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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 2
  • 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.
Topic 3
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 4
  • 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.
Topic 5
  • 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.
Topic 6
  • systems from those required for conventional systems.
Topic 7
  • 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.
Topic 8
  • 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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q146-Q151):

NEW QUESTION # 146
An ML engineer performing supervised learning needs to label images of football games based on the location of the football in the image. Which ONE of the below labeling approaches can be used?

Answer: B

Explanation:
Annotation is the correct labeling approach for supervised learning, as it involves manually labeling the images with the correct information, such as marking the location of the football in the image. This labeled data can then be used to train a machine learning model.


NEW QUESTION # 147
Which of the following statements about ML functional performance metrics is correct?

Answer: C

Explanation:
The ISTQB CT-AI syllabus explains ML performance metrics in Section3.2 - Evaluating ML Models. Forclustering, 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.


NEW QUESTION # 148
ln the near future, technology will have evolved, and Al will be able to learn multiple tasks by itself without needing to be retrained, allowing it to operate even in new environments. The cognitive abilities of Al are similar to a child of 1-2 years.' In the above quote, which ONE of the following options is the correct name of this type of Al?
SELECT ONE OPTION

Answer: A

Explanation:
* A. Technological singularity
Technological singularity refers to a hypothetical point in the future when AI surpasses human intelligence and can continuously improve itself without human intervention. This scenario involves capabilities far beyond those described in the question.
* B. Narrow AI
Narrow AI, also known as weak AI, is designed to perform a specific task or a narrow range of tasks. It does not have general cognitive abilities and cannot learn multiple tasks by itself without retraining.
* C. Super AI
Super AI refers to an AI that surpasses human intelligence and capabilities across all fields. This is an advanced concept and not aligned with the description of having cognitive abilities similar to a young child.
* D. General AI
General AI, or strong AI, has the ability to understand, learn, and apply knowledge across a wide range of tasks, similar to human cognitive abilities. It aligns with the description of AI that can learn multiple tasks and operate in new environments without needing retraining.


NEW QUESTION # 149
Which assignment of AI techniques to testing support is BEST?

Answer: A

Explanation:
The ISTQB CT-AI syllabus (Section5.2 - AI for Testing) explains that various AI approaches can support testing activities. Probabilistic methods--one of the three major AI technique groups--are used topredict system failures, especially when dealing with uncertainty, likelihood estimation, and reliability analysis. This aligns precisely with Option B.


NEW QUESTION # 150
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using. What should be the next step to progress along the machine learning workflow?

Answer: D

Explanation:
The ML workflow typically involves iterative steps, beginning with data preparation once the model and framework are selected. The syllabus explains:
"The steps shown in Figure 1 (the ML workflow) do not include the integration of the ML model with the non-ML parts of the overall system. Typically, ML models cannot be deployed in isolation and need to be integrated with the non-ML parts... The next step would be data preparation as part of the ML workflow to provide input data to support training by an ML algorithm or prediction by an ML model."


NEW QUESTION # 151
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