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NEW QUESTION # 11
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?
- A. Using a classification model to predict the presence of a defect by using code quality metrics as the input data.
- B. Identifying the relationship between developers and the modules developed by them.
- C. Clustering of similar code modules to predict based on similarity.
- D. Search of similar code based on natural language processing.
Answer: A
Explanation:
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.
NEW QUESTION # 12
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION
- A. Add 10% of the rows randomly and create another model and compare the R-Square scores of both the model.
- B. Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
- C. Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
- D. 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.
Answer: D
Explanation:
* 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.
NEW QUESTION # 13
Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?
SELECT ONE OPTION
- A. Different weather conditions
- B. Different Road Types
- C. Different features like ADAS, Lane Change Assistance etc.
- D. ML model metrics to evaluate the functional performance
Answer: D
Explanation:
Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options isleast likelyto be a reason for the explosion in the number of parameters.
* Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car's system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.
* Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car's sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.
* ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.
* Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.
Hence, theleast likelyreason for the incredible growth in the number of parameters isC. ML model metrics to evaluate the functional performance.
:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self-driving cars.
Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.
NEW QUESTION # 14
Which of the following statements about reinforcement learning is correct?
Choose ONE option (1 out of 4)
- A. From experience, the agent learns theoptimal reward function
- B. The agent's training is based on a reward function that rewards successful attempts
- C. The approach is suitable when the application doesnotrequire interaction with the environment
- D. The agent creates a model of the environment from labeled data during training
Answer: B
Explanation:
Section1.6.3 - Reinforcement Learningof the ISTQB CT-AI syllabus states that reinforcement learning (RL) is based on anagent interacting with an environment, performing actions, and receivingrewards or penalties.
The core concept is thereward function, which guides the agent's learning process. The syllabus emphasizes that training in RL isdriven by rewards, and the agent aims to maximize cumulative reward over time.
Therefore, OptionCdirectly reflects the correct description: the agent learns by being rewarded for successful actions .
Option A is incorrect because RL doesnotuse labeled data; that applies to supervised learning. Option B contradicts the syllabus definition: RL fundamentallyrequiresinteraction with the environment. Option D is incorrect because the reward function isdefined by humans, not learned by the agent; the agent learns apolicy, not the reward function itself.
Thus, OptionCis the only statement consistent with RL as defined in the syllabus.
NEW QUESTION # 15
Which statement regarding flexibility and adaptability of AI-based systems is correct?
- A. Adaptability and flexibility are important when the system needs to change its behavior and determine the change on its own.
- B. Adaptability is considered to be the ability of the system to be used in unspecified situations.
- C. Self-learning AI-based systems are classified according to whether they are adaptable only or flexible only.
- D. Flexibility is considered to be the ease with which the system can be reprogrammed to a changed operating condition.
Answer: A
Explanation:
The ISTQB CT-AI syllabus defines these two concepts clearly inSection 2.1 - Flexibility and Adaptability. Flexibility is described as the ability of a system to operate in situationsnot explicitly covered in its original requirements, while adaptability refers to how easily the system can be modified to handle new environments or conditions. The syllabus stresses that both flexibility and adaptability are crucial, particularly inself-learning AI systemsthat may need to respond to changes in their environment and adjust their behavior accordingly. It states that systems must be capable of determining when and how to adjust behavior in evolving situations, especially when the operational environment is not fully known at deployment time . This directly aligns with Option A.
NEW QUESTION # 16
Which ONE of the following combinations of Training, Validation, Testing data is used during the process of learning/creating the model?
SELECT ONE OPTION
- A. Training data * test data
- B. Training data - validation data
- C. Validation data - test data
- D. Training data - validation data - test data
Answer: D
Explanation:
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 is A because it includes all necessary datasets used during the process of learning and creating the model: training, validation, and test data.
NEW QUESTION # 17
Which of the following statements about ML functional performance metrics is correct?
Choose ONE option (1 out of 4)
- A. The silhouette coefficient describes how well the regression model fits the dependent variables.
- B. Metrics used to measure clustering include intra-cluster metrics that measure the proximity of a cluster's data points.
- C. The receiver operating characteristic curve shows, depending on parameters, how well the model distinguishes between different clusters.
- D. The R-squared metric indicates how well the model distinguishes between different classes based on the ROC curve.
Answer: B
Explanation:
The ISTQB CT-AI syllabus explains ML performance metrics in Section3.2 - Evaluating ML Models. For clustering, which is an unsupervised learning method, the syllabus lists metrics such asintra-cluster distance, inter-cluster distance, and coherence measures. Intra-cluster metrics evaluate how close data points are within a cluster, which directly corresponds to Option A.
Option B is incorrect becauseR-squaredis a regression metric measuring goodness-of-fit, not classification performance, and has no connection to ROC curves. Option C is wrong because thesilhouette coefficientis also a clustering metric, measuring cohesion vs. separation-not regression accuracy. Option D is incorrect because ROC curves evaluatebinary or multiclass classification, not clustering.
Thus, OptionAis the only accurate statement based on the syllabus.
NEW QUESTION # 18
A company producing consumable goods wants to identify groups of people with similar tastes for the purpose of targeting different products for each group. You have to choose and apply an appropriate ML type for this problem.
Which ONE of the following options represents the BEST possible solution for this above- mentioned task?
- A. Association
- B. Clustering
- C. Classification
- D. Regression
Answer: B
Explanation:
Clustering is an unsupervised learning method used to group similar data points based on their features. It is ideal for identifying groups of people with similar tastes without prior knowledge of the group labels. This technique will help the company segment its customer base effectively.
NEW QUESTION # 19
Which statement regarding data preparation in the ML workflow is correct?
- A. One challenge of data gathering is obtaining high-quality data from multiple sources.
- B. A key challenge in data transformation is the removal or correction of erroneous data.
- C. Since data preparation is time-consuming, all steps should be automated.
- D. Sampling is so well researched that it is no longer considered risky.
Answer: A
Explanation:
The ISTQB CT-AI syllabus describes theML data preparation workflowin Section2.2 - Data Preparation. Data preparation consists ofdata gathering,cleaning, transformation, andsampling.
The syllabus emphasizes that one significant challenge duringdata gatheringis combining data frommultiple heterogeneous sources, which often differ in structure, quality, and format. Ensuring the resulting dataset is accurate, complete, and representative can be complex, making this a critical challenge in the ML workflow. This aligns directly with Option C.
NEW QUESTION # 20
Which ONE of the following statements BEST describes how system complexity can cause challenges when testing an AI-based system?
- A. Obtaining test data is harder
- B. Obtaining test data is harder
- C. Unexpected changes in system behavior can occur
- D. Sometimes the system can only be tested as a black-box
Answer: C
Explanation:
Unexpected changes in system behavior can occur due to the complexity of AI-based systems.
These systems often involve many interacting components, which can lead to unpredictable results or variations in performance, making it difficult to anticipate how the system will behave under certain conditions. This presents a significant challenge in testing, as such behavior can be difficult to reproduce or control.
NEW QUESTION # 21
Which ONE of the following statements is MOST true in relation to A/B testing?
- A. The purpose is to detect defects
- B. The purpose is to compare two variants of a system
- C. Production data is not used
- D. Many test cases can be generated
Answer: B
Explanation:
The correct answer is D . The CT-AI syllabus defines A/B testing as a method where the responses of two variants of a program, A and B, are compared using the same inputs to determine which variant is better. It is a statistical testing approach that typically compares results across several runs. The syllabus also states that a major difference between A/B testing and back-to-back testing is that A/B testing compares two variants of the same system, whereas back-to-back testing is used to detect defects.
Option A is incorrect because A/B testing does not generate test cases and gives no guidance on how tests should be designed, although operational inputs may be used. Option B is more characteristic of back-to-back testing, which uses a pseudo-oracle for defect detection. Option C is incorrect because production or operational inputs are often used, and the syllabus gives examples of e-commerce and web companies using A
/B testing in production to compare consumer responses. Therefore, comparison of two system variants is the defining feature.
References/topics: CT-AI Syllabus Chapter 9, Section 9.4 "A/B Testing"; Section 9.7 "Selecting Test Techniques for AI-Based Systems."
NEW QUESTION # 22
A robotic AI-based system is being built by a logistics company to operate within its unmanned warehouses. The warehouses can all be very different and new ones are being added each year.
It is expected that the system will not require retraining for each warehouse, and will be able to learn the location of different items and move them to specified locations on request.
Which ONE of the following attributes should be MOST carefully considered when specifying the objectives and acceptance criteria for the system?
- A. Explainability
- B. Inappropriate bias
- C. Ethics
- D. Adaptability
Answer: D
Explanation:
Adaptability is the most important attribute to consider in this scenario. Since the system will operate in different warehouses, each with potentially different layouts and configurations, it must be adaptable to various environments without needing retraining. The system should be able to learn and adjust to the specific characteristics of each warehouse, ensuring it functions effectively in all locations.
NEW QUESTION # 23
The training of an ML model... What type of bias is LEAST important to look for when testing the model?
Choose ONE option (1 out of 4)
- A. Automation bias
- B. Inappropriate bias
- C. Algorithmic bias
- D. Sample bias
Answer: A
Explanation:
The ISTQB CT-AI syllabus distinguishes between several types of bias relevant in AI testing, including sample bias,algorithmic bias, andinappropriate bias. In Section3.3 - Bias in AI-Based Systems, the syllabus stresses the importance of identifying biases that originate fromtraining data,model development, anddecision logic. Sample bias occurs when the training data does not adequately represent the population; algorithmic bias arises when the model produces systematically skewed results due to learned patterns; inappropriate bias involves ethically or socially problematic distortions in the outcomes. All three of these bias types directly affect theoutputs of the AI modeland are therefore highly relevant when testing an industrial inspection system intended to reliably detect defects. These biases can lead to defective items being missed or false alarms being raised, which impacts quality assurance significantly .
Automation bias, however, is fundamentally different. It refers to ahuman cognitive bias, where users (e.g., inspectors) overly trust or rely on the AI system's output. While important in user-interaction testing, it isnota biaswithin the ML model itself. Since the question asks which bias isleast important when testing the model, automation bias can be legitimately deprioritized duringmodel-level testing. Therefore, OptionBis correct.
NEW QUESTION # 24
An image classification system is being trained for classifying faces of humans. The distribution of the data is
70% ethnicity A and 30% for ethnicities B, C and D. Based ONLY on the above information, which of the following options BEST describes the situation of this image classification system?
SELECT ONE OPTION
- A. This is an example of expert system bias.
- B. This is an example of hyperparameter bias.
- C. This is an example of algorithmic bias.
- D. This is an example of sample bias.
Answer: D
Explanation:
* A. This is an example of expert system bias.
* Expert system bias refers to bias introduced by the rules or logic defined by experts in the system, not by the data distribution.
* B. This is an example of sample bias.
* Sample bias occurs when the training data is not representative of the overall population that the model will encounter in practice. In this case, the over-representation of ethnicity A (70%) compared to B, C, and D (30%) creates a sample bias, as the model may become biased towards better performance on ethnicity A.
* C. This is an example of hyperparameter bias.
* Hyperparameter bias relates to the settings and configurations used during the training process, not the data distribution itself.
* D. This is an example of algorithmic bias.
* Algorithmic bias refers to biases introduced by the algorithmic processes and decision-making rules, not directly by the distribution of training data.
Based on the provided information, optionB(sample bias) best describes the situation because the training data is skewed towards ethnicity A, potentially leading to biased model performance.
NEW QUESTION # 25
Which ONE of the below types of testing is NOT a type of experience based testing applied to an AI based system?
- A. Exploring training data to understand its variety, patterns, structure and shape - then using that to inform testing
- B. Usability testing of the format of the output predictions
- C. Using an industry checklist to assess the steps used to prepare an ML system
- D. Using knowledge about previous ML systems to identify potential biases
Answer: B
Explanation:
The correct answer is B . The CT-AI syllabus identifies experience-based testing as including error guessing, exploratory testing, and checklist-based testing , all of which may be applied to AI-based systems. It also explicitly relates exploratory testing to exploratory data analysis, where data is examined for patterns, relationships, trends, outliers, distribution, format, and ranges.
Option A matches exploratory testing and EDA, because the tester explores training data to guide subsequent testing. Option C matches error guessing, because the tester uses knowledge of previous ML-system failures, such as biased training data, to identify likely faults. Option D matches checklist-based testing, because the syllabus cites Google's ML test checklist as an effective checklist approach for ML systems.
Option B is not, by itself, an experience-based AI testing type. Usability testing may be important for AI- based systems, especially where users consume predictions, recommendations, or explanations, but it is classified as a quality-characteristic or user-experience concern, not as one of the named experience-based techniques in this syllabus context.
References/topics: CT-AI Syllabus Chapter 9, Section 9.6 "Experience-Based Testing of AI-Based Systems"; Section 9.6.1 "Exploratory Testing and EDA."
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NEW QUESTION # 26
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