ISTQB CT-AI Exam With Confidence Using Practice Dumps

A major risk when using apre-trained neural networkfor logo detection is that it mayinherit biases and defectsfrom the original dataset and training process. This means that the model could misidentify or fail to recognize certain logos due to:

Differences in data preparation:The original training data may have used a different preprocessing method than the new dataset, leading to inconsistencies.

Limited transparency:The exact details of the dataset and biases within it may not be known, which can cause unexpected behavior.

Bias in logo detection:If the model was trained on a dataset with certain color or text preferences, it may disproportionately misidentify logos with similar characteristics.

This inherited bias can result in:

False Positives:Recognizing other brand logos as the beer company's logo.

False Negatives:Failing to detect the actual logo when variations occur (e.g., different lighting or partial visibility).

Algorithmic Bias:The model may favor certain shapes or color contrasts due to biased training data.

Thus,the most appropriate risk associated with using this pre-trained model is inherited bias.

Section 1.8.3 - Risks of Using Pre-Trained Models and Transfer Learningexplains how pre-trained models may inheritbiases and undocumented defectsthat affect performance in a new environment​.

Reference from ISTQB Certified Tester AI Testing Study Guide:

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.

Adeep neural network (DNN)is a type of artificial neural network that consists of multiple layers between the input and output layers. TheISTQB Certified Tester AI Testing (CT-AI) Syllabusoutlines the following characteristics of a DNN:

Structure of a Deep Neural Network:

ADNN comprises at least three types of layers:

Input layer: Receives the input data.

Hidden layers: Perform complex feature extraction and transformations.

Output layer: Produces the final prediction or classification​.

Analysis of Answer Choices:

A (Only input and output layers)→ Incorrect, as a DNN must haveat least one hidden layer.

B (At least one internal hidden layer)→ Correct, as a neural network must havehidden layersto be considered deep.

C (Minimum of five layers required)→ Incorrect, asthere is no strict definitionthat requires at least five layers.

D (Output layer is not connected to other layers)→ Incorrect, asthe output layer must be connectedto the hidden layers​.

Thus,Option B is the correct answer, asa deep neural network must have at least one hidden layer.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 6.1 (Neural Networks and Deep Neural Networks)

ISTQB CT-AI Syllabus v1.0, Section 6.2 (Structure of Deep Neural Networks)​.