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ISTQB CT-AI Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • Machine Learning ML: This section includes the classification and regression as part of supervised learning, explaining the factors involved in the selection of ML algorithms, and demonstrating underfitting and overfitting.
Topic 4
  • Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 5
  • 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.
Topic 6
  • Using AI for Testing: In this section, the exam topics cover categorizing the AI technologies used in software testing.
Topic 7
  • 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 8
  • 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 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.

ISTQB Certified Tester AI Testing Exam Sample Questions (Q65-Q70):

NEW QUESTION # 65
Which of the following problems would best be solved using the supervised learning category of regression?

Answer: C

Explanation:
The syllabus states:
"Supervised learning... divides problems into two categories: classification and regression. Regression is used when the problem requires the ML model to predict a numeric output, for example predicting the age of a person based on their habits." (Reference: ISTQB CT-AI Syllabus v1.0, Section 3.1.1, Page 26 of 99)


NEW QUESTION # 66
Which ONE of the following options is a technology used to implement AI?

Answer: B

Explanation:
Genetic algorithms are a technology used in AI, particularly in optimization problems and machine learning models. They are inspired by the process of natural selection and evolve solutions over generations.


NEW QUESTION # 67
Which ONE of the following tests is MOST likely to describe a useful test to help detect different kinds of biases in ML pipeline?
SELECT ONE OPTION

Answer: B

Explanation:
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.


NEW QUESTION # 68
Which TWO of the below examples of systems BEST describe regression?
I. Predicting the age of a person
II. Predicting if someone is aged over 18
III. Predicting how much fuel is required for a journey
IV. Predicting whether a release will pass all required tests

Answer: A

Explanation:
Regression is used for predicting continuous values.
I (Predicting the age of a person) and III (Predicting how much fuel is required for a journey) both involve continuous numerical predictions, which are characteristic of regression models.
II (Predicting if someone is aged over 18) and IV (Predicting whether a release will pass all required tests) are classification problems as they involve categorical outcomes (yes/no).


NEW QUESTION # 69
Which ONE of the following options BEST DESCRIBES clustering?

Answer: D

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.
In clustering, the algorithm groups data points into clusters without any prior knowledge of the classes. It discovers the inherent structure in the data.


NEW QUESTION # 70
......

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