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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	ML: Data: This section of the exam covers explaining the activities and challenges related to data preparation. It also covers how to test datasets create an ML model and recognize how poor data quality can cause problems with the resultant ML model.
Topic 2	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 3	Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 4	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 5	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
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 (Q82-Q87):

NEW QUESTION # 82
A wildlife conservation group would like to use a neural network to classify images of different animals. The algorithm is going to be used on a social media platform to automatically pick out pictures of the chosen animal of the month. This month's animal is set to be a wolf. The test teamhas already observed that the algorithm could classify a picture of a dog as being a wolf because of the similar characteristics between dogs and wolves. To handle such instances, the team is planning to train the model with additional images of wolves and dogs so that the model is able to better differentiate between the two.
What test method should you use to verify that the model has improved after the additional training?

A. Adversarial testing to verify that no incorrect images have been used in the training.
B. Metamorphic testing because the application domain is not clearly understood at this point.
C. Back-to-back testing using the version of the model before training and the new version of the model after being trained with additional images.
D. Pairwise testing using combinatorics to look at a long list of photo parameters.

Answer: C

Explanation:
Back-to-back testing isused to compare two different versions of an ML model, which is precisely what is needed in this scenario.
* The model initiallymisclassified dogs as wolvesdue to feature similarities.
* Thetest team retrains the modelwith additional images of dogs and wolves.
* The best way to verify whether this additional trainingimproved classification accuracyis to compare theoriginal model's output with the newly trained model's output using the same test dataset.
* A (Metamorphic Testing):Metamorphic testing is useful forgenerating new test casesbased on existing ones but does not directly compare different model versions.
* B (Adversarial Testing):Adversarial testing is used to check how robust a model is againstmaliciously perturbed inputs, not to verify training effectiveness.
* C (Pairwise Testing):Pairwise testing is a combinatorial technique for reducing the number of test casesby focusing on key variable interactions, not for validating model improvements.
* ISTQB CT-AI Syllabus (Section 9.3: Back-to-Back Testing)
* "Back-to-back testing is used when an updated ML model needs to be compared against a previous version to confirm that it performs better or as expected".
* "The results of the newly trained model are compared with those of the prior version to ensure that changes did not negatively impact performance".
Why Other Options Are Incorrect:Supporting References from ISTQB Certified Tester AI Testing Study Guide:Conclusion:To verify that the model's performance improved after retraining,back-to-back testing is the most appropriate methodas it compares both model versions. Hence, thecorrect answer is D.

NEW QUESTION # 83
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 is C. Flexible AI systems allow for easier modification of the system as a whole.
Reference:
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 # 84
Upon testing a model used to detect rotten tomatoes, the following data was observed by the test engineer, based on certain number of tomato images.

For this confusion matrix which combinations of values of accuracy, recall, and specificity respectively is CORRECT?
SELECT ONE OPTION

A. 1,0.87,0.84
B. 1,0.9, 0.8
C. 0.87.0.9. 0.84
D. 0.84.1,0.9

Answer: C

Explanation:
To calculate the accuracy, recall, and specificity from the confusion matrix provided, we use the following formulas:
Confusion Matrix:
Actually Rotten: 45 (True Positive), 8 (False Positive)
Actually Fresh: 5 (False Negative), 42 (True Negative)
Accuracy:
Accuracy is the proportion of true results (both true positives and true negatives) in the total population.
Formula: Accuracy=TP+TNTP+TN+FP+FNtext{Accuracy} = frac{TP + TN}{TP + TN + FP + FN}Accuracy=TP+TN+FP+FNTP+TN Calculation: Accuracy=45+4245+42+8+5=87100=0.87text{Accuracy} = frac{45 + 42}{45 + 42 + 8 + 5} = frac{87}{100} = 0.87Accuracy=45+42+8+545+42=10087=0.87 Recall (Sensitivity):
Recall is the proportion of true positive results in the total actual positives.
Formula: Recall=TPTP+FNtext{Recall} = frac{TP}{TP + FN}Recall=TP+FNTP Calculation: Recall=4545+5=4550=0.9text{Recall} = frac{45}{45 + 5} = frac{45}{50} = 0.9Recall=45+545=5045=0.9 Specificity:
Specificity is the proportion of true negative results in the total actual negatives.
Formula: Specificity=TNTN+FPtext{Specificity} = frac{TN}{TN + FP}Specificity=TN+FPTN Calculation: Specificity=4242+8=4250=0.84text{Specificity} = frac{42}{42 + 8} = frac{42}{50} = 0.84Specificity=42+842=5042=0.84 Therefore, the correct combinations of accuracy, recall, and specificity are 0.87, 0.9, and 0.84 respectively.
Reference:
ISTQB CT-AI Syllabus, Section 5.1, Confusion Matrix, provides detailed formulas and explanations for calculating various metrics including accuracy, recall, and specificity.
"ML Functional Performance Metrics" (ISTQB CT-AI Syllabus, Section 5).

NEW QUESTION # 85
Which of the following is one of the reasons for data mislabelling?

A. Interoperability error
B. Expert knowledge
C. Lack of domain knowledge
D. Small datasets

Answer: C

Explanation:
Data mislabeling occurs for several reasons, which can significantly impact the performance of machine learning (ML) models, especially in supervised learning. According to the ISTQB Certified Tester AI Testing (CT-AI) syllabus, mislabeling of data can be caused by the following factors:
* Random errors by annotators- Mistakes made due to accidental misclassification.
* Systemic errors- Errors introduced by incorrect labeling instructions or poor training of annotators.
* Deliberate errors- Errors introduced intentionally by malicious data annotators.
* Translation errors- Occur when correctly labeled data in one language is incorrectly translated into another language.
* Subjectivity in labeling- Some labeling tasks require subjective judgment, leading to inconsistencies between different annotators.
* Lack of domain knowledge- If annotators do not have sufficient expertise in the domain, they may label data incorrectly due to misunderstanding the context.
* Complex classification tasks- The more complex the task, the higher the probability of labeling mistakes.
Among the answer choices provided, "Lack of domain knowledge" (Option A) is the best answer because expertise is essential to accurately labeling data in complex domains such as medical, legal, or engineering fields.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 4.5.2 (Mislabeled Data in Datasets)
* ISTQB CT-AI Syllabus v1.0, Section 4.3 (Dataset Quality Issues)

NEW QUESTION # 86
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 attacks can't be prevented by retraining the model with these examples augmented to the training data.
B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
C. These examples are model specific and are not likely to cause another model trained on same task to fail.
D. Black box attacks based on adversarial examples create an exact duplicate model of the original.

Answer: C

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 is D because adversarial examples are typically model-specific and may not cause another model trained on the same task to fail.

NEW QUESTION # 87
......

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