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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing Generative AI and Large Language Models | 15% | - Evaluation of generative AI outputs - Specific risks and test approaches for LLMs - Generative AI fundamentals |
| Topic 2: Input Data Testing for Machine Learning Systems | 20% | - Validation of data pipelines and preprocessing - Data quality attributes - Label verification and ground truth assessment - Testing for bias, representativeness, and completeness |
| Topic 3: Model Testing for Machine Learning Systems | 20% | - Explainability and transparency testing - Model performance and accuracy testing - Adversarial and robustness testing - Metamorphic and statistical testing - Drift detection and monitoring |
| Topic 4: Test Levels and Machine Learning Systems | 15% | - Testing across ML lifecycle stages - Test strategies for AI projects - Unit, integration, system, and acceptance testing for ML |
| Topic 5: Test Approaches and Techniques for AI Systems | 10% | - Test environment and tool considerations - A/B testing and back-to-back testing - Exploratory testing and red teaming - Risk-based testing |
| Topic 6: Machine Learning Development Testing | 10% | - Testing ML development workflows - Testing MLOps and deployment pipelines - Regression testing for retrained models |
| Topic 7: Introduction to Testing AI-Based Systems | 10% | - Characteristics of AI and ML systems - Challenges in testing AI-based systems - AI-specific quality characteristics |
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NEW QUESTION # 105
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?
Answer: A
Explanation:
The syllabus states:
"When testing probabilistic and non-deterministic systems, the same input may produce different outputs.
Tests need to be run several times to produce statistically valid test results, ensuring that an appropriate number of answers are accurate." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.4, page 58 of 99)
NEW QUESTION # 106
Which ONE of the following is a factor associated with the test data that can create challenges specific to testing AI-based systems?
Answer: A
Explanation:
Obtaining appropriate data from users is a significant challenge when testing AI-based systems, as user-provided data may be noisy, unstructured, or highly variable. This can introduce complexities in testing the AI model's robustness and generalizability.
NEW QUESTION # 107
Which of the following is an example of a clustering problem that can be resolved by unsupervised learning?
Answer: D
Explanation:
The syllabus defines clustering as:
"Clustering: This is when the problem requires the identification of similarities in input data points that allows them to be grouped based on common characteristics or attributes. For example, clustering is used to categorize different types of customers for the purpose of marketing." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.2, page 26 of 99)
NEW QUESTION # 108
A mobile app start-up company is implementing an AI-based chat assistant for e-commerce customers. In the process of planning the testing, the team realizes that the specifications are insufficient.
Which testing approach should be used to test this system?
Answer: D
NEW QUESTION # 109
Which of the following technologies for implementing AI is considered to be a reasoning technique?
Answer: C
Explanation:
The ISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning.
Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options--Linear regression,Random Forest, and Genetic algorithms--are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus, Deductive classifiers (Option A)is the only technology classified as a reasoning technique.
NEW QUESTION # 110
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