CT-AI Questions Bank

 A. This statement is incorrect. Current AI is true AI and there is no reason to believe that this fact will change over time.

AI is an evolving field, and the definition of what constitutes AI can change as technology advances.

 B. This statement is correct. In general, what is considered AI today may change over time.

The term AI is dynamic and has evolved over the years. What is considered AI today might be viewed as standard computing in the future. Historically, as technologies become mainstream, they often cease to be considered "AI".

 C. This statement is incorrect. What is considered AI today will continue to be AI even as technology evolves and changes.

This perspective does not account for the historical evolution of the definition of AI. As new technologies emerge, the boundaries of AI shift.

 D. This statement is correct. In general, today the term AI is utilized incorrectly.

While some may argue this, it is not a universal truth. The term AI encompasses a broad range of technologies and applications, and its usage is generally consistent with current technological capabilities.

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:

Detecting biases in the ML pipeline involves various tests to ensure fairness and accuracy throughout the ML process.

Testing the distribution shift in the training data for inappropriate bias (A): This involves checking if there is any shift in the data distribution that could lead to bias in the model. It is an important test but not the most direct method for detecting biases.

Test the model during model evaluation for data bias (B): This is a critical stage where the model is evaluated to detect any biases in the data it was trained on. It directly addresses potential data biases in the model.

Testing the data pipeline for any sources for algorithmic bias (C): This test is crucial as it helps identify biases that may originate from the data processing and transformation stages within the pipeline. Detecting sources of algorithmic bias ensures that the model does not inherit biases from these processes.

Check the input test data for potential sample bias (D): While this is an important step, it focuses more on the input data and less on the overall data pipeline.

Hence, the most likely useful test to help detect different kinds of biases in the ML pipeline isB. Test the model during model evaluation for data bias.

References:

ISTQB CT-AI Syllabus Section 8.3 on Testing for Algorithmic, Sample, and Inappropriate Bias discusses various tests that can be performed to detect biases at different stages of the ML pipeline.

Sample Exam Questions document, Question #32 highlights the importance of evaluating the model for biases.

Clustering is a form ofunsupervised learning, which groups data points based onsimilarities without predefined labels. According toISTQB CT-AI Syllabus, clustering is used in scenarios where:

The objective is to find natural groupings in data.

The dataset does not have labeled outputs.

Patterns and structures need to be identified automatically​.

Analyzing the answer choices:

A. Associating shoppers with their shopping tendencies → Correct

Shoppers can be grouped based on purchasing behaviors(e.g., luxury shoppers vs. budget-conscious shoppers), which is a typical clustering application in market segmentation.

B. Grouping individual fish together based on their types of fins → Incorrect

If thetypes of fins are labeled, it becomes aclassification problem, which requires supervised learning.

C. Classifying muffin purchases based on packaging attractiveness → Incorrect

Classification, not clustering, because attractiveness scores or labels must be predefined.

D. Estimating the expected purchase of cat food after an ad campaign → Incorrect

This is a prediction task, best suited forregression models, which are part of supervised learning.

Thus,Option A is the best answer, asclusteringis used togroup shoppers based on tendencies without predefined labels​.

Certified Tester AI Testing Study Guide References:

ISTQB CT-AI Syllabus v1.0, Section 3.1.2 (Unsupervised Learning - Clustering and Association)

ISTQB CT-AI Syllabus v1.0, Section 3.3 (Selecting a Form of ML - Clustering)​.