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Question 1

Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?

SELECT ONE OPTION

AIdentifying the relationship between developers and the modules developed by them.

BSearch of similar code based on natural language processing.

CClustering of similar code modules to predict based on similarity.

DUsing a classification model to predict the presence of a defect by using code quality metrics as the input data.

Answer : D

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.

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).

Question 2

Which supervised-learning classification/regression statement is correct?

Choose ONE option (1 out of 4)

ARecognizing a dog from many different images is a regression problem

BDeciding whether an object is a bicycle or a motorcycle is a classification problem

CPredicting that diesel prices will increase by ~10% is a classification problem

DIn classification, objects are always assigned to exactly two classes

Answer : B

The ISTQB CT-AI syllabus explains supervised learning under Section1.6 -- Machine Learning Approaches. It definesclassificationas predictingcategorical labels, whereasregressionpredictscontinuous numerical values. OptionB---deciding whether an object is a bicycle or a motorcycle---fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation .

Option A is incorrect because image recognition of a dog is aclassificationtask, not regression. Option C is incorrect because predicting a 10% price rise involves forecasting anumerical value, which is aregressionproblem. Option D is incorrect because classification can involveany number of classes, not only two. Multiclass classification is explicitly mentioned in the syllabus.

Therefore, OptionBis the only answer aligned with the syllabus' definitions.

Question 3

A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options. When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market, the only response from the automated system is, "I don't understand your question."

This AI system should be categorized as?

AGeneral AI

BNarrow AI

CSuper AI

DConventional AI

Answer : D

According to the syllabus,conventional AIsystems are limited to specific, pre-defined tasks and do not have generalized intelligence:

'Conventional AI systems are limited in their scope and typically only perform specific tasks within the domain for which they have been designed. They do not exhibit general AI behavior.'

(Reference: ISTQB CT-AI Syllabus v1.0, Section 1.2)

Question 4

There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?

AUse it to prioritize defects automatically based on the time expected for the fix to be made, the speed of the fix, and the likelihood of regressions

BUse it to assign defects to the best developer to resolve the problem and to load balance the defect assignments among the developers

CUse it to determine the root cause of each defect and develop a process improvement plan that can be implemented to remove the most common root causes

DUse it to review the code and determine where more defects are likely to occur so that testing can be targeted to those areas

Answer : B

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)

Question 5

Which of the following is a problem with AI-generated test cases that are generated from the requirements?

AThey are slow and will usually not be able to execute in the time allowed

BThey are defect-prone because they are unable to detect nuances in the requirements

CThey make debugging more complicated because the number of steps is usually high in order to induce the target failure

DThey are usually missing the expected results, so verification is difficult or must resort to only detecting significant failures

Answer : D

The syllabus mentions a drawback of AI-generated test cases:

''AI-based test generation tools can generate test cases... However, unless a test model that defines required behaviors is used as the basis of the tests, this form of test generation generally suffers from a test oracle problem because the AI-based tool does not know what the expected results should be.''

(Reference: ISTQB CT-AI Syllabus v1.0, Section 11.3, page 78 of 99)

Question 6

Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?

SELECT ONE OPTION

ASearch engines

BProcedural programming

CCase control structures

DGenetic algorithms

Answer : D

Technology Most Typically Used to Implement AI:Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.

Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.

Question 7

Which of the following options is an example of the concept of overfitting?

Choose ONE option (1 out of 4)

AA model for predicting academic performance was trained with data from students at one university. The model shows low predictive accuracy when applied to other universities.

BA model for the recognition of dogs was trained predominantly with pictures of dogs in parks. On pictures with other animals in parks, dogs are also falsely recognized.

CA previously trained model for recognizing cars is adapted and extended so that it can also identify the make of the car beyond its original function.

DA model for predicting IT system failures delivers too many false-negative predictions because the failures cannot be adequately explained via the log files used for training.

Answer : A

The ISTQB CT-AI syllabus definesoverfittingin Section3.2 -- ML Model Evaluationas a condition where an ML model learns the training data too precisely---including noise and irrelevant detail---resulting in poor performance on unseen data. Overfitting is characterized byhigh accuracy on training data but low accuracy on validation or real-world data. OptionAperfectly matches this definition: a model trained only on one university's student data generalizes poorly to students from other universities. This is a textbook example of overfitting because the model has essentially memorized patterns unique to a narrow dataset, instead of learning generalizable relationships applicable across environments .

Option B instead describessample biasor inadequate training diversity, not overfitting. Option C involvestransfer learningor model extension, unrelated to overfitting. Option D indicatesinsufficient training data qualityor lack of meaningful features, but not overfitting. Only Option A reflects the syllabus definition directly: overly specialized training leading to reduced predictive performance on new data.

Thus,Ais the correct and syllabus-aligned example of overfitting.

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