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ISTQB CT-AI Exam Syllabus Topics:

SectionObjectives
Topic 1: Ethics and Risk in AI Testing- Ethical considerations
  • 1. Privacy and security concerns
    • 2. Responsible AI principles
      - Risk-based testing for AI
      • 1. Risk identification in AI systems
        • 2. Mitigation strategies
          Topic 2: Testing AI-Based Systems- Test design techniques
          • 1. Data-driven test design
            • 2. Metamorphic testing
              - Test levels for AI systems
              • 1. Model testing
                • 2. System integration testing
                  Topic 3: AI Quality Characteristics- Quality attributes
                  • 1. Accuracy and robustness
                    • 2. Explainability and transparency
                      Topic 4: Machine Learning Fundamentals for Testing- ML lifecycle
                      • 1. Data collection and preparation
                        • 2. Training, validation, and evaluation
                          - Model types
                          • 1. Supervised and unsupervised learning
                            • 2. Deep learning basics
                              Topic 5: AI System Lifecycle and Operations- Deployment and monitoring
                              • 1. Model deployment strategies
                                • 2. Post-deployment monitoring
                                  - Continuous learning systems
                                  • 1. Model drift detection
                                    • 2. Retraining strategies
                                      Topic 6: Introduction to AI Testing- Challenges in AI testing
                                      • 1. Non-determinism in AI systems
                                        • 2. Data dependency issues
                                          - AI systems overview
                                          • 1. Differences between traditional and AI-based systems
                                            • 2. What is AI and machine learning systems
                                              Topic 7: Data Quality and Bias- Data quality assurance
                                              • 1. Data completeness and consistency
                                                • 2. Data labeling quality
                                                  - Bias and fairness
                                                  • 1. Fairness testing approaches
                                                    • 2. Types of bias in AI systems

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                                                      ISTQB Certified Tester AI Testing Exam Sample Questions (Q135-Q140):

                                                      NEW QUESTION # 135
                                                      A data scientist is performing unsupervised learning on a set of financial records relating to previous loan applications, and trying to predict defaults on future loans. They are reporting poor functional performance because of data issues. Which ONE of the below is LEAST likely to be a contributory factor?

                                                      Answer: D

                                                      Explanation:
                                                      The correct answer is B . In unsupervised learning, the ML model is created from unlabeled data . The algorithm infers patterns in the input data and assigns inputs to groups or classes based on commonalities, rather than learning from known output labels. The CT-AI syllabus identifies clustering and association as the main unsupervised learning problem types.
                                                      Missing information about whether previous loans were granted and repaid is essentially missing outcome label information. That would be highly relevant for supervised learning, where the model learns from input data and corresponding labels, but it is least directly relevant to an unsupervised approach. By contrast, options A, C, and D are classic dataset quality issues. Missing account records indicate incomplete data; inconsistent pre-processing can create data-format and comparability defects; and irrelevant account information may adversely influence results or waste modelling resources. The syllabus explicitly lists incomplete data, data not pre-processed, and irrelevant data as typical dataset quality issues affecting ML models.
                                                      References/topics: CT-AI Syllabus Chapter 3, Section 3.1.2 "Unsupervised Learning"; Chapter 4, Section 4.3 "Dataset Quality Issues"; Chapter 4, Section 4.4 "Data Quality and its Effect on the ML Model."
                                                      =========


                                                      NEW QUESTION # 136
                                                      Which of the following is a technique used in machine learning?

                                                      Answer: C

                                                      Explanation:
                                                      Decision trees are a foundational algorithm used in supervised machine learning. The syllabus describes:
                                                      "A decision tree is a tree-like ML model whose nodes represent decisions and whose branches represent possible outcomes."


                                                      NEW QUESTION # 137
                                                      A company is using a spam filter to attempt to identify which emails should be marked as spam. Detection rules are created by the filter that causes a message to be classified as spam. An attacker wishes to have all messages internal to the company be classified as spam. So, the attacker sends messages with obvious red flags in the body of the email and modifies the "from" portion of the email to make it appear that the emails have been sent by company members. The testers plan to use exploratory data analysis (EDA) to detect the attack and use this information to prevent future adversarial attacks.
                                                      How could EDA be used to detect this attack?

                                                      Answer: D

                                                      Explanation:
                                                      The syllabus explains that EDA can be used to analyze data to identify outliers and unusual patterns, which can indicate adversarial attacks like data poisoning:
                                                      "Testing to detect data poisoning is possible using EDA, as poisoned data may show up as outliers." (Reference: ISTQB CT-AI Syllabus v1.0, Section 9.1.2, page 67 of 99)


                                                      NEW QUESTION # 138
                                                      Which of the following statements about bias in AI based systems is MOST correct?

                                                      Answer: A

                                                      Explanation:
                                                      The correct answer is D . The CT-AI syllabus explains that ML systems make decisions and predictions using algorithms and collected data , and that both can introduce bias into the results. Algorithmic bias can occur when the learning algorithm is incorrectly configured, for example where it overvalues some data compared with other data. Sample bias can occur when training data is not fully representative of the data space to which ML is applied. The syllabus further states that inappropriate bias is often caused by sample bias, but can also be caused by algorithmic bias.
                                                      Option A is too narrow because it describes only one algorithmic mechanism. Option B is also incomplete because it describes sample bias only. Option C is incorrect because the syllabus states that bias can be introduced into many types of AI-based systems, not only ML systems involving personal data. Therefore, the most complete and syllabus-aligned statement is that inappropriate bias can arise from either the algorithm or the data.
                                                      References/topics: CT-AI Syllabus Chapter 2, Section 2.4 "Bias."
                                                      =========


                                                      NEW QUESTION # 139
                                                      Which of the following are the three activities in the data acquisition activities for data preparation?

                                                      Answer: D

                                                      Explanation:
                                                      The syllabus defines data acquisition as consisting of three steps:
                                                      "Data acquisition: The activity of acquiring data relevant to the business problem to be solved by an ML model, typically involving the activities of identifying, gathering and labelling data." (Reference: ISTQB CT-AI Syllabus v1.0, Section 4.1, page 33 of 99)


                                                      NEW QUESTION # 140
                                                      ......

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