ISTQB CT-AI Exam With Confidence Using Practice Dumps

Bias in AI models often originates fromincomplete or non-representative training data. In this case, if the training datasetpurposely excludes specific faulty conditions, the machine learning model willfail to learn and detectthese conditions in real-world scenarios.

This results in:

Sample bias, where the training data is not fully representative of all possible faulty conditions.

Algorithmic bias, where the model prioritizes certain defect types while ignoring others.

B. Selecting training data by purposely including all known faulty conditions→ This would help reduce bias by improving model generalization.

C. Selecting testing data from a different dataset than the training dataset→ This is a good practice to evaluate model generalization but does not inherently introduce bias.

D. Selecting testing data from a boat manufacturer's bolt longevity data→ While using unrelated data can createpoor model accuracy, it does not directly introduce bias unless systematic patterns in the incorrect dataset lead to unfair decision-making.

Section 8.3 - Testing for Algorithmic, Sample, and Inappropriate Biasstates thatsample bias can occur if the training dataset is not fully representative of the expected data space, leading to biased predictions​.

Why are the other options incorrect?Reference from ISTQB Certified Tester AI Testing Study Guide:

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

Pre-processing is an essential step in data preparation that ensures data is clean, formatted correctly, and structured for effective machine learning (ML) model training. One common issue that can be resolved during pre-processing isnumbers stored as strings.

Explanation of Answer Choices:

Option A: Insufficient data

Incorrect. Pre-processing cannot resolve insufficient data. If data is lacking, techniques like data augmentation or external data collection are needed​.

Option B: Invalid data

Incorrect. While pre-processing can identify and handle some forms of invalid data (e.g., missing values, duplicate entries), it does not resolve all invalid data issues. Some cases may require domain expertise to determine validity​.

Option C: Wanted outliers

Incorrect. Pre-processing usually focuses on handling unwanted outliers. Wanted outliers may need to be preserved, which is more of a data selection decision rather than pre-processing​.

Option D: Numbers stored as strings

Correct. One of the key functions of data pre-processing isdata transformation, which includes converting incorrectly formatted data types, such as numbers stored as strings, into their correct numerical format​.

ISTQB CT-AI Syllabus References:

Data Pre-Processing Steps:"Transformation: The format of the given data is changed (e.g., breaking an address held as a string into its constituent parts, dropping a field holding a random identifier, converting categorical data into numerical data, changing image formats)"​.