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ISQI ISTQB Certified Tester AI Testing (v1.0) Sample Questions (Q30-Q35):

NEW QUESTION # 30
Which of the following is THE LEAST appropriate tests to be performed for testing a feature related to autonomy?
SELECT ONE OPTION

A. Test for human handover after a given time interval.
B. Test for human handover when it should actually not be relinquishing control.
C. Test for human handover requiring mandatory relinquishing control.
D. Test for human handover to give rest to the system.

Answer: B

Explanation:
Testing Autonomy:Testing for human handover when it should not be relinquishing control is the least appropriate because it contradicts the very definition of autonomous systems. The other tests are relevant to ensuring smooth operation and transitions between human and AI control.
Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Testing Autonomous AI-Based Systems and Testing for Human-AI Interaction.

NEW QUESTION # 31
Which ONE of the following options does NOT describe a challenge for acquiring test data in ML systems?
SELECT ONE OPTION

A. Data for the use case is being generated at a fast pace.
B. Compliance needs require proper care to be taken of input personal data.
C. Nature of data constantly changes with lime.
D. Test data being sourced from public sources.

Answer: A

Explanation:
Challenges for Acquiring Test Data in ML Systems:Compliance needs, the changing nature of data over time, and sourcing data from public sources are significant challenges. Data being generated quickly is generally not a challenge; it can actually be beneficial as it provides more data for training and testing.
Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Preparation and Data Quality Issues.

NEW QUESTION # 32
Which ONE of the following options BEST DESCRIBES clustering?
SELECT ONE OPTION

A. Clustering requires you to know the classes.
B. Clustering is supervised learning.
C. Clustering is done without prior knowledge of output classes.
D. Clustering is classification of a continuous quantity.

Answer: C

Explanation:
Clustering is a type of machine learning technique used to group similar data points into clusters. It is a key concept in unsupervised learning, where the algorithm tries to find patterns or groupings in data without prior knowledge of output classes. Let's analyze each option:
* A. Clustering is classification of a continuous quantity.
* This is incorrect. Classification typically involves discrete categories, whereas clustering involves grouping similar data points. Classification of continuous quantities is generally referred to as regression.
* B. Clustering is supervised learning.
* This is incorrect. Clustering is an unsupervised learning technique because it does not rely on labeled data.
* C. Clustering is done without prior knowledge of output classes.
* This is correct. In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.
* D. Clustering requires you to know the classes.
* This is incorrect. Clustering does not require prior knowledge of classes. Instead, it aims to identify and form the classes or groups based on the data itself.
Therefore, the correct answer isCbecause clustering is an unsupervised learning technique done without prior knowledge of output classes.

NEW QUESTION # 33
Arihant Meditation is a startup using Al to aid people in deeper and better meditation based on analysis of various factors such as time and duration of the meditation, pulse and blood pressure, EEG patters etc. among others. Their model accuracy and other functional performance parameters have not yet reached their desired level.
Which ONE of the following factors is NOT a factor affecting the ML functional performance?
SELECT ONE OPTION

A. The data pipeline
B. The quality of the labeling
C. Biased data
D. The number of classes

Answer: D

Explanation:
Factors Affecting ML Functional Performance:The data pipeline, quality of the labeling, and biased data are all factors that significantly affect the performance of machine learning models. The number of classes, while relevant for the model structure, is not a direct factor affecting the performance metrics such as accuracy or bias.
Reference:ISTQB_CT-AI_Syllabus_v1.0, Sections on Data Quality and its Effect on the ML Model and ML Functional Performance Metrics.

NEW QUESTION # 34
Which ONE of the following statements is a CORRECT adversarial example in the context of machine learning systems that are working on image classifiers.
SELECT ONE OPTION

A. Black box attacks based on adversarial examples create an exact duplicate model of the original.
B. These attack examples cause a model to predict the correct class with slightly less accuracy even though they look like the original image.
C. These examples are model specific and are not likely to cause another model trained on same task to fail.
D. These attacks can't be prevented by retraining the model with these examples augmented tothe training data.

Answer: C

Explanation:
* 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.

NEW QUESTION # 35
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