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ISTQB CT-AI Prüfungsplan:

Thema
Einzelheiten

Thema 1

ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.

Thema 2

systems from those required for conventional systems.

Thema 3

Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.

Thema 4

Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.

Thema 5

ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.

Thema 6

Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based

Thema 7

Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.

Thema 8

Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

Thema 9

Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.

Thema 10

Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.

Thema 11

Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.

ISTQB Certified Tester AI Testing Exam CT-AI Prüfungsfragen mit Lösungen (Q17-Q22):

17. Frage
Which of the following are the three activities in the data acquisition activities for data preparation?

A. Cleaning, transforming, augmenting
B. Building, approving, deploying
C. Identifying, gathering, labelling
D. Feature selecting, feature growing, feature augmenting

Antwort: C

Begründung:
According to the ISTQB Certified Tester AI Testing (CT-AI) syllabus, data acquisition, a critical step in data preparation for machine learning (ML) workflows, consists of three key activities:
* Identification:This step involves determining the types of data required for training and prediction. For example, in a self-driving car application, data types such as radar, video, laser imaging, and LiDAR (Light Detection and Ranging) data may be identified as necessary sources.
* Gathering:After identifying the required data types, the sources from which the data will be collected are determined, along with the appropriate collection methods. An example could be gathering financial data from the International Monetary Fund (IMF) and integrating it into an AI-based system.
* Labeling:This process involves annotating or tagging the collected data to make it meaningful for supervised learning models. Labeling is an essential activity that helps machine learning algorithms differentiate between categories and make accurate predictions.
These activities ensure that the data is suitable for training and testing machine learning models, forming the foundation of data preparation.

18. Frage
An e-commerce developer built an application for automatic classification of online products in order to allow customers to select products faster. The goal is to provide more relevant products to the user based on prior purchases.
Which of the following factors is necessary for a supervised machine learning algorithm to be successful?

A. Minimizing the amount of time spent training the algorithm
B. Selecting the correct data pipeline for the ML training
C. Labeling the data correctly
D. Grouping similar products together before feeding them into the algorithm

Antwort: C

Begründung:
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.

19. Frage
Which ONE of the following options describes a scenario of A/B testing the LEAST?
SELECT ONE OPTION

A. A comparison of the performance of two different ML implementations on the same input data.
B. A comparison of the performance of an ML system on two different input datasets.
C. A comparison of two different websites for the same company to observe from a user acceptance perspective.
D. A comparison of two different offers in a recommendation system to decide on the more effective offer for same users.

Antwort: B

Begründung:
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.
Reference:
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).

20. Frage
You are testing an autonomous vehicle which uses AI to determine proper driving actions and responses. You have evaluated the parameters and combinations to be tested and have determined that there are too many to test in the time allowed. It has been suggested that you use pairwise testing to limit the parameters. Given the complexity of the software under test, what is likely the outcome from using pairwise testing?

A. The number of parameters to test can be reduced to less than a dozen
B. 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
C. 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
D. All high priority defects will be identified using this method

Antwort: B

Begründung:
The syllabus states that while pairwise testing is effective at finding defects by reducing the number of test cases needed, the resulting test suite can still be extensive and require automation:
"Even the use of pairwise testing can result in extensive test suites... automation and virtual test environments often become necessary to allow the required tests to be run." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.2, Page 67 of 99)

21. Frage
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION

A. The data pipeline
B. Biased data
C. The number of classes
D. The quality of the labeling

Antwort: C

Begründung:
* 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.

22. Frage
......

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