Latest ISTQB CT-AI Exam Review | Pass CT-AI Test Guide

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ISTQB CT-AI Exam Syllabus Topics:

Topic	Details
Topic 1	Test Environments for AI-Based Systems: This section is about factors that differentiate the test environments for AI-based
Topic 2	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 3	systems from those required for conventional systems.
Topic 4	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 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	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.

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ISTQB Certified Tester AI Testing Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
Which statement regarding testing transparency, explainability, or interpretability is MOST correct?

A. LIME can precisely state the decisive reason for a change in the output
B. Dynamic testing is one way to quantify explainability; however, each method is specific to a particular model type
C. Since different users have different backgrounds, interpretability testing depends on the comprehensibility of the ML algorithm
D. Tests for explainability and transparency are comparable to exploratory testing and can be performed with little information about development

Answer: C

Explanation:
The ISTQB CT-AI syllabus states in Section2.10 - Explainability, Transparency, and Interpretability that interpretability isuser-dependent, meaning different users understand explanations differently. This is because interpretability depends not only on the ML algorithm but also on the user's domain knowledge, experience, and expectations. Option B directly reflects this syllabus principle: interpretability testing must consider user background, and explanations must be comprehensible to the intended user group.

NEW QUESTION # 94
Which ONE of the below is NOT likely to cause a data quality issue affecting a single ML model?

A. Hardware issues
B. Incorrect weights
C. Security issues
D. Faulty sensors

Answer: B

Explanation:
Incorrect weights refer to parameters within a trained model and are not typically considered a data quality issue. Data quality issues usually arise from the input data itself, such as security issues, hardware issues, or faulty sensors, which can lead to incorrect or missing data. Incorrect weights are a problem during the model training or fine-tuning phase, not directly related to data quality.

NEW QUESTION # 95
Which statement regarding AI for defect prediction is correct?

A. AI-based defect prediction is based on formal principles and requires only a few factors.
B. AI-based defect prediction is most effective when based on source-code metrics such as branches or McCabe complexity.
C. AI-based defect prediction can detect whether defects exist but not where.
D. AI-based defect prediction is most effective when based on previous similar constellations.

Answer: D

Explanation:
Section5.3 - AI Support for Defect Predictionof the ISTQB CT-AI syllabus explains that AI-based defect prediction models rely onhistorical patterns, including past defects, code behavior, and similar system configurations. ML models trained on prior defect data can identifycomponents likely to contain defectswhen new changes resemble previous defect-inducing patterns. This directly supports Option A, which states that defect prediction is most effective when based on previous similar constellations.

NEW QUESTION # 96
You have been asked for your opinion on the ML approach to be used for a new age verification system. A training dataset is available with many pictures of faces and the subject's age.
When new users sign-up to a mobile app, they have to take a picture of their face. If the system believes they are under 18 years old it will ask them to provide a picture of their passport to prove their age.
Which ONE of the following approaches do you expect to be MOST likely to succeed?

A. Unsupervised learning that identifies clusters in training data that relate to people's ages.
B. Supervised learning classification system that is re-trained based on new data, including whether they later provided ID that showed they were over 18.
C. Supervised learning regression system that is re-trained based on new data, including whether they provided ID that showed their age.
D. Reinforcement learning classification system with a reward function of correct behavior.

Answer: B

Explanation:
A supervised learning classification system is most likely to succeed in this scenario because it involves using labeled data (the pictures of faces with corresponding ages) to train a model. The system can then classify whether a person is under or over 18 years old. The approach of re- training based on new data (including whether they later provided ID showing their age) ensures continuous improvement of the model. This method directly aligns with the goal of age verification.

NEW QUESTION # 97
Which ONE of the following models BEST describes a way to model defect prediction by looking at the history of bugs in modules by using code quality metrics of modules of historical versions as input?
SELECT ONE OPTION

A. Using a classification model to predict the presence of a defect by using code quality metrics as the input data.
B. Search of similar code based on natural language processing.
C. Clustering of similar code modules to predict based on similarity.
D. Identifying the relationship between developers and the modules developed by them.

Answer: A

Explanation:
Defect prediction models aim to identify parts of the software that are likely to contain defects by analyzing historical data and code quality metrics. The primary goal is to use this predictive information to allocate testing and maintenance resources effectively. Let's break down why option D is the correct choice:
Understanding Classification Models:
Classification models are a type of supervised learning algorithm used to categorize or classify data into predefined classes or labels. In the context of defect prediction, the classification model would classify parts of the code as either "defective" or "non-defective" based on the input features.
Input Data - Code Quality Metrics:
The input data for these classification models typically includes various code quality metrics such as cyclomatic complexity, lines of code, number of methods, depth of inheritance, coupling between objects, etc. These metrics help the model learn patterns associated with defects.
Historical Data:
Historical versions of the code along with their defect records provide the labeled data needed for training the classification model. By analyzing this historical data, the model can learn which metrics are indicative of defects.
Why Option D is Correct:
Option D specifies using a classification model to predict the presence of defects by using code quality metrics as input data. This accurately describes the process of defect prediction using historical bug data and quality metrics.
Eliminating Other Options:
A . Identifying the relationship between developers and the modules developed by them: This does not directly involve predicting defects based on code quality metrics and historical data.
B . Search of similar code based on natural language processing: While useful for other purposes, this method does not describe defect prediction using classification models and code metrics.
C . Clustering of similar code modules to predict based on similarity: Clustering is an unsupervised learning technique and does not directly align with the supervised learning approach typically used in defect prediction models.
Reference:
ISTQB CT-AI Syllabus, Section 9.5, Metamorphic Testing (MT), describes various testing techniques including classification models for defect prediction.
"Using AI for Defect Prediction" (ISTQB CT-AI Syllabus, Section 11.5.1).

NEW QUESTION # 98
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