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| Certification Vendor: | ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester AI Testing (CT-AI) Exam |
| Exam Number: | CT-AI |
| Exam Format: | Multiple Choice |
| Available Languages: | English |
| Real Exam Qty: | 40 |
| Exam Duration: | 60 minutes |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) |
| Passing Score: | 65% |
| Certificate Validity Period: | Lifetime |
| Recommended Training: | ISTQB Accredited Training Providers |
| Exam Registration: | ISTQB Official Website |
| Sample Questions: | ISTQB CT-AI Sample Questions |
| Exam Way: | Online and onsite proctored exam via accredited ISTQB examination providers |
| Pre Condition: | Recommended prior knowledge of ISTQB Foundation Level (CTFL) and basic understanding of software testing concepts and machine learning fundamentals. |
| Official Syllabus URL: | https://www.istqb.org |
If you are still unsure whether to pursue ISTQB CT-AI exam questions for ISTQB Certified Tester AI Testing Exam exam preparation, you are losing the game at the first stage in a fiercely competitive marketplace. ISTQB CT-AI Questions are the best option for becoming ISTQB Certified Tester AI Testing Exam.
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NEW QUESTION # 154
Written requirements are given in text documents, which ONE of the following options is the BEST way to generate test cases from these requirements?
SELECT ONE OPTION
Answer: B
Explanation:
When written requirements are given in text documents, the best way to generate test cases is by using Natural Language Processing (NLP). Here's why:
Natural Language Processing (NLP): NLP can analyze and understand human language. It can be used to process textual requirements to extract relevant information and generate test cases. This method is efficient in handling large volumes of textual data and identifying key elements necessary for testing.
Why Not Other Options:
Analyzing source code for generating test cases: This is more suitable for white-box testing where the code is available, but it doesn't apply to text-based requirements.
Machine learning on logs of execution: This approach is used for dynamic analysis based on system behavior during execution rather than static textual requirements.
GUI analysis by computer vision: This is used for testing graphical user interfaces and is not applicable to text-based requirements.
NEW QUESTION # 155
The following confusion matrix represents the functional performance of a classifier.
Which ONE of the following is the correct calculation for the accuracy of the classifier?
Answer: C
Explanation:
The correct answer is D . In the confusion matrix, the values are: true positive TP = 60 , false positive FP = 20
, false negative FN = 9 , and true negative TN = 11 . Accuracy measures the percentage of all classifications that are correct. The CT-AI syllabus defines the formula as: Accuracy = (TP + TN) / (TP + TN + FP + FN)
× 100% .
Substituting the given values gives: (60 + 11) / (60 + 11 + 20 + 9) × 100% = 71 / 100 × 100% = 71% .
Option A is mathematically invalid and does not apply the confusion matrix formula. Option B calculates recall-like behaviour using TP / (TP + FN) rather than accuracy. Option C calculates the F1-score using precision and recall, not accuracy. Option D is therefore the only option that correctly counts both forms of correct prediction: correctly predicted positives and correctly predicted negatives.
References/topics: CT-AI Syllabus Chapter 5, Section 5.1 "Confusion Matrix."
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NEW QUESTION # 156
Consider an AI system in which the complex internal structure has been generated by another software system. Why would the tester choose to do black-box testing on this particular system?
Answer: C
Explanation:
In AI-based systems, particularly those where theinternal structure has been generated by another software system, the complexity often makes it difficult for human testers to analyze the inner workings. As per the ISTQB Certified Tester AI Testing (CT-AI) Syllabus:
* Black-box testingis particularly useful when dealing with AI systems that have been generated by another system because:
* It allows testingwithout requiring knowledge of the internal logic.
* The AI model may be too complex for human testers to comprehend, making white-box testing ineffective.
* Black-box testing evaluates theinputs and outputs, ensuring functional correctnesswithout needing insight into how the system reaches a decision.
* Why other options are incorrect?
* A (Test automation and black-box testing): While automation is possible,black-box testing is not primarily about automationbut aboutabstracting the internal complexity.
* B (Understanding the logic of the software): This contradicts the premise of black-box testing, which is designed totest functionality without needing to understandthe inner workings.
* C (Checking transparency of the algorithm):Black-box testing does not check algorithm transparency-that would requirewhite-box testing or explainability techniques.
Thus, the best choice isOption D, as black-box testingremoves the need to analyze the internal structure of AI systems, making it the most appropriate testing method in this case.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 8.5 (Challenges Testing Complex AI-Based Systems)
* ISTQB CT-AI Syllabus v1.0, Section 8.6 (Testing the Transparency, Interpretability, and Explainability of AI-Based Systems)
NEW QUESTION # 157
A system was developed for screening the X-rays of patients for potential malignancy detection (skin cancer). A workflow system has been developed to screen multiple cancers by using several individually trained ML models chained together in the workflow.
Testing the pipeline could involve multiple kind of tests (I - III):
I . Pairwise testing of combinations
II . Testing each individual model for accuracy
III . A/B testing of different sequences of models
Which ONE of the following options contains the kinds of tests that would be MOST APPROPRIATE to include in the strategy for optimal detection?
SELECT ONE OPTION
Answer: C
Explanation:
The question asks which combination of tests would be most appropriate to include in the strategy for optimal detection in a workflow system using multiple ML models.
Pairwise testing of combinations (I): This method is useful for testing interactions between different components in the workflow to ensure they work well together, identifying potential issues in the integration.
Testing each individual model for accuracy (II): Ensuring that each model in the workflow performs accurately on its own is crucial before integrating them into a combined workflow.
A/B testing of different sequences of models (III): This involves comparing different sequences to determine which configuration yields the best results. While useful, it might not be as fundamental as pairwise and individual accuracy testing in the initial stages.
Reference:
ISTQB CT-AI Syllabus Section 9.2 on Pairwise Testing and Section 9.3 on Testing ML Models emphasize the importance of testing interactions and individual model accuracy in complex ML workflows.
NEW QUESTION # 158
Which ONE of the following statements about the hardware used to implement ML systems is MOST likely to be correct?
Answer: D
Explanation:
ML systems often require hardware that supports complex operations, such as matrix multiplications and other linear algebra computations. These operations are fundamental to many machine learning algorithms, particularly in deep learning. Specialist hardware (e.g., GPUs) may be used for efficiency, but complex operations are the core requirement.
NEW QUESTION # 159
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