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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • systems from those required for conventional systems.
Topic 3
  • 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.
Topic 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.
Topic 5
  • 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.
Topic 6
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 7
  • 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 8
  • 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 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.
Topic 10
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
Which challenge to testing self-learning systems puts you at risk of a data attack?
Choose ONE option (1 out of 4)

Answer: C

Explanation:
The ISTQB CT-AI syllabus describes thatself-learning systems continuously adjust their behaviorduring operation as new data arrives. Section4.1 - Challenges of Testing AI-Based Systemshighlights that such systems are vulnerable todata attacks, particularly through adversarial inputs, poisoning, or malicious drift.
The risk arises because unexpected changes in the input distribution may alter the learned model in harmful ways. OptionD - Unexpected changescorresponds directly to this syllabus-defined risk.
Option A refers to system specification issues but does not relate to data attacks. Option B discusses environment complexity, which makes testing difficult but is not tied to adversarial threats. Option C (insufficient testing time) affects quality but does not specifically increase vulnerability to malicious data manipulation.
Unexpected changes-including data drift, poisoned samples, or maliciously constructed training data-pose the greatest risk. When a self-learning system adapts to altered data patterns, it may unknowingly learn incorrect associations, causing model degradation or manipulation. Therefore,Option Dcorrectly identifies the challenge that increases exposure to data attacks.


NEW QUESTION # 72
Which ONE of the below is MOST likely to be a challenge in ML data preparation?

Answer: B

Explanation:
The correct answer is A . The CT-AI syllabus identifies data preparation as one of the most resource-intensive parts of the ML workflow, stating that it uses an average of 43% of ML workflow effort. It also explains that data preparation forms part of the data pipeline and includes acquisition, pre-processing, feature engineering, and related exploratory analysis.
The syllabus lists several data preparation challenges, including the need for domain and data knowledge, difficulty obtaining high-quality data from multiple sources, difficulty automating a scalable and efficient production data pipeline, costs associated with data preparation, defects introduced into the data pipeline, and sample bias.
Option B, functional performance, is a result or evaluation concern for the ML model rather than a data preparation challenge itself. Option C, designing a reward function, is specifically a reinforcement learning challenge. Option D, specifying acceptance criteria, belongs primarily to understanding objectives and defining evaluation targets, not the data preparation activity.
References/topics: CT-AI Syllabus Chapter 4, Section 4.1 "Data Preparation as Part of the ML Workflow"; Section 4.1.1 "Challenges in Data Preparation."
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NEW QUESTION # 73
Which ONE of the following types of coverage SHOULD be used if test cases need to cause each neuron to achieve both positive and negative activation values?

Answer: D

Explanation:
Coverage for Neuron Activation Values: Sign change coverage is used to ensure that test cases cause each neuron to achieve both positive and negative activation values. This type of coverage ensures that the neurons are thoroughly tested under different activation states.


NEW QUESTION # 74
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?

Answer: D

Explanation:
The syllabus explains that supervised learning requires correctly labeled data so the algorithm can learn the relationship between input features and output labels:
"In supervised learning, the algorithm creates the ML model from labeled data during the training phase. The labeled data is used to infer the relationship between the input data and output labels."


NEW QUESTION # 75
You are conducting a user acceptance test of a decision support recommendation system used in a data processing business. In addition to testing the functional performance of the recommendation systems, what else would you be MOST likely to test?

Answer: D

Explanation:
The correct answer is D . A decision support recommendation system does not merely produce predictions; it influences human decision-making. The CT-AI syllabus explains that automation bias, also called complacency bias, occurs when humans become too trusting of automated recommendations. One form occurs when a human accepts system recommendations and fails to consider other sources, including their own judgment.
For that reason, testing should cover both the quality of the system's recommendations and the quality of the corresponding human input provided by representative users. The syllabus explicitly states that testers should understand how human decision-making may be compromised and should test both the system recommendations and the human input.
Options A and C are still functional performance measures of the recommendation system. Option B is a performance efficiency concern and may be relevant at system testing, but it does not address the decision- support risk. Option D directly targets the critical acceptance concern: whether users can detect and appropriately handle inaccurate AI recommendations rather than blindly following them.
References/topics: CT-AI Syllabus Chapter 7, Section 7.4 "Testing for Automation Bias in AI-Based Systems"; Section 7.2.6 "Acceptance Testing."


NEW QUESTION # 76
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