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| Section | Weight | Objectives |
|---|---|---|
| Input Data Testing for Machine Learning Systems | 20% | - Label verification and ground truth assessment - Validation of data pipelines and preprocessing - Testing for bias, representativeness, and completeness - Data quality attributes |
| Machine Learning Development Testing | 10% | - Testing ML development workflows - Testing MLOps and deployment pipelines - Regression testing for retrained models |
| 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 |
| Test Approaches and Techniques for AI Systems | 10% | - Exploratory testing and red teaming - Test environment and tool considerations - A/B testing and back-to-back testing - Risk-based testing |
| Introduction to Testing AI-Based Systems | 10% | - AI-specific quality characteristics - Challenges in testing AI-based systems - Characteristics of AI and ML systems |
| Model Testing for Machine Learning Systems | 20% | - Metamorphic and statistical testing - Model performance and accuracy testing - Drift detection and monitoring - Explainability and transparency testing - Adversarial and robustness testing |
| Testing Generative AI and Large Language Models | 15% | - Specific risks and test approaches for LLMs - Evaluation of generative AI outputs - Generative AI fundamentals |
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NEW QUESTION # 127
A team of software testers is attempting to create an AI algorithm to assist in software testing. This particular team has gone through over 40 iterations of testing and cannot afford to spend as much time as it takes to run the full regression test suite. They are hoping to have the algorithm reduce the amount of testing required thus reducing the time needed for each testing cycle.
How can an AI-based tool be expected to assist in this reduction?
Answer: A
Explanation:
AI-based tools can significantly optimize regression test suites by analyzing historical data, past test results, associated defects, and changes made to the software. These tools prioritize and select the most relevant test cases based on previous defect patterns and frequently failing features, which helps in reducing the test execution time while maintaining effectiveness.
The optimization process involves:
* Prioritizing test cases:AI-based tools rank test cases based on past defect detection trends, ensuring that the most relevant tests are executed first.
* Reducing redundant test cases:The tool can eliminate test cases that do not contribute significantly to defect detection, reducing overall test execution time.
* Augmenting test cases:The AI can also suggest new test cases if certain features are more prone to defects.
This approach has been proven to reduce regression test suite sizes by up to 50% while maintaining fault detection capabilities.
* Section 11.4 - Using AI for the Optimization of Regression Test Suitesstates that AI-based tools can optimize regression test suites by analyzing past test data and defect occurrences, leading to significant reductions in test execution time.
Reference from ISTQB Certified Tester AI Testing Study Guide:
NEW QUESTION # 128
A tourist calls an airline to book a ticket and is connected with an automated system which is able to recognize speech, understand requests related to purchasing a ticket, and provide relevant travel options.
When the tourist asks about the expected weather at the destination or potential impacts on operations because of the tight labor market the only response from the automated system is: "Idon't understand your question." This AI system should be categorized as?
Answer: B
Explanation:
Narrow AI (also known as Weak AI) is designed to perform specific tasks without possessing general intelligence or consciousness. The AI system in the question is capable of recognizing speech and responding to specific booking-related requests but fails when asked about unrelated topics (such as weather or labor markets).
* Option A:"General AI"
* Incorrect. General AI (AGI) refers to an AI system that can perform any intellectual task a human can. The described system is task-specific and does not exhibit general intelligence.
* Option B:"Narrow AI"
* Correct. The AI system is limited to a predefined domain (ticket booking) and cannot process unrelated questions. This is characteristic of Narrow AI, which excels at specific tasks but lacks broader cognitive abilities.
* Option C:"Super AI"
* Incorrect. Super AI surpasses human intelligence, exhibiting advanced reasoning and creativity.
The AI in the scenario is far from this level.
* Option D:"Conventional AI"
* Incorrect. Conventional AI is a broader term that may include rule-based systems. The described system relies on machine learning and natural language processing, making it more aligned with Narrow AI.
* Definition of Narrow AI:"Narrow AI refers to AI systems that are designed to perform a single task or a limited set of tasks, without general intelligence".
* General vs. Narrow AI:"General AI remains an area of research, while most current AI applications fall into the category of Narrow AI".
Analysis of the Answer Options:ISTQB CT-AI Syllabus References:Thus,option B is the correct categorization for the AI-based ticket booking system.
NEW QUESTION # 129
Which supervised-learning classification/regression statement is correct?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches
. It definesclassificationas predictingcategorical labels, whereasregressionpredictscontinuous numerical values. OptionB-deciding whether an object is a bicycle or a motorcycle-fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation .
Option A is incorrect because image recognition of a dog is aclassificationtask, not regression. Option C is incorrect because predicting a 10% price rise involves forecasting anumerical value, which is aregression problem. Option D is incorrect because classification can involveany number of classes, not only two.
Multiclass classification is explicitly mentioned in the syllabus.
Therefore, OptionBis the only answer aligned with the syllabus' definitions.
NEW QUESTION # 130
Which of the following describes the AI effect?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
TheAI Effectis clearly defined in theISTQB Certified Tester AI Testing Syllabus v1.0under Section1.1 - Definition of AI and AI Effect. The document explains that society's understanding of what qualifies as "AI" changes over time. Technologies once considered AI-such as expert systems from the 1970s and 1980s or early chess-playing systems-are no longer viewed as AI today. This phenomenon is explicitly labeled the"AI Effect,"described as"the changing perception of what constitutes AI."The syllabus states that as AI capabilities become routine or widely implemented, they often stop being perceived as true artificial intelligence .
Options B, C, and D do not capture this definition. While AI learning from data (B) is a property of ML, it does not describe the shifting perception of AI. Option C describes a technological achievement, not a perceptual shift. Option D references a historical AI milestone (Deep Blue defeating Kasparov) that the syllabus specifically uses as an example of technology that is no longer considered AI due to the AI Effect.
Therefore, onlyOption Aaccurately reflects the AI Effect as defined by the ISTQB syllabus.
NEW QUESTION # 131
Which of the following approaches would help overcome testing challenges associated with probabilistic and non-deterministic AI-based systems?
Answer: B
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 # 132
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