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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions (Q29-Q34):

NEW QUESTION # 29
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

  • A. Machine learning on logs of execution
  • B. GUI analysis by computer vision
  • C. Analyzing source code for generating test cases
  • D. Natural language processing on textual requirements

Answer: D

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.
References:This aligns with the methodology discussed in the syllabus under the section on using AI for generating test cases from textual requirements.


NEW QUESTION # 30
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION

  • A. These examples are model specific and are not likely to cause another model trained on same task to fail.
  • B. These attacks can't be prevented by retraining the model with these examples augmented tothe training data.
  • C. Black box attacks based on adversarial examples create an exact duplicate model of the original.
  • D. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.

Answer: A

Explanation:
* A. Black box attacks based on adversarial examples create an exact duplicate model of the original.
* Black box attacks do not create an exact duplicate model. Instead, they exploit the model by querying it and using the outputs to craft adversarial examples without knowledge of the internal workings.
* B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
* Adversarial examples typically cause the model to predict the incorrect class rather than just reducing accuracy. These examples are designed to be visually indistinguishable from the original image but lead to incorrect classifications.
* C. These attacks can't be prevented by retraining the model with these examples augmented to the training data.
* This statement is incorrect because retraining the model with adversarial examples included in the training data can help the model learn to resist such attacks, a technique known as adversarial training.
* D. These examples are model specific and are not likely to cause another model trained on the same task to fail.
* Adversarial examples are often model-specific, meaning that they exploit the specific weaknesses of a particular model. While some adversarial examples might transfer between models, many are tailored to the specific model they were generated for and may not affect other models trained on the same task.
Therefore, the correct answer isDbecause adversarial examples are typically model-specific and may not cause another model trained on the same task to fail.


NEW QUESTION # 31
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION

  • A. Al systems require changing of operational environments; therefore, flexibility is required.
  • B. Al systems are inherently flexible.
  • C. Self-learning systems are expected to deal with new situations without explicitly having to program for it.
  • D. Flexible Al systems allow for easier modification of the system as a whole.

Answer: D

Explanation:
Flexibility in AI systems is crucial for various reasons, particularly because it allows for easier modification and adaptation of the system as a whole.
* AI systems are inherently flexible (A): This statement is not correct. While some AI systems may be designed to be flexible, they are not inherently flexible by nature. Flexibility depends on the system's design and implementation.
* AI systems require changing operational environments; therefore, flexibility is required (B): While it's true that AI systems may need to operate in changing environments, this statement does not directly address the importance of flexibility for the modification of the system.
* Flexible AI systems allow for easier modification of the system as a whole (C): This statement correctly describes the importance of flexibility. Being able to modify AI systems easily is critical for their maintenance, adaptation to new requirements, and improvement.
* Self-learning systems are expected to deal with new situations without explicitly having to program for it (D): This statement relates to the adaptability of self-learning systems rather than their overall flexibility for modification.
Hence, the correct answer isC. Flexible AI systems allow for easier modification of the system as a whole.
References:
* ISTQB CT-AI Syllabus Section 2.1 on Flexibility and Adaptability discusses the importance of flexibility in AI systems and how it enables easier modification and adaptability to new situations.
* Sample Exam Questions document, Question #30 highlights the importance of flexibility in AI systems.


NEW QUESTION # 32
Max. Score: 2
Al-enabled medical devices are used nowadays for automating certain parts of the medical diagnostic processes. Since these are life-critical process the relevant authorities are considenng bringing about suitable certifications for these Al enabled medical devices. This certification may involve several facets of Al testing (I - V).
I.Autonomy
II.Maintainability
III.Safety
IV.Transparency
V.Side Effects
Which ONE of the following options contains the three MOST required aspects to be satisfied for the above scenario of certification of Al enabled medical devices?
SELECT ONE OPTION

  • A. Aspects II, III and IV
  • B. Aspects I, II, and III
  • C. Aspects I, IV, and V
  • D. Aspects III, IV, and V

Answer: D

Explanation:
For AI-enabled medical devices, the most required aspects for certification are safety, transparency, and side effects. Here's why:
* Safety (Aspect III): Critical for ensuring that the AI system does not cause harm to patients.
* Transparency (Aspect IV): Important for understanding and verifying the decisions made by the AI system.
* Side Effects (Aspect V): Necessary to identify and mitigate any unintended consequences of the AI system.
Why Not Other Options:
* Autonomy and Maintainability (Aspects I and II): While important, they are secondary to the immediate concerns of safety, transparency, and managing side effects in life-critical processes.
References:This explanation is aligned with the critical quality characteristics for AI-based systems as mentioned in the ISTQB CT-AI syllabus, focusing on the certification of medical devices.


NEW QUESTION # 33
Which ONE of the following approaches to labelling requires the least time and effort?
SELECT ONE OPTION

  • A. Al-Assisted
  • B. Internal
  • C. Pre-labeled dataset
  • D. Outsourced

Answer: C

Explanation:
Labelling Approaches:Among the options provided, pre-labeled datasets require the least time and effort because the data has already been labeled, eliminating the need for further manual or automated labeling efforts.
Reference:ISTQB_CT-AI_Syllabus_v1.0, Section 4.5 Data Labelling for Supervised Learning, which discusses various approaches to data labeling, including pre-labeled datasets, and their associated time and effort requirements.


NEW QUESTION # 34
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