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Now they have become certified Certified Tester AI Testing Exam Certification Exam experts and pursue a rewarding career in the top world brands. You can also trust top-notch and easy-to-use ISTQB CT-AI practice test questions. The Certified Tester AI Testing Exam (CT-AI) exam questions are checked and verified by experienced and qualified Certified Tester AI Testing Exam (CT-AI) exam trainers. They have years of experience and knowledge to collect, design, and answer the real Certified Tester AI Testing Exam (CT-AI) exam questions.
NEW QUESTION # 131
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: A
NEW QUESTION # 132
Which AI-specific test objective and acceptance criterion should be selected MOST LIKELY for testing GPT_Legal?
Answer: C
Explanation:
The ISTQB CT-AI syllabus introduces AI-specific quality characteristics, including evolution, functional safety, compatibility, andbias-related data quality. Section5.1 - AI-Specific Test Objectives explains that evolution refers to an AI system's capability to continue improving or at least maintain performance as it undergoes additional training. GPT_Legal is explicitly described as aself-learning systemexpected to:
continuously reduce false positives,
achieve weekly accuracy improvements of 10%,
reach and maintain 90% accuracy,
adapt to new environments (patent law firm -> corporate legal department).
This aligns perfectly with the syllabus definition ofevidence of evolution: ensuring the model doesnot degradeas additional training data is introduced. Option B therefore directly supports the described acceptance criteria for this evolving, self-learning application.
NEW QUESTION # 133
You are evaluating the use of a highly accurate pre trained model that is used widely in industry tor a similar use case. There is an intention to apply transfer learning techniques to further customise the model. Which ONE of the following is the LEAST likely to be a significant risk with this approach?
Answer: C
Explanation:
The correct answer is D because the scenario explicitly reduces that risk: the model is described as highly accurate , widely used in industry, and applied to a similar use case. The CT-AI syllabus explains that using a pre-trained model can save training costs and largely eliminate the risk of it not working when an existing suitable model is reused. It also states that transfer learning effectiveness depends heavily on similarity between the original model's function and the new required function.
Options A, B, and C remain significant risks under the syllabus. A is credible because shortcomings such as inherited biases may pass from the pre-trained model to the reused system. B is credible because models created through transfer learning are highly likely to remain sensitive to vulnerabilities of the pre-trained model, including adversarial attacks. C is credible because differences in data preparation steps between original model development and later use may reduce functional performance. Therefore, the least likely significant risk is unexpectedly low functional performance of the already highly accurate, widely used, similar-purpose pre-trained model.
References/topics: CT-AI Syllabus Chapter 1, Sections 1.8.1-1.8.3 "Pre-Trained Models," "Transfer Learning," and "Risks of using Pre-Trained Models and Transfer Learning."
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NEW QUESTION # 134
Pairwise testing can be used in the context of self-driving cars for controlling an explosion in the number of combinations of parameters.
Which ONE of the following options is LEAST likely to be a reason for this incredible growth of parameters?
SELECT ONE OPTION
Answer: C
Explanation:
Pairwise testing is used to handle the large number of combinations of parameters that can arise in complex systems like self-driving cars. The question asks which of the given options isleast likelyto be a reason for the explosion in the number of parameters.
Different Road Types (A): Self-driving cars must operate on various road types, such as highways, city streets, rural roads, etc. Each road type can have different characteristics, requiring the car's system to adapt and handle different scenarios. Thus, this is a significant factor contributing to the growth of parameters.
Different Weather Conditions (B): Weather conditions such as rain, snow, fog, and bright sunlight significantly affect the performance of self-driving cars. The car's sensors and algorithms must adapt to these varying conditions, which adds to the number of parameters that need to be considered.
ML Model Metrics to Evaluate Functional Performance (C): While evaluating machine learning (ML) model performance is crucial, it does not directly contribute to the explosion of parameter combinations in the same way that road types, weather conditions, and car features do. Metrics are used to measure and assess performance but are not themselves variable conditions that the system must handle.
Different Features like ADAS, Lane Change Assistance, etc. (D): Advanced Driver Assistance Systems (ADAS) and other features add complexity to self-driving cars. Each feature can have multiple settings and operational modes, contributing to the overall number of parameters.
Hence, theleast likelyreason for the incredible growth in the number of parameters isC. ML model metrics to evaluate the functional performance.
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing discusses the application of this technique to manage the combinations of different variables in AI-based systems, including those used in self-driving cars.
Sample Exam Questions document, Question #29 provides context for the explosion in parameter combinations in self-driving cars and highlights the use of pairwise testing as a method to manage this complexity.
NEW QUESTION # 135
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION
Answer: B
Explanation:
Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.
Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.
Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.
Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.
Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.
Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.
ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.
Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.
NEW QUESTION # 136
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