2026 Latest CT-GenAI dumps – Instant Download PDF [Q15-Q29]

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2026 Latest CT-GenAI dumps – Instant Download PDF

Updated Verified CT-GenAI Downloadable Printable Exam Dumps

ISQI CT-GenAI Exam Syllabus Topics:

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

Q15. The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

Precision for anomaly identification and recall for coverage of valid/invalid partitions

Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions

Diversity for anomaly identification and precision for partitions

Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions

Q16. What does an embedding represent in an LLM?

Tokens grouped into context windows

Numerical vectors capturing semantic relationships

Logical rules for reasoning

A set of test cases for validation

Q17. What BEST protects sensitive test data at rest and in transit?

Rely on obfuscation instead of encryption

Enforce role-based access controls

Disable TLS and rely on VPN only

Use public file shares with read-only links

Q18. What is a primary compliance concern related to Shadow AI in organizational test environments?

Automated compliance validation during AI tool deployment

Failure to update system documentation within the test process

Difficulty in aligning project milestones with business outcomes

Violation of established data handling and regulatory compliance standards

Q19. Who typically defines the system prompt in a testing workflow?

A tester configuring the assistant

End user during normal chat use

CI server automatically without human input

Product owner in user stories only

Q20. Which option BEST differentiates the three prompting techniques?

Few-shot = no examples; Chaining = single prompt; Meta = disable iteration

Meta = step decomposition; Chaining = zero-shot only; Few-shot = manual optimization

Chaining = give examples; Few-shot = break tasks; Meta = manual edits only

Few-shot = examples; Chaining = multi-step prompts; Meta = model helps draft/refine prompts

Q21. Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization’s standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?

Prompt chaining

Few-shot prompting

Meta prompting

Zero-shot prompting

Q22. What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

Reliance on predefined templates to generate short, factual answers

Ability to respond to prompts without explicit user instructions

Ability to trigger automated actions beyond conversation

Use of a conversational tone and improved response personalization

Q23. A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?

A/B testing of prompts

Iterative prompt modification

Output analysis

Integrating user feedback

Q24. When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?

Maximizing scalability by deploying larger cloud-based LLM clusters

Maintaining data privacy and minimizing security risks from external services

Achieving faster responses by reducing model checkpoints and updates

Focusing primarily on user experience improvements and response formatting

Q25. 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?

Include references to version control systems like Git in the constraints.

Specify that the role is a test architect specializing in CI/CD pipelines.

Add a step to review the change log for syntax errors before analysis.

Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity

Q26. How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

Replacing existing test coverage validation with automated summary reports generated by AI

Transitioning from manual execution to complete automation with no human oversight

Moving from black-box exploratory testing toward exclusively performing code-based white-box checks

Shifting from test execution toward reviewing, refining, and validating AI-generated testware

Q27. Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

It dynamically generates test insights using contextual information

It produces scripted conversational responses similar to traditional bots

It focuses primarily on visual dashboards and user navigation features

It provides fixed responses from predefined rule sets and scripts

Q28. You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM’s ability to predict risks and provide actionable alerts?

Emphasize constraints that focus on deviations that could impact release timelines or quality gates.

Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.

Specify that the role is a test analyst with expertise in predictive analytics and risk management.

Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.

Q29. An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?

Data poisoning

Malicious code generation

Data exfiltration

Request manipulation

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2026 Latest CT-GenAI dumps – Instant Download PDF [Q15-Q29]

ISQI CT-GenAI Exam Syllabus Topics:

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