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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing |
| Exam Number: | CT-AI |
| Passing Score: | 65% |
| Certificate Validity Period: | Lifetime (no expiration) |
| Exam Duration: | 60 minutes |
| Real Exam Qty: | 40 |
| Related Certifications: | ISTQB CTAL-TTA ISTQB CTFL |
| Available Languages: | English |
| Exam Format: | Multiple Choice |
| Exam Price: | EUR 250 |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online proctored or in-person at authorized testing centers |
| Pre Condition: | ISTQB CTFL (Certified Tester Foundation Level) certification is recommended but not mandatory |
| Official Syllabus URL: | https://www.istqb.org/certifications/artificial-intelligence-testing-certification |
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NEW QUESTION # 97
Which ONE of the following requirements for an AI based system is MOST likely to cause a challenge for testing?
Answer: A
Explanation:
The correct answer is C . A requirement that an AI-based system be 100% accurate with no tolerance for failure creates a major testing challenge because many AI-based systems are probabilistic and may require accuracy tolerances rather than absolute expected outcomes. The CT-AI syllabus states that the probabilistic nature of many AI-based systems can make it necessary to specify tolerances for quality requirements such as prediction accuracy.
Option A is also challenging because natural language interfaces increase flexibility and make it difficult to document all possible interactions, but it is not as absolute as requiring perfect accuracy. Option B is a conventional performance efficiency requirement and can normally be tested using timing and resource measurements. Option D relates to autonomy and may require scenario-based testing, but it is still testable if the operational envelope and failure conditions are well specified. The decisive issue with option C is that it provides no tolerance range, no probabilistic acceptance model, and no practical allowance for statistical uncertainty. This creates both a specification problem and a test oracle problem.
References/topics: CT-AI Syllabus Chapter 7, Section 7.1 "Specification of AI-Based Systems"; Chapter
8, Section 8.4 "Challenges Testing Probabilistic and Non-Deterministic AI-Based Systems."
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NEW QUESTION # 98
Which of the following is a technique used in machine learning?
Answer: C
Explanation:
Decision trees are a foundational algorithm used in supervised machine learning. The syllabus describes:
"A decision tree is a tree-like ML model whose nodes represent decisions and whose branches represent possible outcomes." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.4)
NEW QUESTION # 99
"AllerEgo" is a product that uses sell-learning to predict the behavior of a pilot under combat situation for a variety of terrains and enemy aircraft formations. Post training the model was exposed to the real-world data and the model was found to be behaving poorly. A lot of data quality tests had been performed on the data to bring it into a shape fit for training and testing.
Which ONE of the following options is least likely to describes the possible reason for the fall in the performance, especially when considering the self-learning nature of the Al system?
Answer: A
Explanation:
Defining criteria for improvement is a challenge in the acceptance of AI models, but it is not directly related to the performance drop in real-world scenarios. It relates more to the evaluation and deployment phase rather than affecting the model's real-time performance post-deployment.
NEW QUESTION # 100
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: A
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 # 101
The stakeholders of a machine learning model have confirmed that they understand the objective and purpose of the model, and ensured that the proposed model aligns with their business priorities. They have also selected a framework and a machine learning model that they will be using. What should be the next step to progress along the machine learning workflow?
Answer: C
Explanation:
The ML workflow typically involves iterative steps, beginning with data preparation once the model and framework are selected. The syllabus explains:
"The steps shown in Figure 1 (the ML workflow) do not include the integration of the ML model with the non- ML parts of the overall system. Typically, ML models cannot be deployed in isolation and need to be integrated with the non-ML parts... The next step would be data preparation as part of the ML workflow to provide input data to support training by an ML algorithm or prediction by an ML model." (Reference: ISTQB CT-AI Syllabus v1.0, Sections 3.2 & 4.1)
NEW QUESTION # 102
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