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ISTQB CT-AI Exam Syllabus Topics:
Topic
Details
Topic 1
- Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 2
- 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 3
- Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 4
- 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 5
- Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 6
- 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 7
- 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 8
- 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 9
- systems from those required for conventional systems.
Topic 10
- 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.
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ISTQB Certified Tester AI Testing Exam Sample Questions (Q55-Q60):
NEW QUESTION # 55
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION
- A. Test for human handover requiring mandatory relinquishing control.
- B. Test for human handover when it should actually not be relinquishing control.
- C. Test for human handover to give rest to the system.
- D. Test for human handover after a given time interval.
Answer: B
Explanation:
* Testing Autonomy: Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Sections on Testing Autonomous AI-Based Systems and Testing for Human-AI Interaction.
NEW QUESTION # 56
A word processing company is developing an automatic text correction tool. A machine learning algorithm was used to develop the auto text correction feature. The testers have discovered when they start typing "Isle of Wight" it fills in "Isle of Eight". Several UAT testers have accepted this change without noticing. What type of bias is this?
- A. Geographical/Locality
- B. Ignorance/Cognitive
- C. Automation/Complacency
- D. Complacency/Disregard
Answer: C
Explanation:
Automation bias, also known as complacency bias, occurs when humans over-rely on automated systems and fail to question or validate the system's output. In this scenario, the auto-text correction feature of the word processing tool incorrectly suggests "Isle of Eight" instead of "Isle of Wight." The issue arises because multiple UAT testers accept the incorrect suggestion without noticing it, demonstrating a reliance on the AI- based system rather than their own judgment.
Automation bias is commonly seen in:
* Text correction systems, where users accept incorrect suggestions without verifying them.
* Medical diagnosis AI tools, where doctors may rely too much on AI recommendations.
* Autonomous driving systems, where drivers become overly dependent on automation and fail to react in critical situations.
* Section 7.4 - Testing for Automation Bias in AI-Based Systemsexplains that automation bias occurs when people accept AI-generated outputs without verifying them, often leading to incorrect decisions.
Reference from ISTQB Certified Tester AI Testing Study Guide:
NEW QUESTION # 57
Consider an AI system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?
- A. Black-box testing eliminates the need for the tester to understand the internal structure of the AI system.
- B. Test automation can be built quickly and easily from the test cases developed during black-box testing.
- C. The tester wishes to better understand the logic of the software used to create the internal structure.
- D. The black-box testing method will allow the tester to check the transparency of the algorithm used to create the internal structure.
Answer: A
Explanation:
In AI-based systems, particularly those where theinternal structure has been generated by another software system, the complexity often makes it difficult for human testers to analyze the inner workings. As per the ISTQB Certified Tester AI Testing (CT-AI) Syllabus:
* Black-box testingis particularly useful when dealing with AI systems that have been generated by another system because:
* It allows testingwithout requiring knowledge of the internal logic.
* The AI model may be too complex for human testers to comprehend, making white-box testing ineffective.
* Black-box testing evaluates theinputs and outputs, ensuring functional correctnesswithout needing insight into how the system reaches a decision.
* Why other options are incorrect?
* A (Test automation and black-box testing): While automation is possible,black-box testing is not primarily about automationbut aboutabstracting the internal complexity.
* B (Understanding the logic of the software): This contradicts the premise of black-box testing, which is designed totest functionality without needing to understandthe inner workings.
* C (Checking transparency of the algorithm):Black-box testing does not check algorithm transparency-that would requirewhite-box testing or explainability techniques.
Thus, the best choice isOption D, as black-box testingremoves the need to analyze the internal structure of AI systems, making it the most appropriate testing method in this case.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 8.5 (Challenges Testing Complex AI-Based Systems)
* ISTQB CT-AI Syllabus v1.0, Section 8.6 (Testing the Transparency, Interpretability, and Explainability of AI-Based Systems)
NEW QUESTION # 58
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?
- A. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting a sufficient volume of input data.
- B. Run the test several times to ensure that the AI always returns the same correct test result.
- C. Run the test several times to generate a statistically valid test result to ensure that an appropriate number of answers are accurate.
- D. Decompose the system test into multiple data ingestion tests to determine if the AI system is getting precise and accurate input data.
Answer: C
Explanation:
Probabilistic and non-deterministic AI-based systemsdo not always produce the same output for identical inputs. This makes traditional testing approaches ineffective. Instead, the best approach is torun tests multiple times and analyze results statistically.
* Statistical Validity:Running tests multiple times ensures that observed results are statistically significant. Instead of relying on a single test run,analyzing multiple iterations helps determine trends, probabilities, and outliers.
* Expected Result Tolerance:AI-based systems may produce different results within an acceptable range. Defining acceptable tolerances (e.g., "result must be within 2% of the optimal value") improves test effectiveness.
* A (Run Several Times for the Same Correct Result):AI systems are ofteninherently non- deterministicand may not return the exact same result every time. Expecting identical outputs contradicts the nature of these systems.
* B & C (Decomposing Tests into Data Ingestion Tests):While data ingestion quality is important, it does notdirectlysolve the issue of probabilistic test results. Statistical analysis is the key approach.
* ISTQB CT-AI Syllabus (Section 8.4: Challenges Testing Probabilistic and Non-Deterministic AI- Based Systems)
* "For probabilistic systems, running a test multiple times may be necessary to obtain a statistically valid test result.".
* "Where a single definitive output is not possible, results should be analyzed statistically rather than relying on individual test cases.".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:Sinceprobabilistic AI systems do not always return the same result, the best approach is torun multiple test iterations and validate results statistically. Hence, thecorrect answer is D.
NEW QUESTION # 59
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I . Pairwise testing of combinations
II . Testing each individual model for accuracy
III . A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
- A. Only II
- B. I and II
- C. I and III
- D. Only III
Answer: B
Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
Reference:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.
NEW QUESTION # 60
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