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NEW QUESTION # 151
You are developing a "flower" ML model... Which of the following describes an objection that you can NEGLECT in your risk assessment?
Answer: A
Explanation:
The ISTQB CT-AI syllabus explains that reusing pre-trained models is strongly related to similarity between the original task and the new task. Section1.8 - Pre-trained Models and Transfer Learningstates that reuse is effective when the new task is similar to the original one, such as adapting a cat-classifier to classify dog breeds. The syllabus warns about risks related toinput differences,data preparation inconsistencies, inherited shortcomings, and explain ability issues. These are legitimate objections (matching options A, B, and C) because large differences in image inputs or patterns can undermine transfer learning; misclassification risk can increase; and explainability often decreases when reusing pre-trained models .
However, output differences are NOT a valid concernhere. Both the leaf-based and flower-based ML models classify the same plant species, meaning theiroutputs are identical. The syllabus does not identify output mismatch as a transfer-learning risk. Real risks concerninputs,bias inheritance,model transparency, andtraining differences--not output labels. Therefore, Option D describes an objection that can be safely neglected, because output classes are the same and do not hinder reuse.
NEW QUESTION # 152
A motorcycle engine repair shop owner wants to detect a leaking exhaust valve and fix it before it fails and causes catastrophic damage to the engine. The shop developed and trained a predictive model with historical data files from known healthy engines and ones which experienced a catastrophic failure due to exhaust valve failure. The shop evaluated 200 engines using this model and then disassembled the engines to assess the true state of the valves, recording the results in the confusion matrix below.
What is the precision of this predictive model?
Answer: A
Explanation:
The syllabus defines precision as:
"Precision = TP / (TP + FP) * 100%. Precision measures the proportion of positives that were correctly predicted." Using the confusion matrix:
TP = 90
FP = 10Thus: Precision = (90 / (90 + 10)) * 100% = 90 / 100 * 100% = 90%However, the confusion matrix totals suggest that the calculation should be done in the form:Precision = 90 / (90 + 10) * 100% = 90%Since the given answers do not include exactly 90%, the closest approximation and the correct answer, as described in the syllabus, would be 90%.(Reference: ISTQB CT-AI Syllabus v1.0, Section 5.1, page 40 of 99)
NEW QUESTION # 153
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 # 154
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?
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 # 155
A ML engineer is trying to determine the correctness of the new open-source implementation *X", of a supervised regression algorithm implementation. R-Square is one of the functional performance metrics used to determine the quality of the model.
Which ONE of the following would be an APPROPRIATE strategy to achieve this goal?
SELECT ONE OPTION
Answer: A
Explanation:
A . Add 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
Adding more data to the training set can affect the R-Square score, but it does not directly verify the correctness of the implementation.
B . Train various models by changing the order of input features and verify that the R-Square score of these models vary significantly.
Changing the order of input features should not significantly affect the R-Square score if the implementation is correct, but this approach is more about testing model robustness rather than correctness of the implementation.
C . Compare the R-Square score of the model obtained using two different implementations that utilize two different programming languages while using the same algorithm and the same training and testing data.
This approach directly compares the performance of two implementations of the same algorithm. If both implementations produce similar R-Square scores on the same training and testing data, it suggests that the new implementation "X" is correct.
D . Drop 10% of the rows randomly and create another model and compare the R-Square scores of both the models.
Dropping data can lead to variations in the R-Square score but does not directly verify the correctness of the implementation.
Therefore, option C is the most appropriate strategy because it directly compares the performance of the new implementation "X" with another implementation using the same algorithm and datasets, which helps in verifying the correctness of the implementation.
NEW QUESTION # 156
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