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NEW QUESTION # 67
There is a growing backlog of unresolved defects for your project. You know the developers have an ML model that they have created which has learned which developers work on which type of software and the speed with which they resolve issues. How could you use this model to help reduce the backlog and implement more efficient defect resolution?
Answer: D
Explanation:
AI and ML models can play a significant role in optimizing defect resolution processes. According to the ISTQB Certified Tester AI Testing (CT-AI) Syllabus, ML models can be used toanalyze defect reports, prioritize critical defects, and assign defects to developersbased on historical defect resolution patterns.
The key AI applications for defect management include:
* Defect Categorization- NLP techniques can analyze defect reports and classify them based on metadata like severity and impact.
* Defect Prioritization- ML models trained on past defects can predict which issues are likely to cause failures, allowing teams toprioritizethe most critical issues.
* Defect Assignment- AI-based models can suggest which developers are best suited for specific defects, optimizing the resolution process based on past performance and specialization.
From the given answer choices:
* Option A (Automatic Prioritization)is useful but does not directlyreduce backlog efficientlyby considering developer expertise and workload balancing.
* Option C (Root Cause Analysis for Process Improvement)is along-term strategybut does not directly address backlog reduction.
* Option D (Defect Prediction for Testing Focus)helps preemptively identify issues but does not resolve the existing backlog.
Thus,Option Bis the best choice as it aligns with AI's capability toassign defects to the most suitable developersbased on historical data, ensuring efficient defect resolution and backlog reduction.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 11.2 (Using AI to Analyze Reported Defects)
* ISTQB CT-AI Syllabus v1.0, Section 11.5 (Using AI for Defect Prediction).
NEW QUESTION # 68
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?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
The syllabus explains the fundamental distinction betweenconventional systemsandAI-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 patternsfrom 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.
Option A is incorrect because AI outputs are typicallylessexplainable, not more. Option B is incorrect because both systems can use thesame X-ray images; ML does not require structurally different images. Option D is oversimplified and not fully accurate; while training data is central to ML, AI systems also include architecture, algorithms, and preprocessing-not just data.
Thus,Option Cis the correct and syllabus-aligned answer.
NEW QUESTION # 69
Which ONE of the following options represents a technology MOST TYPICALLY used to implement Al?
SELECT ONE OPTION
Answer: D
Explanation:
* Technology Most Typically Used to Implement AI: Genetic algorithms are a well-known technique used in AI . They are inspired by the process of natural selection and are used to find approximate solutions to optimization and search problems. Unlike search engines, procedural programming, or case control structures, genetic algorithms are specifically designed for evolving solutions and are commonly employed in AI implementations.
* Reference: ISTQB_CT-AI_Syllabus_v1.0, Section 1.4 AI Technologies, which identifies different technologies used to implement AI.
NEW QUESTION # 70
Which of the following is one of the reasons for data mislabelling?
Answer: A
Explanation:
Data mislabeling occurs for several reasons, which can significantly impact the performance of machine learning (ML) models, especially in supervised learning. According to the ISTQB Certified Tester AI Testing (CT-AI) syllabus, mislabeling of data can be caused by the following factors:
* Random errors by annotators- Mistakes made due to accidental misclassification.
* Systemic errors- Errors introduced by incorrect labeling instructions or poor training of annotators.
* Deliberate errors- Errors introduced intentionally by malicious data annotators.
* Translation errors- Occur when correctly labeled data in one language is incorrectly translated into another language.
* Subjectivity in labeling- Some labeling tasks require subjective judgment, leading to inconsistencies between different annotators.
* Lack of domain knowledge- If annotators do not have sufficient expertise in the domain, they may label data incorrectly due to misunderstanding the context.
* Complex classification tasks- The more complex the task, the higher the probability of labeling mistakes.
Among the answer choices provided, "Lack of domain knowledge" (Option A) is the best answer because expertise is essential to accurately labeling data in complex domains such as medical, legal, or engineering fields.
Certified Tester AI Testing Study Guide References:
* ISTQB CT-AI Syllabus v1.0, Section 4.5.2 (Mislabeled Data in Datasets)
* ISTQB CT-AI Syllabus v1.0, Section 4.3 (Dataset Quality Issues)
NEW QUESTION # 71
Which of the following descriptions of quality aspects of a data set is correct?
Choose ONE option (1 out of 4)
Answer: B
Explanation:
The ISTQB CT-AI syllabus describes severaldata quality aspectsthat affect ML performance. In Section2.2
- Data Preparation, it explains that datasets may suffer from issues such asincomplete data,irrelevant data, incorrect data,unbalanced data, or data lacking preprocessing. "Incomplete data" means thatportions of the required data are missing, often because some time periods, records, or sources were not captured. This aligns exactly with Option A, which correctly identifies missing intervals as incomplete data.
Option B is incorrect: "data not preprocessed" refers to data that has not undergone normalization, cleaning, or transformation-not data recorded incorrectly. Option C is wrong because irrelevant datadoesnegatively affect ML models by introducing noise and unnecessary features. The syllabus explicitly states that including irrelevant features can degrade model learning. Option D is incorrect: "unbalanced data" relates to disproportionate class distribution, not recency or freshness of data.
Thus, OptionAis the only statement that correctly matches the syllabus definition of this data quality aspect.
NEW QUESTION # 72
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