ISTQB CT-AI Certified Tester AI Testing Exam Exam Practice Test

The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.

Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.

Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.

A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.

References:

ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.

Precision is a performance metric used to evaluate the accuracy of positive predictions in a classification model. It is defined by the formula:

Precision=TPTP+FP×100%\text{Precision} = \frac{TP}{TP + FP} \times 100\%Precision=TP+FPTP​×100%

Where:

TP (True Positives)= Number of correctly predicted positive cases

FP (False Positives)= Number of incorrectly predicted positive cases

The confusion matrix provided in the question would typically list these values. Based on ISTQB's guidelines for calculating precision, selecting the correct number of true positives and false positives from the given data should yield94.2%as the precision​.

Section 5.1 - Confusion Matrix and ML Functional Performance Metricsexplains the calculation ofprecisionusing the confusion matrix​.

Reference from ISTQB Certified Tester AI Testing Study Guide:

Bias in an AI system occurs when the training data contains inherent prejudices that cause the model to make unfair predictions. Experience-based testing, particularlyExploratory Data Analysis (EDA), helps uncover these biases by analyzing patterns, distributions, and potential discriminatory factors in the training data​.

Option A:“Experience-based testing should be used to confirm that the training data set is operationally relevant. This can include applying exploratory data analysis (EDA) to check for bias within the training data set.”

This is the correct answer. EDA involves examining the dataset for bias, inconsistencies, or missing values, ensuring fairness in ML model predictions​.

Option B:“Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set. If the two models significantly differ, it will indicate there is bias in the original model.”

Back-to-back testing is used for regression testing and to compare versions of an AI system but is not primarily used to detect bias​.

Option C:“Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation data set ensuring no bias is present.”

Acceptance testing focuses on meeting predefined business requirements rather than detecting and mitigating bias​.

Option D:“A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.”

A/B testing is used for evaluating variations of a model rather than for explicitly identifying bias​.

Bias Testing Methods:"AI-based systems should be tested for algorithmic bias, sample bias, and inappropriate bias. Experience-based testing and EDA are useful for detecting bias"​.

Exploratory Data Analysis (EDA):"EDA helps uncover potential bias in training data through visualization and statistical analysis"​.

Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,Option A is the best choice for detecting bias in the loan applicant model.

Narrow AI (also known as Weak AI) is designed to perform specific tasks without possessing general intelligence or consciousness. The AI system in the question is capable of recognizing speech and responding to specific booking-related requests but fails when asked about unrelated topics (such as weather or labor markets)​.

Option A:“General AI”

Incorrect. General AI (AGI) refers to an AI system that can perform any intellectual task a human can. The described system is task-specific and does not exhibit general intelligence​.

Option B:“Narrow AI”

Correct. The AI system is limited to a predefined domain (ticket booking) and cannot process unrelated questions. This is characteristic of Narrow AI, which excels at specific tasks but lacks broader cognitive abilities​.

Option C:“Super AI”

Incorrect. Super AI surpasses human intelligence, exhibiting advanced reasoning and creativity. The AI in the scenario is far from this level​.

Option D:“Conventional AI”

Incorrect. Conventional AI is a broader term that may include rule-based systems. The described system relies on machine learning and natural language processing, making it more aligned with Narrow AI​.

Definition of Narrow AI:"Narrow AI refers to AI systems that are designed to perform a single task or a limited set of tasks, without general intelligence"​.

General vs. Narrow AI:"General AI remains an area of research, while most current AI applications fall into the category of Narrow AI"​.

Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,option B is the correct categorization for the AI-based ticket booking system.

Whentesting an AI-based chat assistantfor e-commerce customers, thelack of sufficient specificationsmakes it difficult to use structured test techniques. TheISTQB CT-AI Syllabusrecommendsexploratory testingin such cases:

Why Exploratory Testing?

Exploratory testing is usefulwhen specifications are incomplete or unclear.

AI-based systems, particularly those usingnatural language processing (NLP),may not behave deterministically, making scripted test cases ineffective.

Thetester interacts dynamicallywith the system, identifying unexpected behaviorsnot documented in the specification​.

Analysis of Answer Choices:

A (Exploratory testing)→Correct, as it is the best approach when specifications are incomplete.

B (Static analysis)→ Incorrect, as static analysis checks code without execution, which isnot helpfulfor AI chatbots.

C (Equivalence partitioning)→ Incorrect, asthis technique requires well-defined inputs and outputs, which are missing due toinsufficient specifications.

D (State transition testing)→ Incorrect, as state-based testingrequires knowledge of valid and invalid transitions, which is difficult with a chatbot lacking a clear specification​.

Thus,Option A is the correct answer, asexploratory testing is the best approach when dealing with insufficient specifications in AI-based systems.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 7.7 (Selecting a Test Approach for an ML System)

ISTQB CT-AI Syllabus v1.0, Section 9.6 (Experience-Based Testing of AI-Based Systems)​.

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​​.

Pairwise testing is a combinatorial testing technique that reduces the number of test cases by focusing on testing interactions between pairs of parameters rather than all possible combinations. It is widely used in AI-based systems, including autonomous vehicles, where the number of possible input parameter combinations can be extremely high​.

Option A:“The number of parameters to test can be reduced to less than a dozen.”

This is incorrect. While pairwise testing significantly reduces the number of test cases, it does not necessarily limit them to a fixed number like a dozen. The final number of tests depends on the number of parameters and their possible values​.

Option B:“All high priority defects will be identified using this method.”

This is incorrect. While pairwise testing is effective in detecting defects caused by interactions between two parameters, it may not uncover defects resulting from more complex interactions involving three or more parameters​.

Option C:“While the number of tests needed can be reduced, there may still be a large enough set of tests that automation will be required to execute all of them.”

This is the correct answer. Even though pairwise testing reduces the number of test cases, AI-based systems such as autonomous vehicles still have a large number of test scenarios. Therefore, automation is often necessary to execute all test cases within the available time​.

Option D:“Pairwise cannot be applied to this problem because there is AI involved, and the evolving values may result in unexpected results that cannot be verified.”

This is incorrect. Pairwise testing can still be applied to AI-based systems, including those that evolve over time. However, additional testing techniques may be required to verify evolving behavior​.

Pairwise Testing for AI Systems:"Pairwise testing is widely used because it effectively reduces the number of test cases while maintaining defect detection capability"​.

Automation Requirement:"In practice, even with pairwise testing, extensive test suites may still require automation"​.

Analysis of the Answer Options:ISTQB CT-AI Syllabus References:

The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:

Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.

Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.

High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.

Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.

References:

ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.

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.

 AI-Based Autonomous Functions:An AI-based autonomous system is one that can respond to its environment without human intervention. The other options either involve human decisions or do not use AI at all.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Autonomy and Testing Autonomous AI-Based Systems​​​​.

Metamorphic Testing (MT)is a testing technique that verifies AI-based systems by generatingfollow-up test casesbased on existing test cases. These follow-up test cases adhere to aMetamorphic Relation (MR), ensuring that if the system is functioning correctly, changes in input should result in predictable changes in output.

Metamorphic testing works by transforming source test cases into follow-up test cases

Here, thesource test caseinvolves testing themedium-speed vehicle’stravel time.

Thefollow-up test casesare derived byextrapolating travel times for fast and slow vehiclesusing predictable relationships based on speed differences.

MR states that modifying input should result in a predictable change in output

Since the speed of the vehicle is a known factor, it is possible to predict the new arrival times and verify whether they follow expected trends.

This is a direct application of metamorphic testing principles

Inroute optimization systems, metamorphic testing often applies transformations tospeed, distance, or conditionsto verify expected outcomes.

(B) Decomposing each route into traffic density and vehicle power❌

While useful for statistical analysis, this approach does not generate follow-up test cases based on a definedmetamorphic relation (MR).

(C) Selecting dissimilar routes and transforming them into a fast or slow route❌

Thisdoes not follow metamorphic testing principles, which require predictable transformations.

(D) Running fast vehicles on long routes and slow vehicles on short routes❌

This methoddoes not maintain a controlled MRand introduces too manyuncontrolled variables.

Metamorphic testing generates follow-up test cases based on a source test case."MT is a technique aimed at generating test cases which are based on a source test case that has passed.One or more follow-up test cases are generated by changing (metamorphizing) the source test case based on a metamorphic relation (MR)."​

MT has been used for testing route optimization AI systems."In the area of AI, MT has been used for testing image recognition, search engines, route optimization and voice recognition, among others."​

Why Option A is Correct?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, as it aligns with the principles ofmetamorphic testing by modifying input speeds and verifying expected results.

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.

The question asks which deficiency is most likely to be discovered by the test expert given the scenario of poor real-world performance despite good isolated accuracy.

A lack of similarity between the training and testing data (A): This is a common issue in ML where the model performs well on training data but poorly on real-world data due to a lack of representativeness in the training data. This leads to poor generalization to new, unseen data.

The input data has not been tested for quality prior to use for testing (B): While data quality is important, this option is less likely to be the primary reason for the described issue compared to the representativeness of training data.

A lack of focus on choosing the right functional-performance metrics (C): Proper metrics are crucial, but the issue described seems more related to the data mismatch rather than metric selection.

A lack of focus on non-functional requirements testing (D): Non-functional requirements are important, but the scenario specifically mentions issues with detecting real cancer cases, pointing more towards data issues.

References:

ISTQB CT-AI Syllabus Section 4.2 on Training, Validation, and Test Datasets emphasizes the importance of using representative datasets to ensure the model generalizes well to real-world data.

Sample Exam Questions document, Question #40 addresses issues related to data representativeness and model generalization.

A. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

Adding more data to the training set can affect the R-Square score, but it does not directly verify the correctness of the implementation.

B. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.

Changing the order of input features should not significantly affect the R-Square score if the implementation is correct, but this approach is more about testing model robustness rather than correctness of the implementation.

C. Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.

This approach directly compares the performance of two implementations of the same algorithm. If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation "X" is correct.

D. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.

Dropping data can lead to variations in the R-Square score but does not directly verify the correctness of the implementation.

Therefore, optionCis the most appropriate strategy because it directly compares the performance of the new implementation "X" with another implementation using the same algorithm and datasets, which helps in verifying the correctness of the implementation​​.

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​​.

Themachine learning (ML) workflowfollows a structured sequence of steps. Once stakeholders have agreed on theobjectives, business priorities, and the framework/model selection, the next logical step is toprepare and pre-process the databefore training the model.

Data Preparationis crucial becausemachine learning models rely heavily on the quality of input data. Poor data can result in biased, inaccurate, or unreliable models​.

The process involvesdata acquisition, cleaning, transformation, augmentation, and feature engineering​.

Preparing the dataensures it is in the right format, free from errors, and representative of the problem domain, leading to better generalization in training.

A (Tune the ML Algorithm):Hyperparameter tuning occursafter the model has been trainedand evaluated​.

C (Agree on Acceptance Criteria):Acceptance criteria should already have been defined in theinitial objective-setting phasebefore framework and model selection​.

D (Evaluate the Framework and Model):The selection of the framework and ML model has already been completed. The next step isdata preparation, not reevaluation​.

ISTQB CT-AI Syllabus (Section 3.2: ML Workflow - Data Preparation Phase)

"Data preparation comprises data acquisition, pre-processing, and feature engineering. Exploratory data analysis (EDA) may be performed alongside these activities"​.

"The data used to train, tune, and test the model must be representative of the operational data that will be used by the model"​.

Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the model selection is complete, thenext step in the ML workflow is to prepare and pre-process the datato ensure it is ready for training and testing. Thus, thecorrect answer is B.

A/B testing is a statistical testing method that compares two different versions of a system to determine which one performs better. In this scenario, theold NLP algorithmwas rated for accuracy, and after the update, thenew algorithmwas also rated by users. A statistical test was performed to compare the two versions, which is the fundamental approach ofA/B testing.

A/B testing is commonly used in:

User experience testing(e.g., comparing different versions of a website).

ML model evaluation(e.g., comparing two AI-based classifiers).

Performance assessment(e.g., determining if a new recommendation algorithm is more effective).

This approach allows for data-driven decisions, ensuring that any changes to the system result in meaningful improvements.

Section 9.4 - A/B Testingstates that A/B testing is used to compare updates in AI-based systems to determine if the newer version is better​.

Reference from ISTQB Certified Tester AI Testing Study Guide:

 A. Using a pixel comparison of the GUI before and after the change to check the differences.

Pixel comparison is a traditional method and does not involve AI. It compares images at the pixel level, which can be effective but is not an intelligent approach. It is not considered an AI usage and is the least likely usage of AI for detecting GUI changes.

 B. Using computer vision to compare the GUI before and after the test object changes.

Computer vision involves using AI techniques to interpret and process images. It is a likely usage of AI for detecting changes in the GUI.

 C. Using vision-based detection of the GUI layout changes before and after test object changes.

Vision-based detection is another AI technique where the layout and structure of the GUI are analyzed to detect changes. This is a typical application of AI.

 D. Using a ML-based classifier to flag if changes in GUI are to be flagged for humans.

An ML-based classifier can intelligently determine significant changes and decide if they need human review, which is a sophisticated AI application.

The question refers to a problem where data used for an object detection ML system was labelled incorrectly. This issue is most closely related to "accuracy issues." Here's a detailed explanation:

Accuracy Issues: The primary goal of labeling data in machine learning is to ensure that the model can accurately learn and make predictions based on the given labels. Incorrectly labeled data directly impacts the model's accuracy, leading to poor performance because the model learns incorrect patterns.

Why Not Other Options:

Security Issues: This pertains to data breaches or unauthorized access, which is not relevant to the problem of incorrect data labeling.

Privacy Issues: This concerns the protection of personal data and is not related to the accuracy of data labeling.

Bias Issues: While bias in data can affect model performance, it specifically refers to systematic errors or prejudices in the data rather than outright incorrect labeling.

References:This explanation is consistent with the concepts covered in the ISTQB CT-AI syllabus under dataset quality issues and their impact on machine learning models​​.

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)​.

A. The difficulty of defining criteria for improvement before the model can be accepted.

Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.

B. The fast pace of change did not allow sufficient time for testing.

This can significantly affect the model's performance. If the system is self-learning, it needs to adapt quickly, and insufficient testing time can lead to incomplete learning and poor performance.

C. The unknown nature and insufficient specification of the operating environment might have caused the poor performance.

This is highly likely to affect performance. Self-learning AI systems require detailed specifications of the operating environment to adapt and learn effectively. If the environment is insufficiently specified, the model may fail to perform accurately in real-world scenarios.

D. There was an algorithmic bias in the AI system.

Algorithmic bias can significantly impact the performance of AI systems. If the model has biases, it will not perform well across different scenarios and data distributions.

Given the context of the self-learning nature and the need for real-time adaptability, optionAis least likely to describe the fall in performance because it deals with acceptance criteria rather than real-time performance issues​​.

To calculate the accuracy, recall, and specificity from the confusion matrix provided, we use the following formulas:

Confusion Matrix:

Actually Rotten: 45 (True Positive), 8 (False Positive)

Actually Fresh: 5 (False Negative), 42 (True Negative)

Accuracy:

Accuracy is the proportion of true results (both true positives and true negatives) in the total population.

Formula: Accuracy=TP+TNTP+TN+FP+FN\text{Accuracy} = \frac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN​

Calculation: Accuracy=45+4245+42+8+5=87100=0.87\text{Accuracy} = \frac{45 + 42}{45 + 42 + 8 + 5} = \frac{87}{100} = 0.87Accuracy=45+42+8+545+42​=10087​=0.87

Recall (Sensitivity):

Recall is the proportion of true positive results in the total actual positives.

Formula: Recall=TPTP+FN\text{Recall} = \frac{TP}{TP + FN}Recall=TP+FNTP​

Calculation: Recall=4545+5=4550=0.9\text{Recall} = \frac{45}{45 + 5} = \frac{45}{50} = 0.9Recall=45+545​=5045​=0.9

Specificity:

Specificity is the proportion of true negative results in the total actual negatives.

Formula: Specificity=TNTN+FP\text{Specificity} = \frac{TN}{TN + FP}Specificity=TN+FPTN​

Calculation: Specificity=4242+8=4250=0.84\text{Specificity} = \frac{42}{42 + 8} = \frac{42}{50} = 0.84Specificity=42+842​=5042​=0.84

Therefore, the correct combinations of accuracy, recall, and specificity are 0.87, 0.9, and 0.84 respectively.

References:

ISTQB CT-AI Syllabus, Section 5.1, Confusion Matrix, provides detailed formulas and explanations for calculating various metrics including accuracy, recall, and specificity.

"ML Functional Performance Metrics" (ISTQB CT-AI Syllabus, Section 5).

 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​​.

AI and ML models can play a significant role in optimizing defect resolution processes. According to theISTQB Certified Tester AI Testing (CT-AI) Syllabus, ML models can be used toanalyze defect reports, prioritize critical defects, and assign defects to developersbased on historical defect resolution patterns. The key AI applications for defect management include:

Defect Categorization– NLP techniques can analyze defect reports and classify them based on metadata like severity and impact.

Defect Prioritization– ML models trained on past defects can predict which issues are likely to cause failures, allowing teams toprioritizethe most critical issues​.

Defect Assignment– AI-based models can suggest which developers are best suited for specific defects, optimizing the resolution process based on past performance and specialization​.

From the given answer choices:

Option A (Automatic Prioritization)is useful but does not directlyreduce backlog efficientlyby considering developer expertise and workload balancing.

Option C (Root Cause Analysis for Process Improvement)is along-term strategybut does not directly address backlog reduction.

Option D (Defect Prediction for Testing Focus)helps preemptively identify issues but does not resolve the existing backlog.

Thus,Option Bis the best choice as it aligns with AI's capability toassign defects to the most suitable developersbased on historical data, ensuring efficient defect resolution and backlog reduction​.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 11.2 (Using AI to Analyze Reported Defects)

ISTQB CT-AI Syllabus v1.0, Section 11.5 (Using AI for Defect Prediction)​.