ISTQB Certified Tester: AI Testing Practice Mode

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  - 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: