CT-AI Exam Dumps - ISTQB AI Testing Questions and Answers

AI-generated test cases are often created using machine learning (ML) models or heuristic algorithms. While these can be effective in generating large numbers of test cases quickly, they oftensuffer from the "test oracle problem."

Test Oracle Problem:A test oracle is the mechanism used to determine the expected output of a test case. AI-generated test cases oftenlack expected resultsbecause AI-based tools do not inherently understand what the correct output should be​.

Difficulty in Verification:Without expected results, verifying test cases becomes challenging. Testers mustrely on heuristics, anomaly detection, or significant failures, rather than traditional pass/fail conditions​.

A (Slow Execution Time):AI-generated tests are typically automated and designed for efficiency. They are not inherently slow and often executefasterthan manually written tests​.

B (Defect-Prone Due to Nuance Issues):While AI-generated tests may struggle with some complexities in requirements, they primarilylack expected results, rather than failing due to an inability to detect nuances​.

C (Complicated Debugging Due to Many Steps):AI-generated testsreducedebugging complexity by limiting the number of steps required to reproduce failures​.

ISTQB CT-AI Syllabus (Section 11.3: Using AI for Test Case Generation)

"AI-generated test cases often lack expected results, making it difficult to verify correctness without a test oracle."​.

"Verification often relies on detecting significant failures rather than having predefined expected results."​.

Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Since AI-generated test cases frequentlylack expected results, verification becomes difficult, requiring testers tofocus on major failuresrather than precise pass/fail conditions. Thus, thecorrect answer is D.

AI test environments have several unique characteristics that differentiate them from traditional test environments. Let’s evaluate each option:

A. Challenges resulting from low accuracy of the models.

Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.

B. The challenge of mimicking undefined scenarios generated due to self-learning.

AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.

C. The challenge of providing explainability to the decisions made by the system.

Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.

D. Challenges in the creation of scenarios of human handover for autonomous systems.

While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI.

Given the above points, optionDis the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.

Overfitting occurs when a machine learning (ML) model learns patterns that are too specific to the training data, leading to a lack of generalization for new, unseen data. This means the model performs exceptionally well on the training data but poorly on validation or test data because it has memorized the noise and minor details rather than learning the underlying patterns​.

Option A:“The model is not able to generalize to accommodate new types of data.”

This is the correct definition of overfitting. When a model cannot generalize beyond its training data, it struggles with new input, which results in overfitting​.

Option B:“The model is too simplistic for the data.”

This describes underfitting rather than overfitting. Underfitting happens when a model is too simple to capture the underlying patterns in the data​.

Option C:“The model is missing relationships between the inputs and outputs.”

This also aligns more with underfitting, where the model fails to capture important relationships in the data​.

Option D:“The model discards data it considers to be noise or outliers.”

While some ML models may ignore outliers, overfitting actually occurs when the model includes noise and outliers in its learning process rather than discarding them​.

Overfitting Definition:"Overfitting occurs when the model fits too closely to a set of data points and fails to properly generalize. It works well on training data but struggles with new data."​.

Testing for Overfitting:"Overfitting may be detected by testing the model with a dataset that is completely independent of the training dataset"

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

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)​.

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​​.

A/B testing, also known as split testing, is a method used to compare two versions of a product or system to determine which one performs better. It is widely used in web development, marketing, and machine learning to optimize user experiences and model performance. Here’s why option C is the least descriptive of an A/B testing scenario:

Understanding A/B Testing:

In A/B testing, two versions (A and B) of a system or feature are tested against each other. The objective is to measure which version performs better based on predefined metrics such as user engagement, conversion rates, or other performance indicators.

Application in Machine Learning:

In ML systems, A/B testing might involve comparing two different models, algorithms, or system configurations on the same set of data to observe which yields better results.

Why Option C is the Least Descriptive:

Option C describes comparing the performance of an ML system on two different input datasets. This scenario focuses on the input data variation rather than the comparison of system versions or features, which is the essence of A/B testing. A/B testing typically involves a controlled experiment with two versions being tested under the same conditions, not different datasets.

Clarifying the Other Options:

A. A comparison of two different websites for the same company to observe from a user acceptance perspective: This is a classic example of A/B testing where two versions of a website are compared.

B. A comparison of two different offers in a recommendation system to decide on the more effective offer for the same users: This is another example of A/B testing in a recommendation system.

D. A comparison of the performance of two different ML implementations on the same input data: This fits the A/B testing model where two implementations are compared under the same conditions.

References:

ISTQB CT-AI Syllabus, Section 9.4, A/B Testing, explains the methodology and application of A/B testing in various contexts.

"Understanding A/B Testing" (ISTQB CT-AI Syllabus).

Supervised machine learning requires correctly labeled data to train an effective model. The learning process relies on input-output mappings where each training example consists of an input (features) and a correctly labeled output (target variable). Incorrect labeling can significantly degrade model performance.

Supervised Learning Process

The algorithm learns from labeled data, mapping inputs to correct outputs during training.

If labels are incorrect, the model will learn incorrect relationships and produce unreliable predictions.

Quality of Training Data

The accuracy of any supervised ML model ishighly dependent on the quality of labels.

Poorly labeled data leads to mislabeled training sets, resulting inbiased or underperforming models.

Error Minimization and Model Accuracy

Incorrectly labeled data affects theconfusion matrix, reducing precision, recall, and accuracy.

It leads to overfitting or underfitting, which decreases the model's ability to generalize.

Industry Standard Practices

Many AI development teams spend a significant amount of time ondata annotation and quality controlto ensure high-quality labeled datasets.

(B) Minimizing the amount of time spent training the algorithm❌(Incorrect)

While reducing training time is important for efficiency, the quality of training is more critical. A well-trained model takes time to process large datasets and optimize its parameters.

(C) Selecting the correct data pipeline for the ML training❌(Incorrect)

A good data pipeline helps, butit does not directly impact learning successas much as labeling does.Even a well-optimized pipeline cannot fix incorrect labels.

(D) Grouping similar products together before feeding them into the algorithm❌(Incorrect)

This describesclustering, which is anunsupervised learning technique. Supervised learningrequires labeled examples, not just grouping of data.

Labeled data is necessary for supervised learning."For supervised learning, it is necessary to have properly labeled data."​

Data labeling errors can impact performance."Supervised learning assumes that the data is correctly labeled by the data annotators.However, it is rare in practice for all items in a dataset to be labeled correctly."​

Why Labeling is Critical?Why Other Options are Incorrect?References from ISTQB Certified Tester AI Testing Study GuideThus,option A is the correct answer, ascorrectly labeled data is essential for supervised machine learning success.

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.

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​​.

A. 64-bit CPUs.

While 64-bit CPUs are essential for handling large amounts of memory and performing complex computations, they are not specifically optimized for the types of operations commonly used in machine learning.

B. Hardware supporting fast matrix multiplication.

Matrix multiplication is a fundamental operation in many machine learning algorithms, especially in neural networks and deep learning. Hardware optimized for fast matrix multiplication, such as GPUs (Graphics Processing Units), is most suitable for implementing AI and ML because it can handle the parallel processing required for these operations efficiently.

C. High powered CPUs.

High powered CPUs are beneficial for general-purpose computing tasks and some aspects of ML, but they are not as efficient as specialized hardware like GPUs for matrix multiplication and other ML-specific tasks.

D. Hardware supporting high precision floating point operations.

High precision floating point operations are important for scientific computing and some specific AI tasks, but for many ML applications, fast matrix multiplication is more critical than high precision alone.

Therefore, the correct answer isBbecause hardware supporting fast matrix multiplication, such as GPUs, is most suitable for the parallel processing requirements of machine learning​​.