The syllabus states:

“When testing probabilistic and non-deterministic systems, the same input may produce different outputs. Tests need to be run several times to produce statistically valid test results, ensuring that an appropriate number of answers are accurate.”

(Reference: ISTQB CT-AI Syllabus v1.0, Section 8.4, page 58 of 99)

 Testing Autonomy:Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.

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

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​​.

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.

According to the syllabus, pre-trained models often inherit biases and limitations from the data and processes used in their original training, which may not align with the new use case. Specifically, the syllabus states:

"When using a pre-trained model, the training data and process cannot be fully controlled or known by the user of the model. As a result, the model can inherit biases or inaccuracies that were part of its original development and training process."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 1.8.3)

 Coverage for Neuron Activation Values:Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 6.2 Coverage Measures for Neural Networks, which details different types of coverage measures, including sign change coverage​​.

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.

Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:

A. Clustering is classification of a continuous quantity.

This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.

B. Clustering is supervised learning.

This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.

C. Clustering is done without prior knowledge of output classes.

This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.

D. Clustering requires you to know the classes.

This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.

Therefore, the correct answer isCbecause clustering is an unsupervised learning technique done without prior knowledge of output classes​​.

 Labelling Approaches:Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 4.5 Data Labelling for Supervised Learning, which discusses various approaches to data labeling, including pre-labeled datasets, and their associated time and effort requirements​​.

The syllabus explains that ML models can be used to analyze reported defects and suggest which developers are best suited to fix them based on historical data about defect assignment and resolution speed:

"Assignment: ML models can suggest which developers are best suited to fix particular defects, based on the defect content and previous developer assignments."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 11.2, page 78 of 99)

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.

The syllabus defines clustering as:

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

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

The syllabus clearly explains the structure of a deep neural network (DNN):

"A deep neural network comprises three types of layers. The input layer receives inputs... Between the input and output layers are hidden layers made up of artificial neurons, which are also known as nodes."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 6.1, page 45 of 99)

The syllabus describes that data pre-processing includes cleaning (e.g., fixing or removing invalid data) and transforming data (e.g., changing data types such as numbers stored as strings).

"Transformation: The format of the given data is changed… converting categorical data into numerical data, changing image formats…"

(Reference: ISTQB CT-AI Syllabus v1.0, Section 4.1.1, Page 34 of 99)

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​​.

The syllabus details that threshold coverage requires each neuron to achieve an activation value greater than a specified threshold:

"Threshold coverage: Full threshold coverage requires that each neuron in the neural network achieves an activation value greater than a specified threshold."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 6.2, page 48 of 99)

The syllabus definesexplainabilityas the ability to understand how the AI-based system comes up with a particular result:

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

(Reference: ISTQB CT-AI Syllabus v1.0, Section 2.7)

 Factors Affecting ML Functional Performance:The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.

 Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics​​​​.

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.

The syllabus mentions that AI can help optimize regression test suites:

"An AI-based tool can perform optimization of the regression test suite by analyzing… the information from previous test results, associated defects, and the latest changes that have been made, such as features which are broken more frequently and which tests exercise code impacted by recent changes."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 11.4, page 79 of 99)

The syllabus explains that input changes that arein the same domainas what was used for training are expected to be handled with adaptability:

"Adaptability refers to the ability of a system to adjust its behavior in response to changes in its environment or inputs. This includes changes to the inputs which are still within the expected operational range of the system, such as resolution changes in images or sensor data."

(Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6 and 8.2)