[Jul 28, 2026] Updates Up to 365 days On Valid CT-GenAI Braindumps [Q21-Q35]

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[Jul 28, 2026] Updates Up to 365 days On Valid CT-GenAI Braindumps

Best QualityCT-GenAI Exam Questions ISQI Test To Gain Brilliante Result

ISQI CT-GenAI Exam Syllabus Topics:

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

NO.21 Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

ISO/IEC 42001:2023

NIST AI RMF 1.0

ISO/IEC 23053:2022

EU AI Act

NO.22 Which technique MOST directly reduces hallucinations by grounding the model in project realities?

Provide detailed context

Randomize prompts each run

Rely on generic examples only

Use longer temperature settings

NO.23 In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.

i, ii, iii

i, iii, v

i, ii, iii (Duplicate entry in original source)

ii, iii, iv

NO.24 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.

NO.25 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

NO.26 An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?

No defect; this is acceptable

Reasoning error in risk calculation logic

Hallucination

Dataset bias toward UI features

NO.27 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

NO.28 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

NO.29 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

NO.30 Which of the following is NOT a valid form of LLM-driven test data generation?

Creating production database backups

Setting boundary values

Creating combinatorial data (e.g., pairwise)

Generating synthetic datasets

NO.31 Which statement BEST contrasts interaction style and scope?

Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.

Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing

Chatbots require API integration; LLM apps do not.

Both are identical aside from UI theme.

NO.32 A prompt begins: “You are a senior test manager responsible for risk-based test planning on a payments platform.” Which component is this?

Instruction

Context

Role

Constraints

NO.33 What is a key data-related aspect when defining a GenAI strategy for testing?

Neglect legacy data sources as they provide limited immediate relevance to testing tasks

Prioritize accurate and relevant input data secured through defined quality procedures

Aggregate data from all available organizational repositories without filtration

Use only auto-generated synthetic data to avoid dependency on enterprise repositories

NO.34 Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

Select models with maximum vendor visibility and strong online presence to ensure reliability

Select open-source models prioritizing creativity over compliance or performance consistency

Select broad-coverage models offering diverse functionalities for various test scenarios

Select LLMs aligned to measurable test outcomes, compatible with current infrastructure

NO.35 Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

Some GenAI tools may store/process data without explicit consent

GenAI outputs can accidentally reveal sensitive information present in inputs

Strict GDPR compliance eliminates all privacy risk

Using GenAI without regulatory compliance can lead to legal exposure

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ISQI CT-GenAI Exam Syllabus Topics:

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