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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing Generative AI and Large Language Models | 15% | - Specific risks and test approaches for LLMs - Generative AI fundamentals - Evaluation of generative AI outputs |
| Topic 2: Introduction to Testing AI-Based Systems | 10% | - Characteristics of AI and ML systems - AI-specific quality characteristics - Challenges in testing AI-based systems |
| Topic 3: Machine Learning Development Testing | 10% | - Testing ML development workflows - Testing MLOps and deployment pipelines - Regression testing for retrained models |
| Topic 4: Test Approaches and Techniques for AI Systems | 10% | - Exploratory testing and red teaming - Risk-based testing - A/B testing and back-to-back testing - Test environment and tool considerations |
| Topic 5: Input Data Testing for Machine Learning Systems | 20% | - Data quality attributes - Testing for bias, representativeness, and completeness - Validation of data pipelines and preprocessing - Label verification and ground truth assessment |
| Topic 6: Model Testing for Machine Learning Systems | 20% | - Metamorphic and statistical testing - Explainability and transparency testing - Adversarial and robustness testing - Model performance and accuracy testing - Drift detection and monitoring |
| Topic 7: Test Levels and Machine Learning Systems | 15% | - Unit, integration, system, and acceptance testing for ML - Test strategies for AI projects - Testing across ML lifecycle stages |
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NEW QUESTION # 102
A car insurance company is using a new AI service to reward defensive driving behavior among its policyholders. The driving behavior is recorded in a rating number (score).
The AI service determines this score from the following input values:
Reference speed v_max in km/h
Average speed v_mean in km/h
Average acceleration a_pos in m/s²
Average braking deceleration a_neg in m/s²
The more defensive the driving behavior is (slow driving, low acceleration, low braking deceleration), the higher is the score.
Three initial test cases (Test 1 to Test 3) are used for testing the AI service. In addition, new test cases A-D are proposed.
Which of the new tests is NOT a follow-up test case for metamorphic testing?
Choose ONE option! (1 out of 4)
Answer: A
Explanation:
According to the ISTQB CT-AI syllabus,metamorphic testingworks by applyingmetamorphic relations (MRs): predictable input transformations that should lead to predictable output changes. From the initial test data, clear relations emerge for defensive driving scoring. The score increases when:
* v_mean decreases,
* a_pos decreases,
* a_neg becomes less negative,and decreases when the opposite occurs.
A valid metamorphic follow-up test must modify inputs in a direction consistent with at least one MR while keeping the expected output direction predictable.
Test Alowers v_mean compared to Test 1, with similar acceleration values. This directly satisfies the MR that lower speed # higher score.
Test Cincreases both acceleration and braking intensity compared to Test 2, making the reduced score range (30-70) consistent with more aggressive driving.
Test Dmodifies acceleration and braking magnitudes in ways consistent with Test 3's defensive-driving scoring boundaries.
Test B, however, changes multiple variables in contradictory directions:
* v_mean increases (worse)
* a_pos increases (worse)
* a_neg becomes less negative (better)
Because these changes conflict,the expected score trend becomes unpredictable, violating the premise of a metamorphic follow-up test.
ThusTest B cannot be considered a metamorphic follow-up, which makesOption Ccorrect.
NEW QUESTION # 103
Which of the following is one of the reasons for data mislabelling?
Answer: A
Explanation:
The syllabus lists multiple reasons for mislabelled data, including the lack of domain knowledge:
"Lack of required domain knowledge may lead to incorrect labelling."
(Reference: ISTQB CT-AI Syllabus v1.0, Section 4.5.2, page 38 of 99)
NEW QUESTION # 104
There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?
Answer: C
Explanation:
AI and ML models can play a significant role in optimizing defect resolution processes. According to the ISTQB Certified Tester AI Testing (CT-AI) Syllabus, ML models can be used toanalyze defect reports, prioritize critical defects, and assign defects to developersbased on historical defect resolution patterns.
The key AI applications for defect management include:
* Defect Categorization- NLP techniques can analyze defect reports and classify them based on metadata like severity and impact.
* Defect Prioritization- ML models trained on past defects can predict which issues are likely to cause failures, allowing teams toprioritizethe most critical issues.
* Defect Assignment- AI-based models can suggest which developers are best suited for specific defects, optimizing the resolution process based on past performance and specialization.
From the given answer choices:
* Option A (Automatic Prioritization)is useful but does not directlyreduce backlog efficientlyby considering developer expertise and workload balancing.
* Option C (Root Cause Analysis for Process Improvement)is along-term strategybut does not directly address backlog reduction.
* Option D (Defect Prediction for Testing Focus)helps preemptively identify issues but does not resolve the existing backlog.
Thus,Option Bis the best choice as it aligns with AI's capability toassign defects to the most suitable developersbased on historical data, ensuring efficient defect resolution and backlog reduction.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 11.2 (Using AI to Analyze Reported Defects)
* ISTQB CT-AI Syllabus v1.0, Section 11.5 (Using AI for Defect Prediction).
NEW QUESTION # 105
Which ONE of the following hardware is MOST suitable for implementing Al when using ML?
Answer: C
Explanation:
Matrix multiplication is a fundamental operation in many machine learning algorithms, especially in neural networks and deep learning. Hardware optimized for fast matrix multiplication, such as GPUs (Graphics Processing Units), is most suitable for implementing AI and ML because it can handle the parallel processing required for these operations efficiently.
NEW QUESTION # 106
An airline has created an ML model to project fuel requirements for future flights. The model imports weather data such as wind speeds and temperatures, calculates flight routes based on historical routings from air traffic control, and estimates loads from average passenger and baggage weights. The model performed within an acceptable standard for the airline throughout the summer but as winter set in, the load weights became less accurate. After some exploratory data analysis, it became apparent that luggage weights were higher in the winter than in summer.
Which of the following statements BEST describes the problem and how it could have been prevented?
Answer: D
Explanation:
The syllabus states:
"Concept drift occurs when the operational environment changes without the trained model changing correspondingly. The outputs of the model become less accurate and less useful. Therefore, the operational model should be regularly evaluated against its acceptance criteria." (Reference: ISTQB CT-AI Syllabus v1.0, Section 7.6, Page 54 of 99)
NEW QUESTION # 107
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