ISTQB Excellent Learning CT-AI Mode–Pass CT-AI First Attempt

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Testing AI-Specific Quality Characteristics: In this section, the topics covered are about the challenges in testing created by the self-learning of AI-based systems.
Topic 2	systems from those required for conventional systems.
Topic 3	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 4	ML Functional Performance Metrics: In this section, the topics covered include how to calculate the ML functional performance metrics from a given set of confusion matrices.
Topic 5	Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 6	Neural Networks and Testing: This section of the exam covers defining the structure and function of a neural network including a DNN and the different coverage measures for neural networks.
Topic 7	Introduction to AI: This exam section covers topics such as the AI effect and how it influences the definition of AI. It covers how to distinguish between narrow AI, general AI, and super AI; moreover, the topics covered include describing how standards apply to AI-based systems.
Topic 8	Methods and Techniques for the Testing of AI-Based Systems: In this section, the focus is on explaining how the testing of ML systems can help prevent adversarial attacks and data poisoning.
Topic 9	Quality Characteristics for AI-Based Systems: This section covers topics covered how to explain the importance of flexibility and adaptability as characteristics of AI-based systems and describes the vitality of managing evolution for AI-based systems. It also covers how to recall the characteristics that make it difficult to use AI-based systems in safety-related applications.
Topic 10	Testing AI-Based Systems Overview: In this section, focus is given to how system specifications for AI-based systems can create challenges in testing and explain automation bias and how this affects testing.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q150-Q155):

NEW QUESTION # 150
In a certain coffee producing region of Colombia, there have been some severe weather storms, resulting in massive losses in production. This caused a massive drop in stock price of coffee.
Which ONE of the following types of testing SHOULD be performed for a machine learning model for stock-price prediction to detect influence of such phenomenon as above on price of coffee stock.

A. Testing for concept drift
B. Testing for security
C. Testing for accuracy
D. Testing for bias

Answer: A

Explanation:
Type of Testing for Stock-Price Prediction Models: Concept drift refers to the change in the statistical properties of the target variable over time. Severe weather storms causing massive losses in coffee production and affecting stock prices would require testing for concept drift to ensure that the model adapts to new patterns in data over time.

NEW QUESTION # 151
A bank wants to use an algorithm to determine which applicants should be given a loan. The bank hires a data scientist to construct a logistic regression model to predict whether the applicant will repay the loan or not.
The bank has enough data on past customers to randomly split the data into a training dataset and a test
/validation dataset. A logistic regression model is constructed on the training dataset using the following independent variables:
* Gender
* Marital status
* Number of dependents
* Education
* Income
* Loan amount
* Loan term
* Credit score
The model reveals that those with higher credit scores and larger total incomes are more likely to repay their loans. The data scientist has suggested that there might be bias present in the model based on previous models created for other banks.
Given this information, what is the best test approach to check for potential bias in the model?

A. Acceptance testing should be used to make sure the algorithm is suitable for the customer. The team can re-work the acceptance criteria such that the algorithm is sure to correctly predict the remaining applicants that have been set aside for the validation dataset ensuring no bias is present.
B. A/B testing should be used to verify that the test data set does not detect any bias that might have been introduced by the original training data. If the two models significantly differ, it will indicate there is bias in the original model.
C. Back-to-back testing should be used to compare the model created using the training data set to another model created using the test data set. If the two models significantly differ, it will indicate there is bias in the original model.
D. Experience-based testing should be used to confirm that the training data set is operationally relevant.
This can include applying exploratory data analysis (EDA) to check for bias within the training data set.

Answer: D

Explanation:
The syllabus mentions that experience-based testing and EDA are effective for detecting biases:
"Experience-based testing can be used to verify that the training dataset is operationally relevant and identify potential sources of bias. EDA is also useful for exploring the data and understanding any relationships that might lead to bias in the model." (Reference: ISTQB CT-AI Syllabus v1.0, Section 8.3, page 58 of 99)

NEW QUESTION # 152
Which ONE of the following statements BEST describes a testing challenge that specifically applied to a self- learning system?

A. When systems change themselves the results of previously passing tests may change
B. In can be difficult to explain the link between test inputs and outputs
C. It is necessary to test whether the system will relinquish control to a human at the right time
D. External data sources might be required to ensure that the system is unbiased

Answer: A

Explanation:
The correct answer is A . A self-learning system can modify its own behaviour as it learns from operational data, environmental inputs, or its own decisions. The CT-AI syllabus identifies "unexpected change" as a major testing challenge for self-learning systems. It explains that when systems change themselves and their outputs, the results of previously passing tests can change, creating a regression testing and test design challenge. Testers may need tests that remain valid as the system evolves, and may also need new tests based on newly observed behaviours.
Option B relates more directly to autonomy , where the system must know when to return control to a human operator. Option C relates to testing for inappropriate bias, particularly when hidden or inferred attributes may influence outcomes. Option D relates to explainability, where the link between an input and a model output may be difficult to understand. These are important AI testing issues, but they are not the most specific self- learning challenge.
References/topics: CT-AI Syllabus Chapter 8, Section 8.1 "Challenges Testing Self-Learning Systems."
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NEW QUESTION # 153
Which supervised-learning classification/regression statement is correct?

A. Deciding whether an object is a bicycle or a motorcycle is a classification problem
B. In classification, objects are always assigned to exactly two classes
C. Predicting that diesel prices will increase by ~10% is a classification problem
D. Recognizing a dog from many different images is a regression problem

Answer: A

Explanation:
The ISTQB CT-AI syllabus explains supervised learning under Section1.6 - Machine Learning Approaches. It defines classification as predicting categorical labels, where as regression predicts continuous numerical values. OptionB--deciding whether an object is a bicycle or a motorcycle-- fits the definition of classification precisely because the model chooses between discrete categories. The syllabus also uses similar examples to illustrate classification tasks, reinforcing that this is the correct interpretation.

NEW QUESTION # 154
Which ONE of the below is MOST likely to be a challenge in ML data preparation?

A. Functional performance
B. The time and associated costs required
C. Designing a reward function
D. Specification of acceptance criteria

Answer: B

Explanation:
In machine learning, data preparation is often the most time-consuming and costly phase. It involves collecting, cleaning, and transforming raw data into a usable form for training models.
This can require significant effort and resources, making it the most likely challenge compared to the other options listed.

NEW QUESTION # 155
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