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The ISTQB CT-AI certification is on trending nowadays, and many IT aspirants are trying to get it. Success in the CT-AI test helps you land well-paying jobs. Additionally, the ISTQB CT-AI certification exam is also beneficial to get promotions in your current company. But the main problem that every applicant faces while preparing for the CT-AI Certification test is not finding updated ISTQB CT-AI practice questions.
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NEW QUESTION # 138
A test engineer is planning testing for a wearable medical device using AI. The medical device will detect possible heart issues in patients and dispatch emergency services automatically. It is not expected that many patients will have heart issues, and this is reflected in the available data. In this case, it is decided it is more important that emergency services are not sent un-necessarily, than it is to detect actual heart problems.
Which ONE of the following metrics should the test engineer choose to ensure that the emergency services are sent only when needed?
Answer: C
Explanation:
The correct answer is D. Precision . In this scenario, a positive prediction means that the system detects a possible heart issue and dispatches emergency services. The stated priority is to avoid sending emergency services unnecessarily. That means the test engineer must minimize false positives , because a false positive would incorrectly classify a patient as needing emergency assistance. The CT-AI syllabus states that precision is suitable when the cost of false positives is high and confidence in positive outcomes needs to be high.
Accuracy is a weak choice because the data is imbalanced: most patients are not expected to have heart issues, so a model could appear accurate while still making unacceptable emergency-dispatch errors. MSE and R- squared are regression metrics, not classification metrics, and the problem is a classification decision: dispatch or do not dispatch. Recall would be appropriate if the main concern were not missing true heart issues, but the question explicitly prioritizes avoiding unnecessary dispatches. Therefore, precision is the best functional performance metric for this acceptance objective.
References/topics: CT-AI Syllabus Chapter 5, Sections 5.1 and 5.4 "Selecting ML Functional Performance Metrics."
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NEW QUESTION # 139
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 # 140
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 # 141
While measuring the test coverage of a neural network, a test engineer wants to measure the number of neurons that have each output two activation function results with a minimum difference between the two results of 0.5.
Which ONE of the below coverage measures would achieve that goal?
Answer: C
Explanation:
Value-change coverage is a coverage measure that focuses on tracking the changes in the values of a neuron's output. In this case, the test engineer is interested in measuring the number of neurons that exhibit a difference of 0.5 between two activation function results, which is effectively a value change. This coverage measure ensures that the neuron undergoes a significant change in its output, fulfilling the engineer's goal.
NEW QUESTION # 142
Which ONE of the below statements BEST describes why test environments for autonomous systems might need to be different to other test environments?
Answer: B
Explanation:
Tools may be required to simulate extreme scenarios because autonomous systems often need to be tested under challenging or rare conditions that are difficult to replicate in a real-world environment. These scenarios, such as extreme weather, system failures, or unexpected behaviors, need to be simulated in a controlled environment to ensure the system can handle a wide range of situations safely and effectively. This makes the test environment for autonomous systems unique compared to other types of testing.
NEW QUESTION # 143
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