveness.

The optimization process involves:

Prioritizing test cases:AI-based tools rank test cases based on past defect detection trends, ensuring that the most relevant tests are executed first.

Reducing redundant test cases:The tool can eliminate test cases that do not contribute significantly to defect detection, reducing overall test execution time.

Augmenting test cases:The AI can also suggest new test cases if certain features are more prone to defects.

This approach has been proven to reduce regression test suite sizes by up to 50% while maintaining fault detection capabilities​.

Section 11.4 - Using AI for the Optimization of Regression Test Suitesstates that AI-based tools can optimize regression test suites by analyzing past test data and defect occurrences, leading to significant reductions in test execution time​.

Reference from ISTQB Certified Tester AI Testing Study Guide:

Reward hacking occurs when an AI-based system optimizes for a reward function in a way that is unintended by its designers, leading to behavior that technically maximizes the defined reward but does not align with the intended objectives.

In this case, the robot vacuum was given a reward scheme that encouraged speed while discouraging collisions detected by bumper sensors. However, since the bumper sensors were only on the front, the AI found a loophole—driving backward—thereby avoiding triggering the bumper sensors while still maximizing its reward function.

This is a classic example of reward hacking, where an AI "games" the system to achieve high rewards in an unintended way. Other examples include:

An AI playing a video game that modifies the score directly instead of completing objectives.

A self-learning system exploiting minor inconsistencies in training data rather than genuinely improving performance.

Section 2.6 - Side Effects and Reward Hackingexplains that AI systems may produce unexpected, and sometimes harmful, results when optimizing for a given goal in ways not intended by designers​.

Definition of Reward Hacking in AI: "The activity performed by an intelligent agent to maximize its reward function to the detriment of meeting the original objective"​

Reference from ISTQB Certified Tester AI Testing Study Guide:

Object recognition issues in test automation often arise whendevelopers frequently change object identifiersin the GUI. AI can enhance the stability of GUI automation by:

Using multiple criteria for object identification

AI cantrack UI elements using multiple attributessuch asXPath, label, ID, class, and screen coordinatesrather than relying on a single identifier that may change over time.

This approach makes the automationless brittle and more adaptive to changes in the UI​.

Why other options are incorrect?

B (Ignore unrecognizable objects to avoid failures): Ignoring objects instead of identifying them properly wouldlead to incomplete or incorrect test execution.

C (Dynamically name objects and alter source code): AI-based testing tools donot modify application source code; they work byadjusting the recognition strategy.

D (Anticipate developer changes and pre-alter automation code): While AI can adapt,it does not predict future changes to the GUI, making this option unrealistic​.

Thus,Option A is the best answer, as AI tools enhance object recognitionby dynamically selecting the most stable and persistent identification methods, improving test automation reliability.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 11.6.1 (Using AI to Test Through the Graphical User Interface (GUI))

ISTQB CT-AI Syllabus v1.0, Section 11.6.2 (Using AI to Test the GUI)​.

For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here’s why:

Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.

Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.

Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.

Why Not Other Options:

Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.

References:This explanation is aligned with the critical quality characteristics for AI-based systems as mentioned in the ISTQB CT-AI syllabus, focusing on the certification of medical devices​​.

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.

References:

ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.

Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.

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.

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.

References: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.

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.

References:

ISTQB CT-AI Syllabus Section 3.2 on the ML Workflow outlines the different stages of the ML process, including the tuning phase where hyperparameters are set.

Sample Exam Questions document, Question #31 specifically addresses the stage in the ML workflow where hyperparameters are configured.

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).

A. This is an example of expert system bias.

Expert system bias refers to bias introduced by the rules or logic defined by experts in the system, not by the data distribution.

B. This is an example of sample bias.

Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice. In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.

C. This is an example of hyperparameter bias.

Hyperparameter bias relates to the settings and configurations used during the training process, not the data distribution itself.

D. This is an example of algorithmic bias.

Algorithmic bias refers to biases introduced by the algorithmic processes and decision-making rules, not directly by the distribution of training data.

Based on the provided information, optionB(sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance​​.

Understanding Supervised Learning - RegressionSupervised learning is a category of machine learning where the model is trained on labeled data. Within this category,regressionis used when the goal is to predict a continuous numeric value.

Regressiondeals with problems where the output variable is continuous in nature, meaning it can take any numerical value within a range.

Common examples include predicting prices, estimating demand, and analyzing production trends.

(A) Determining the optimal age for a chicken's egg-laying production using input data of the chicken's age and average daily egg production for one million chickens.✅(Correct)

This is a classicregression problembecause it involves predicting a continuous variable:daily egg productionbased on the input variablechicken's age.

The goal is to find a numerical relationship between age and egg production, which makesregression the appropriate supervised learning method.

(B) Recognizing a knife in carry-on luggage at a security checkpoint in an airport scanner.❌(Incorrect)

This is animage recognition task, which falls underclassification, not regression.

Classification problems involve assigning inputs to discrete categories (e.g., "knife detected" or "no knife detected").

(C) Determining if an animal is a pig or a cow based on image recognition.❌(Incorrect)

This is anotherclassification problemwhere the goal is to categorize an image into one of two labels (pig or cow).

(D) Predicting shopper purchasing behavior based on the category of shopper and the positioning of promotional displays within a store.❌(Incorrect)

This problem could involve a mix ofclassificationandassociation rule learning, but it does not explicitly predict a continuous variable in the way regression does.

Regression is used when predicting a numeric output."Predicting the age of a person based on input data about their habits or predicting the future prices of stocks are examples of problems that use regression."​

Supervised learning problems are divided into classification and regression."If the output is numeric and continuous in nature, it may be regression."​

Regression is commonly used for predicting numerical trends over time."Regression models result in a numerical or continuous output value for a given input."​

Analysis of Answer ChoicesReferences from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as it aligns with the principles of regression-based supervised learning.