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[General] CT-AI Reliable Exam Tips | CT-AI Valid Dumps Pdf

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【General】 CT-AI Reliable Exam Tips | CT-AI Valid Dumps Pdf

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ISTQB CT-AI Exam Syllabus Topics:
TopicDetails
Topic 1
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 2
  • Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.
Topic 3
  • Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 4
  • systems from those required for conventional systems.
Topic 5
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 6
  • Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q32-Q37):NEW QUESTION # 32
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not.
The bank has enough data on past customers to randomly split the data into a training dataset and a test
/validation dataset. A logistic regression model is constructed on the training dataset using the following independent variables:
* Gender
* Marital status
* Number of dependents
* Education
* Income
* Loan amount
* Loan term
* Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?
  • A. Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set. If the two models significantly differ, it will indicate there is bias in the original model.
  • B. Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation dataset ensuring no bias is present.
  • C. Experience-based testing should be used to confirm that the training data set is operationally relevant.
    This can include applying exploratory data analysis (EDA) to check for bias within the training data set.
  • D. A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.
Answer: C
Explanation:
The syllabus mentions that experience-based testing and EDA are effective for detecting biases:
"Experience-based testing can be used to verify that the training dataset is operationally relevant and identify potential sources of bias. EDA is also useful for exploring the data and understanding any relationships that might lead to bias in the model." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.3, page 58 of 99)

NEW QUESTION # 33
Which ONE of the following options does NOT describe an Al technology related characteristic which differentiates Al test environments from other test environments?
SELECT ONE OPTION
  • A. Challenges resulting from low accuracy of the models.
  • B. Challenges in the creation of scenarios of human handover for autonomous systems.
  • C. The challenge of mimicking undefined scenarios generated due to self-learning
  • D. The challenge of providing explainability to the decisions made by the system.
Answer: B
Explanation:
AI test environments have several unique characteristics that differentiate them from traditional test environments. Let's evaluate each option:
A . Challenges resulting from low accuracy of the models.
Low accuracy is a common challenge in AI systems, especially during initial development and training phases. Ensuring the model performs accurately in varied and unpredictable scenarios is a critical aspect of AI testing.
B . The challenge of mimicking undefined scenarios generated due to self-learning.
AI systems, particularly those that involve machine learning, can generate undefined or unexpected scenarios due to their self-learning capabilities. Mimicking and testing these scenarios is a unique challenge in AI environments.
C . The challenge of providing explainability to the decisions made by the system.
Explainability, or the ability to understand and articulate how an AI system arrives at its decisions, is a significant and unique challenge in AI testing. This is crucial for trust and transparency in AI systems.
D . Challenges in the creation of scenarios of human handover for autonomous systems.
While important, the creation of scenarios for human handover in autonomous systems is not a characteristic unique to AI test environments. It is more related to the operational and deployment challenges of autonomous systems rather than the intrinsic technology-related characteristics of AI .
Given the above points, option D is the correct answer because it describes a challenge related to operational deployment rather than a technology-related characteristic unique to AI test environments.

NEW QUESTION # 34
Which ONE of the following statements correctly describes the importance of flexibility for Al systems?
SELECT ONE OPTION
  • A. Flexible Al systems allow for easier modification of the system as a whole.
  • B. Al systems require changing of operational environments; therefore, flexibility is required.
  • C. Al systems are inherently flexible.
  • D. Self-learning systems are expected to deal with new situations without explicitly having to program for it.
Answer: A
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 # 35
"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
  • A. Maximize accuracy and recall
  • B. Maximize recall and precision
  • C. Maximize specificity number of classes
  • D. Maximize precision and accuracy
Answer: B
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.

NEW QUESTION # 36
You have been developing test automation for an e-commerce system. One of the problems you are seeing is that object recognition in the GUI is having frequent failures. You have determined this is because the developers are changing the identifiers when they make code updates.
How could AI help make the automation more reliable?
  • A. It could dynamically name the objects, altering the source code, so the object names will match the object names used in the automation.
  • B. It could generate a model that will anticipate developer changes and pre-alter the test automation code accordingly.
  • C. It could identify the objects multiple ways and then determine the most commonly used and stable identification for each object.
  • D. It could modify the automation code to ignore unrecognizable objects to avoid failures.
Answer: C
Explanation:
Object recognition issues in test automation often arise whendevelopers frequently change object identifiers in the GUI. AI can enhance the stability of GUI automation by:
* Using multiple criteria for object identification
* AI cantrack UI elements using multiple attributessuch asXPath, label, ID, class, and screen coordinatesrather than relying on a single identifier that may change over time.
* This approach makes the automationless brittle and more adaptive to changes in the UI.
* Why other options are incorrect?
* B (Ignore unrecognizable objects to avoid failures): Ignoring objects instead of identifying them properly wouldlead to incomplete or incorrect test execution.
* C (Dynamically name objects and alter source code): AI-based testing tools donot modify application source code; they work byadjusting the recognition strategy.
* D (Anticipate developer changes and pre-alter automation code): While AI can adapt,it does not predict future changes to the GUI, making this option unrealistic.
Thus,Option A is the best answer, as AI tools enhance object recognitionby dynamically selecting the most stable and persistent identification methods, improving test automation reliability.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 11.6.1 (Using AI to Test Through the Graphical User Interface (GUI))
* ISTQB CT-AI Syllabus v1.0, Section 11.6.2 (Using AI to Test the GUI).

NEW QUESTION # 37
......
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