[Aug 21, 2026] Achive your Success with Latest ISQI CT-GenAI Exam [Q24-Q42]

Posted On August 21, 2026

Achive your Success with Latest ISQI CT-GenAI Exam [Aug 21, 2026]

The CT-GenAI Exam Test For Brief Preparation

QUESTION 24
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

QUESTION 25
Which statement BEST describes vision-language models (VLMs)?

VLMs are a subset of multimodal LLMs integrating visual and textual information.

VLMs are unrelated to multimodal LLMs and focus only on UI automation.

VLMs are a superset of multimodal LLMs.

VLMs process audio and video but not images.

QUESTION 26
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

QUESTION 27
A prompt section states: “Web checkout module v3.2; focus on coupon application; existing regression suite IDs T-112-T-150; recent defect ID BUG-431.” Which component is this?

Input data

Constraints

Instruction

Output format

QUESTION 28
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

QUESTION 29
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.

QUESTION 30
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

QUESTION 31
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

Ability to respond to prompts without explicit user instructions

Ability to trigger automated actions beyond conversation

Reliance on predefined templates to generate short, factual answers

Use of a conversational tone and improved response personalization

QUESTION 32
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

QUESTION 33
Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

The number of tokens processed directly determines the carbon intensity of each query

The location of the data center determines model bias and accuracy levels

The duration of user sessions primarily affects latency but not power efficiency

The type of cloud platform affects processing speed but not total energy draw

QUESTION 34
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

QUESTION 35
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

QUESTION 36
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

QUESTION 37
You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?

Generate prioritized conditions in one shot -> verify coverage

Generate conditions -> prioritize by risk -> map to requirements to find gaps

Detect requirement defects -> generate conditions -> prioritize

Prioritize requirements -> generate conditions -> review defects

QUESTION 38
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?

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

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

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

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

QUESTION 39
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

QUESTION 40
Which technique MOST directly reduces hallucinations by grounding the model in project realities?

Provide detailed context

Randomize prompts each run