Free and Premium ISTQB CT-AI Dumps Questions Answers

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:

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:

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.

Explainability in AI-based systems refers to the ease with which users can determine how the system reaches a particular result. It is a crucial aspect when demonstrating AI decision-making, as it ensures that decisions made by AI models are transparent, interpretable, and understandable by stakeholders.

Before deploying an AI-based system, a developer must validate how decisions are made in a test environment. This process falls under the characteristic of explainability because it involves clarifying how an AI model arrives at its conclusions, which helps build trust in the system and meet regulatory and ethical requirements.

ISTQB CT-AI Syllabus (Section 2.7: Transparency, Interpretability, and Explainability)

"Explainability is considered to be the ease with which users can determine how the AI-based system comes up with a particular result"​.

"Most users are presented with AI-based systems as ‘black boxes’ and have little awareness of how these systems arrive at their results. This ignorance may even apply to the data scientists who built the systems. Occasionally, users may not even be aware they are interacting with an AI-based system"​.

ISTQB CT-AI Syllabus (Section 8.6: Testing the Transparency, Interpretability, and Explainability of AI-based Systems)

"Testing the explainability of AI-based systems involves verifying whether users can understand and validate AI-generated decisions. This ensures that AI systems remain accountable and do not make incomprehensible or biased decisions"​.

Contrast with Other Options:

Autonomy (B): Autonomy relates to an AI system's ability to operate independently without human oversight​. While decision-making is a key function of autonomy, the focus here is on demonstrating the reasoning behind decisions, which falls under explainability rather than autonomy.

Self-learning (C): Self-learning systems adapt based on previous data and experiences, which is different from making decisions understandable to humans​.

Non-determinism (D): AI-based systems are often probabilistic and non-deterministic, meaning they do not always produce the same output for the same input. This can make testing and validation more challenging, but it does not relate to explaining the decision-making process​.

Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since the question explicitly asks about the characteristic under which decision-making falls when being demonstrated before deployment,explainability is the correct choicebecause it ensures that AI decisions are transparent, understandable, and accountable to stakeholders​.

Probabilistic and non-deterministic AI-based systemsdo not always produce the same output for identical inputs. This makes traditional testing approaches ineffective. Instead, the best approach is torun tests multiple times and analyze results statistically.

Statistical Validity:Running tests multiple times ensures that observed results are statistically significant. Instead of relying on a single test run,analyzing multiple iterations helps determine trends, probabilities, and outliers​.

Expected Result Tolerance:AI-based systems may produce different results within an acceptable range. Defining acceptable tolerances (e.g., "result must be within 2% of the optimal value") improves test effectiveness​.

A (Run Several Times for the Same Correct Result):AI systems are ofteninherently non-deterministicand may not return the exact same result every time. Expecting identical outputs contradicts the nature of these systems​.

B & C (Decomposing Tests into Data Ingestion Tests):While data ingestion quality is important, it does notdirectlysolve the issue of probabilistic test results. Statistical analysis is the key approach​.

ISTQB CT-AI Syllabus (Section 8.4: Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems)

"For probabilistic systems, running a test multiple times may be necessary to obtain a statistically valid test result."​.

"Where a single definitive output is not possible, results should be analyzed statistically rather than relying on individual test cases."​.

Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sinceprobabilistic AI systems do not always return the same result, the best approach is torun multiple test iterations and validate results statistically. Hence, thecorrect answer is D.

Back-to-back testing isused to compare two different versions of an ML model, which is precisely what is needed in this scenario.

The model initiallymisclassified dogs as wolvesdue to feature similarities.

Thetest team retrains the modelwith additional images of dogs and wolves.

The best way to verify whether this additional trainingimproved classification accuracyis to compare theoriginal model's output with the newly trained model's output using the same test dataset​.

A (Metamorphic Testing):Metamorphic testing is useful forgenerating new test casesbased on existing ones but does not directly compare different model versions​.

B (Adversarial Testing):Adversarial testing is used to check how robust a model is againstmaliciously perturbed inputs, not to verify training effectiveness​.

C (Pairwise Testing):Pairwise testing is a combinatorial technique for reducing the number of test casesby focusing on key variable interactions, not for validating model improvements​.

ISTQB CT-AI Syllabus (Section 9.3: Back-to-Back Testing)

"Back-to-back testing is used when an updated ML model needs to be compared against a previous version to confirm that it performs better or as expected"​.

"The results of the newly trained model are compared with those of the prior version to ensure that changes did not negatively impact performance"​.

Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:To verify that the model's performance improved after retraining,back-to-back testing is the most appropriate methodas it compares both model versions. Hence, thecorrect answer is D.

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​​.

Pre-processing is an essential step in data preparation that ensures data is clean, formatted correctly, and structured for effective machine learning (ML) model training. One common issue that can be resolved during pre-processing isnumbers stored as strings.

Explanation of Answer Choices:

Option A: Insufficient data

Incorrect. Pre-processing cannot resolve insufficient data. If data is lacking, techniques like data augmentation or external data collection are needed​.

Option B: Invalid data

Incorrect. While pre-processing can identify and handle some forms of invalid data (e.g., missing values, duplicate entries), it does not resolve all invalid data issues. Some cases may require domain expertise to determine validity​.

Option C: Wanted outliers

Incorrect. Pre-processing usually focuses on handling unwanted outliers. Wanted outliers may need to be preserved, which is more of a data selection decision rather than pre-processing​.

Option D: Numbers stored as strings

Correct. One of the key functions of data pre-processing isdata transformation, which includes converting incorrectly formatted data types, such as numbers stored as strings, into their correct numerical format​.

ISTQB CT-AI Syllabus References:

Data Pre-Processing Steps:"Transformation: The format of the given data is changed (e.g., breaking an address held as a string into its constituent parts, dropping a field holding a random identifier, converting categorical data into numerical data, changing image formats)"​.

 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.

A major risk when using apre-trained neural networkfor logo detection is that it mayinherit biases and defectsfrom the original dataset and training process. This means that the model could misidentify or fail to recognize certain logos due to:

Differences in data preparation:The original training data may have used a different preprocessing method than the new dataset, leading to inconsistencies.

Limited transparency:The exact details of the dataset and biases within it may not be known, which can cause unexpected behavior.

Bias in logo detection:If the model was trained on a dataset with certain color or text preferences, it may disproportionately misidentify logos with similar characteristics.

This inherited bias can result in:

False Positives:Recognizing other brand logos as the beer company's logo.

False Negatives:Failing to detect the actual logo when variations occur (e.g., different lighting or partial visibility).

Algorithmic Bias:The model may favor certain shapes or color contrasts due to biased training data.

Thus,the most appropriate risk associated with using this pre-trained model is inherited bias.

Section 1.8.3 - Risks of Using Pre-Trained Models and Transfer Learningexplains how pre-trained models may inheritbiases and undocumented defectsthat affect performance in a new environment​.

Reference from ISTQB Certified Tester AI Testing Study Guide:

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.

Clustering is a form ofunsupervised learning, which groups data points based onsimilarities without predefined labels. According toISTQB CT-AI Syllabus, clustering is used in scenarios where:

The objective is to find natural groupings in data.

The dataset does not have labeled outputs.

Patterns and structures need to be identified automatically​.

Analyzing the answer choices:

A. Associating shoppers with their shopping tendencies → Correct

Shoppers can be grouped based on purchasing behaviors(e.g., luxury shoppers vs. budget-conscious shoppers), which is a typical clustering application in market segmentation.

B. Grouping individual fish together based on their types of fins → Incorrect

If thetypes of fins are labeled, it becomes aclassification problem, which requires supervised learning.

C. Classifying muffin purchases based on packaging attractiveness → Incorrect

Classification, not clustering, because attractiveness scores or labels must be predefined.

D. Estimating the expected purchase of cat food after an ad campaign → Incorrect

This is a prediction task, best suited forregression models, which are part of supervised learning.

Thus,Option A is the best answer, asclusteringis used togroup shoppers based on tendencies without predefined labels​.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 3.1.2 (Unsupervised Learning - Clustering and Association)

ISTQB CT-AI Syllabus v1.0, Section 3.3 (Selecting a Form of ML - Clustering)​.

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 isDbecause adversarial examples are typically model-specific and may not cause another model trained on the same task to fail​​.

 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​​​​.

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)​.

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.

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.

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​​.

 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​​.

In AI-based systems, particularly those where theinternal structure has been generated by another software system, the complexity often makes it difficult for human testers to analyze the inner workings. As per the ISTQB Certified Tester AI Testing (CT-AI) Syllabus:

Black-box testingis particularly useful when dealing with AI systems that have been generated by another system because:

It allows testingwithout requiring knowledge of the internal logic.

The AI model may be too complex for human testers to comprehend, making white-box testing ineffective.

Black-box testing evaluates theinputs and outputs, ensuring functional correctnesswithout needing insight into how the system reaches a decision​.

Why other options are incorrect?

A (Test automation and black-box testing): While automation is possible,black-box testing is not primarily about automationbut aboutabstracting the internal complexity.

B (Understanding the logic of the software): This contradicts the premise of black-box testing, which is designed totest functionality without needing to understandthe inner workings.

C (Checking transparency of the algorithm):Black-box testing does not check algorithm transparency—that would requirewhite-box testing or explainability techniques​.

Thus, the best choice isOption D, as black-box testingremoves the need to analyze the internal structure of AI systems, making it the most appropriate testing method in this case.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 8.5 (Challenges Testing Complex AI-Based Systems)

ISTQB CT-AI Syllabus v1.0, Section 8.6 (Testing the Transparency, Interpretability, and Explainability of AI-Based Systems)​

A. Identifying suitable tests by looking at the complexity of the test cases.

While complexity analysis can help in selecting important test cases, it does not directly address the issue of optimizing the entire regression suite effectively.

B. Using a random subset of tests.

Randomly selecting test cases may miss critical tests and does not ensure an optimized regression suite. This approach lacks a systematic method for ensuring comprehensive coverage.

C. Automating test scripts using AI-based test automation tools.

Automation helps in running tests efficiently but does not inherently optimize the selection of tests to prevent the suite from growing too large.

D. Using an AI-based tool to optimize the regression test suite by analyzing past test results.

This is the most effective approach as AI-based tools can analyze historical test data, identify patterns, and prioritize tests that are more likely to catch defects based on past results. This method ensures an optimized and manageable regression test suite by focusing on the most impactful test cases.

Therefore, the correct answer isDbecause using an AI-based tool to analyze past test results is the best option to optimize regression test selection and manage the size of the regression suite effectively​​.

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.

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, optionCis 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. Technological singularity

Technological singularity refers to a hypothetical point in the future when AI surpasses human intelligence and can continuously improve itself without human intervention. This scenario involves capabilities far beyond those described in the question.

 B. Narrow AI

Narrow AI, also known as weak AI, is designed to perform a specific task or a narrow range of tasks. It does not have general cognitive abilities and cannot learn multiple tasks by itself without retraining.

 C. Super AI

Super AI refers to an AI that surpasses human intelligence and capabilities across all fields. This is an advanced concept and not aligned with the description of having cognitive abilities similar to a young child.

 D. General AI

General AI, or strong AI, has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human cognitive abilities. It aligns with the description of AI that can learn multiple tasks and operate in new environments without needing retraining.