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ISQI CT-GenAI Exam Syllabus Topics:

Section	Objectives
Topic 1: Foundations of Generative AI and LLMs	- Introduction to Generative AI in Software Testing 1. Difference between chatbots and LLM-based test tools 2. LLM basics, tokenization, context window, multimodal models
Topic 2: Prompt Engineering for Testing	- Prompt design techniques 1. Zero-shot, one-shot, few-shot prompting 2. Structuring prompts for test case generation 3. Prompt chaining and meta prompting
Topic 3: Application of GenAI in Software Testing	- Practical use in testing workflows 1. Regression suite optimization 2. Test data generation and augmentation 3. Test case generation using LLMs 4. Defect report analysis and summarization
Topic 4: Organizational Adoption and Governance	- Enterprise GenAI adoption 1. LLMOps and governance models 2. Integration into CI/CD pipelines 3. Policy, ethics, and compliance considerations
Topic 5: Risk, Quality, and Limitations of GenAI	- Risks in GenAI usage 1. Bias and fairness issues 2. Data privacy and security concerns 3. Environmental and energy considerations 4. Hallucinations and reasoning errors

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q10-Q15):

NEW QUESTION # 10
You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

A. Add a step to review the change log for syntax errors before analysis.
B. Specify that the role is a test architect specializing in CI/CD pipelines.
C. Include references to version control systems like Git in the constraints.
D. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity

Answer: D

NEW QUESTION # 11
Which option BEST differentiates the three prompting techniques?

A. Few-shot = examples; Chaining = multi-step prompts; Meta = model helps draft/refine prompts
B. Chaining = give examples; Few-shot = break tasks; Meta = manual edits only
C. Meta = step decomposition; Chaining = zero-shot only; Few-shot = manual optimization
D. Few-shot = no examples; Chaining = single prompt; Meta = disable iteration

Answer: A

Explanation:
Differentiating between prompting techniques is essential for a tester to select the right tool for the task.Few- shot promptingis characterized by providing the model with a few examples of inputs and desired outputs, allowing it to learn the pattern and format.Prompt Chaininginvolves breaking a complex task into a sequence of smaller, interconnected prompts, where the output of one step becomes the input for the next (e.g., first extract requirements, then generate test cases from those requirements).Meta-promptingis a more advanced technique where the user asks the LLM to help design, write, or refine the prompt itself-essentially using the AI as a "prompt engineer" to optimize the instructions. Option D correctly identifies these core characteristics.
Options A, B, and C contain fundamental mischaracterizations: for instance, Few-shotrequiresexamples (contradicting A), and Chaining is theoppositeof a single prompt (contradicting A). Mastering these distinctions allows testers to move from simple "chatting" to sophisticated AI orchestration that can handle complex, multi-stage testing workflows with high reliability.

NEW QUESTION # 12
Which statement BEST describes vision-language models (VLMs)?

A. VLMs are unrelated to multimodal LLMs and focus only on UI automation.
B. VLMs are a superset of multimodal LLMs.
C. VLMs process audio and video but not images.
D. VLMs are a subset of multimodal LLMs integrating visual and textual information.

Answer: D

Explanation:
Vision-Language Models (VLMs)represent a specialized subset of multimodal Large Language Models.
Their defining characteristic is the ability to process, understand, and reason across both textual and visual modalities simultaneously. In the field of software testing, VLMs are revolutionary because they allow the AI to "see" a User Interface (UI). A tester can provide a screenshot of a web page alongside a natural language prompt, and the VLM can identify UI elements, detect visual regressions, or even validate that the visual layout matches a design specification. They are not a "superset" (Option C) of multimodal AI, but rather a specific implementation of it focused on the intersection of sight and language. Unlike traditional OCR or pixel-comparison tools used in legacy UI automation (Option B), VLMs understand thecontextof what they see-for instance, identifying a "broken" button icon that a human would recognize but a rule-based script might miss. This integration of visual and textual data is what makes them a vital component of modern, AI- augmented Quality Assurance strategies.

NEW QUESTION # 13
The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

A. Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions
B. Precision for anomaly identification and recall for coverage of valid/invalid partitions
C. Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
D. Diversity for anomaly identification and precision for partitions

Answer: B

Explanation:
In the evaluation of GenAI outputs for testing, metrics must align with the specific nature of the task. For anomaly identification, the goal is to correctly identify true issues without an overwhelming number of false positives; therefore,Precisionis the critical metric (the ratio of true anomalies to the total flagged).
Conversely, forpartition testing(identifying valid and invalid input classes), the goal is thoroughness and ensuring no significant category is missed.Recallis the most appropriate metric here, as it measures the model's ability to "call back" or cover all possible relevant partitions from the requirement set. As highlighted in the CT-GenAI syllabus, evaluating AI effectiveness often requires a combination of these model- performance metrics. While "Accuracy" (Option D) provides a general view, it is often misleading in imbalanced testing scenarios (like anomaly detection where anomalies are rare). By using Precision and Recall together, a test organization can quantitatively assess if the AI is both trustworthy in its alerts and comprehensive in its test design coverage.

NEW QUESTION # 14
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

A. Using GenAI without regulatory compliance can lead to legal exposure
B. Some GenAI tools may store/process data without explicit consent
C. GenAI outputs can accidentally reveal sensitive information present in inputs
D. Strict GDPR compliance eliminates all privacy risk

Answer: D

Explanation:
The statement that "Strict GDPR compliance eliminates all privacy risk" isincorrectbecause compliance is a legal and procedural framework, not a foolproof technical shield against all possible risks. Even within a GDPR-compliant environment, risks such as "model inversion" attacks, accidental data leakage through
"membership inference," or the unintentional generation of Sensitive Personally Identifiable Information (SPII) can still occur. Data privacy in GenAI is complex because LLMs function by processing and sometimes retaining patterns from the data they are fed. As noted in the CT-GenAI syllabus, some tools may process data in ways that are not fully transparent (Option A), and outputs can inadvertently include snippets of sensitive data used during the prompting or training phase (Option B). Furthermore, failing to adhere to regulations like GDPR or the EU AI Act certainly leads to legal and financial exposure (Option D). Therefore, while compliance frameworks significantly mitigate risk, they do not "eliminate" it; a robust GenAI strategy requires ongoing technical controls, data masking, and human oversight to manage residual privacy threats effectively.

NEW QUESTION # 15
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