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Passing the ISTQB CT-AI certification exam is necessary for professional development, and employing real CT-AI Exam Dumps can assist applicants in reaching their professional goals. These actual CT-AI questions assist students in discovering areas in which they need improvement, boost confidence, and lower anxiety. Candidates will breeze through Certified Tester AI Testing Exam (CT-AI) certification examination with flying colors and advance to the next level of their jobs if they prepare with updated CT-AI exam questions.
NEW QUESTION # 153
A car insurance company is using a new AI service to reward defensive driving behavior among its policyholders. The driving behavior is recorded in a rating number (score).
The AI service determines this score from the following input values:
Reference speed v_max in km/h
Average speed v_mean in km/h
Average acceleration a_pos in m/sยฒ
Average braking deceleration a_neg in m/sยฒ
The more defensive the driving behavior is (slow driving, low acceleration, low braking deceleration), the higher is the score.
Three initial test cases (Test 1 to Test 3) are used for testing the AI service. In addition, new test cases A-D are proposed.
Which of the new tests is NOT a follow-up test case for metamorphic testing?
Choose ONE option! (1 out of 4)
Answer: B
Explanation:
According to the ISTQB CT-AI syllabus,metamorphic testingworks by applyingmetamorphic relations (MRs): predictable input transformations that should lead to predictable output changes. From the initial test data, clear relations emerge for defensive driving scoring. The score increases when:
* v_mean decreases,
* a_pos decreases,
* a_neg becomes less negative,and decreases when the opposite occurs.
A valid metamorphic follow-up test must modify inputs in a direction consistent with at least one MR while keeping the expected output direction predictable.
Test Alowers v_mean compared to Test 1, with similar acceleration values. This directly satisfies the MR that lower speed # higher score.
Test Cincreases both acceleration and braking intensity compared to Test 2, making the reduced score range (30-70) consistent with more aggressive driving.
Test Dmodifies acceleration and braking magnitudes in ways consistent with Test 3's defensive-driving scoring boundaries.
Test B, however, changes multiple variables in contradictory directions:
* v_mean increases (worse)
* a_pos increases (worse)
* a_neg becomes less negative (better)
Because these changes conflict,the expected score trend becomes unpredictable, violating the premise of a metamorphic follow-up test.
ThusTest B cannot be considered a metamorphic follow-up, which makesOption Ccorrect.
NEW QUESTION # 154
A robotic AI-based system is being built by a logistics company to operate within its unmanned warehouses. The warehouses can all be very different and new ones are being added each year.
It is expected that the system will not require retraining for each warehouse, and will be able to learn the location of different items and move them to specified locations on request.
Which ONE of the following attributes should be MOST carefully considered when specifying the objectives and acceptance criteria for the system?
Answer: D
Explanation:
Adaptability is the most important attribute to consider in this scenario. Since the system will operate in different warehouses, each with potentially different layouts and configurations, it must be adaptable to various environments without needing retraining. The system should be able to learn and adjust to the specific characteristics of each warehouse, ensuring it functions effectively in all locations.
NEW QUESTION # 155
You have access to the training data that was used to train an AI-based system. You can review this information and use it as a guideline when creating your tests. What type of characteristic is this?
Answer: A
Explanation:
AI-based systems can sometimes behave like black boxes, where the internal decision-making process is unclear. Transparency refers to the ability to inspect and understand the training data, algorithms, and decision-making process of the AI system.
Transparency ensures that testers and stakeholders can review how an AI system was trained.
Access to training data is a key factor in transparency because it allows testers to analyze biases, completeness, and representativeness of the dataset.
Transparency is an essential characteristic of explainable AI (XAI). Having access to training data means that testers can investigate how data influences AI behavior.
Regulatory and ethical AI guidelines emphasize transparency. Many AI ethics frameworks, such as GDPR and Trustworthy AI guidelines, recommend transparency to ensure fair and explainable AI decision-making.
NEW QUESTION # 156
A test engineer is planning the best functional performance metrics to evaluate an unsupervised learning model. The model groups data points based on their similarity. The test engineer wants to measure how similar the data points in each group actually are.
Which is the MOST likely metric they should use?
Answer: A
Explanation:
The most appropriate metric for evaluating the similarity of data points within each group in an unsupervised learning model is intra-cluster. This metric measures how similar the data points within each cluster are to one another. The goal is to have high intra-cluster similarity, meaning the data points within a group should be similar to each other.
NEW QUESTION # 157
How can a tester check the system for bias as part of a review of data sources, acquisition, and preprocessing?
Answer: C
Explanation:
Bias detection at thedata levelis performed by reviewing data acquisition and preprocessing steps, as explained in Section2.3 - Data Quality and Biasof the ISTQB CT-AI syllabus. Sample bias arises when data is distorted or when preprocessing introduces unintended shifts--for example, by filtering, normalization, or labeling steps that disproportionately affect subsets of the data. Option B correctly reflects this: reviewers can identify whether preprocessing steps have altered the dataset in a way that introduces sample distortions. This aligns perfectly with syllabus guidance on reviewing data pipelines for bias sources.
NEW QUESTION # 158
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