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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester - AI Testing Exam |
| Exam Number: | CT-AI |
| Related Certifications: | ISTQB Certified Tester Testing with Generative AI (CT-GenAI) ISTQB Certified Tester Foundation Level (CTFL) |
| Exam Duration: | 60 (75 for non-native language) |
| Available Languages: | German, Portuguese, Japanese, French, Spanish, English, Chinese, Korean |
| Real Exam Qty: | 40 |
| Passing Score: | 65% (29/44 points for v2.0; 31/47 points for v1.0) |
| Exam Price: | โฌ180 - โฌ250 (varies by region and provider) |
| Exam Format: | 1-2 points per question, Multiple-choice questions |
| Certificate Validity Period: | Valid indefinitely (no expiration) |
| Recommended Training: | CT-AI Syllabus v2.0 ISTQB Accredited Training Providers |
| Exam Registration: | iSQI Exam Registration ISTQB Official Registration Pearson VUE |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online remote proctored / Onsite test center |
| Pre Condition: | Must hold ISTQB Certified Tester Foundation Level (CTFL) certification |
| Official Syllabus URL: | https://istqb.org/certifications/certified-tester-ai-testing-ct-ai/ |
>> Certified ISTQB CT-AI Questions <<
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NEW QUESTION # 97
Which of the following statements about the structure and function of neural networks is true?
Choose ONE option (1 out of 4)
Answer: C
Explanation:
Section1.7 - Neural Networksof the ISTQB CT-AI syllabus explains that neural networks consist of neurons connected by weighted links. During training,learning occurs by adjusting the weights on these connections. This is the essence of gradient descent and backpropagation. Option B correctly states this behavior: only theweightsare modified, not the activation functions, neuron counts, or architectural structure.
Option A is incorrect because a neuron'sbiasis not determined by previous activations; it is an independent trainable parameter added to the weighted input sum. Option C is incorrect because the syllabus states that a single-layer perceptron is a valid type of neural network, although limited to linearly separable problems.
Option D is incorrect because no rule requires the number of input neurons to exceed or equal the number of output neurons. Instead, input neurons correspond to thenumber of features, while output neurons correspond totasks or classes.
Therefore,Option Bprecisely reflects the syllabus definition of what changes during neural network training.
NEW QUESTION # 98
"Splendid Healthcare" has started developing a cancer detection system based on ML. The type of cancer they plan on detecting has 2% prevalence rate in the population of a particular geography. It is required that the model performs well for both normal and cancer patients.
Which ONE of the following combinations requires MAXIMIZATION?
SELECT ONE OPTION
Answer: C
Explanation:
* Prevalence Rate and Model Performance:
* The cancer detection system being developed by "Splendid Healthcare" needs to account for the fact that the type of cancer has a 2% prevalence rate in the population. This indicates that the dataset is highly imbalanced with far fewer positive (cancer) cases compared to negative (normal) cases.
* Importance of Recall:
* Recall, also known as sensitivity or true positive rate, measures the proportion of actual positive cases that are correctly identified by the model. In medical diagnosis, especially cancer detection, recall is critical because missing a positive case (false negative) could have severe consequences for the patient. Therefore, maximizing recall ensures that most, if not all, cancer cases are detected.
* Importance of Precision:
* Precision measures the proportion of predicted positive cases that are actually positive. High precision reduces the number of false positives, meaning fewer people will be incorrectly diagnosed with cancer. This is also important to avoid unnecessary anxiety and further invasive testing for those who do not have the disease.
* Balancing Recall and Precision:
* In scenarios where both false negatives and false positives have significant consequences, it is crucial to balance recall and precision. This balance ensures that the model is not only good at detecting positive cases but also accurate in its predictions, reducing both types of errors.
* Accuracy and Specificity:
* While accuracy (the proportion of total correct predictions) is important, it can be misleading in imbalanced datasets. In this case, high accuracy could simply result from the model predicting the majority class (normal) correctly. Specificity (true negative rate) is also important, but for a cancer detection system, recall and precision take precedence to ensure positive cases are correctly and accurately identified.
* Conclusion:
* Therefore, for a cancer detection system with a low prevalence rate, maximizing both recall and precision is crucial to ensure effective and accurate detection of cancer cases.
This explanation aligns with the principles outlined in the ISTQB CT-AI Syllabus, particularly sections on performance metrics for ML models and handling imbalanced datasets (Chapter 5: ML Functional Performance Metrics).
NEW QUESTION # 99
Which statement regarding AI for defect prediction is correct?
Answer: A
Explanation:
Section5.3 - AI Support for Defect Predictionof the ISTQB CT-AI syllabus explains that AI-based defect prediction models rely onhistorical patterns, including past defects, code behavior, and similar system configurations. ML models trained on prior defect data can identifycomponents likely to contain defectswhen new changes resemble previous defect-inducing patterns. This directly supports Option A, which states that defect prediction is most effective when based on previous similar constellations.
NEW QUESTION # 100
Which ONE of the following is a factor associated with the test data that can create challenges specific to testing AI-based systems?
Answer: A
Explanation:
Obtaining appropriate data from users is a significant challenge when testing AI-based systems, as user-provided data may be noisy, unstructured, or highly variable. This can introduce complexities in testing the AI model's robustness and generalizability.
NEW QUESTION # 101
You are conducting a user acceptance test of a decision-support recommendation system used in a data processing business. In addition to testing the functional performance of the recommendation systems, what else would you be MOST likely to test?
Answer: C
Explanation:
In addition to testing the functional performance of the recommendation system, it is also important to test the amount of system resources consumed by each recommendation. This includes evaluating factors like processing time, memory usage, and the efficiency of the system when making recommendations. This ensures that the system performs well under the required load and is scalable for business use.
NEW QUESTION # 102
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