CT-AI완벽한시험자료 & CT-AI시험패스가능한공부

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ISTQB CT-AI Exam Syllabus Topics:

Section	Objectives
Topic 1: Ethics and Risk in AI Testing	- Ethical considerations 1. Responsible AI principles 2. Privacy and security concerns - Risk-based testing for AI 1. Mitigation strategies 2. Risk identification in AI systems
Topic 2: Machine Learning Fundamentals for Testing	- ML lifecycle 1. Training, validation, and evaluation 2. Data collection and preparation - Model types 1. Deep learning basics 2. Supervised and unsupervised learning
Topic 3: AI System Lifecycle and Operations	- Continuous learning systems 1. Retraining strategies 2. Model drift detection - Deployment and monitoring 1. Post-deployment monitoring 2. Model deployment strategies
Topic 4: Introduction to AI Testing	- Challenges in AI testing 1. Data dependency issues 2. Non-determinism in AI systems - AI systems overview 1. What is AI and machine learning systems 2. Differences between traditional and AI-based systems
Topic 5: AI Quality Characteristics	- Quality attributes 1. Explainability and transparency 2. Accuracy and robustness
Topic 6: Data Quality and Bias	- Bias and fairness 1. Types of bias in AI systems 2. Fairness testing approaches - Data quality assurance 1. Data completeness and consistency 2. Data labeling quality
Topic 7: Testing AI-Based Systems	- Test levels for AI systems 1. System integration testing 2. Model testing - Test design techniques 1. Metamorphic testing 2. Data-driven test design

>> CT-AI완벽한 시험자료 <<

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최신 ISTQB AI Testing CT-AI 무료샘플문제 (Q129-Q134):

질문 # 129
Which statement regarding testing transparency, explainability, or interpretability is MOST correct?

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

정답：B

설명：
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.

질문 # 130
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

A. II, III
B. I, IV
C. I, III
D. I, II

정답：C

설명：
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).

질문 # 131
Which of the following technologies for implementing AI is considered to be a reasoning technique?

A. Genetic algorithms
B. Deductive classifiers
C. Random Forest
D. Linear regression

정답：B

설명：
The ISTQB Certified Tester AI Testing Syllabus v1.0explicitly categorizes different AI implementation technologies in Section1.4 - AI Technologies. Within this section, AI methods are grouped into categories, one of which is"Reasoning techniques."These reasoning techniques includerule engines, deductive classifiers, case-based reasoning, and procedural reasoning.
Because deductive classifiers are directly listed under this set of reasoning approaches, they are recognized as a reasoning-based AI technology.
Reasoning techniques differ from machine learning approaches because they rely onstructured, predefined rules or logicto reach conclusions. Deductive classifiers use logical inference and symbolic reasoning to classify inputs by applying encoded knowledge. This makes them fundamentally different from statistical or data-driven ML algorithms.
The other options--Linear regression,Random Forest, and Genetic algorithms--are listed by the syllabus asmachine learning techniques, not reasoning methods. Linear regression performs numerical prediction, Random Forest is an ensemble decision-tree ML model, and genetic algorithms are optimization-based ML approaches inspired by evolutionary processes. None of these involve symbolic logical deduction.
Thus, based on the authoritative definitions in the syllabus, Deductive classifiers (Option A)is the only technology classified as a reasoning technique.

질문 # 132
A system is to be developed to detect lung cancer using X-ray images. Which statement BEST describes the difference between a conventional system and an AI system with supervised machine learning?

A. The results of analyzing an X-ray for lung cancer using an AI system are more understandable than with a conventional system.
B. The implementation of an AI system consists mainly of training data, whereas that of a conventional system consists of branches and loops.
C. The X-ray images that an AI system can analyze must be structurally different from X-ray images used in a conventional system.
D. An AI system independently determines patterns in X-rays during training; a conventional system requires a human to program in those patterns.

정답：D

설명：
The syllabus explains the fundamental distinction between conventional systems and AI-based systems using supervised machine learningin Section1.3 - AI-Based and Conventional Systems.
A conventional system relies on human-programmed logic--such as branches, conditions, and explicit rules--to interpret input data. The system behaves exactly as specified by its developers.
In contrast, AI systems using supervised learning automatically learn patterns from labeled data.
The syllabus states that"patterns in data are used by the system to determine how it should react in the future... The AI determines on its own what patterns or features in the data can be used".
This aligns directly with Option C: an AI system identifies relevant diagnostic patterns in X-ray images during training, whereas a conventional system requires human experts to explicitly program those patterns.

질문 # 133
Which ONE of the following characteristics is the least likely to cause safety related issues for an Al system?
SELECT ONE OPTION

A. Non-determinism
B. Self-learning
C. High complexity
D. Robustness

정답：D

설명：
The question asks which characteristic is least likely to cause safety-related issues for an AI system. Let's evaluate each option:
* Non-determinism (A): Non-deterministic systems can produce different outcomes even with the same inputs, which can lead to unpredictable behavior and potential safety issues.
* Robustness (B): Robustness refers to the ability of the system to handle errors, anomalies, and unexpected inputs gracefully. A robust system is less likely to cause safety issues because it can maintain functionality under varied conditions.
* High complexity (C): High complexity in AI systems can lead to difficulties in understanding, predicting, and managing the system's behavior, which can cause safety-related issues.
* Self-learning (D): Self-learning systems adapt based on new data, which can lead to unexpected changes in behavior. If not properly monitored and controlled, this can result in safety issues.
:
ISTQB CT-AI Syllabus Section 2.8 on Safety and AI discusses various factors affecting the safety of AI systems, emphasizing the importance of robustness in maintaining safe operation.

질문 # 134
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