Spring Sale 65% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: exams65

ExamsBrite Dumps

ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Question and Answers

ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0

Last Update Feb 28, 2026
Total Questions : 40

We are offering FREE CT-GenAI iSQI exam questions. All you do is to just go and sign up. Give your details, prepare CT-GenAI free exam questions and then go for complete pool of ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 test questions that will help you more.

CT-GenAI pdf

CT-GenAI PDF

$36.75  $104.99
 Add to Cart
CT-GenAI Engine

CT-GenAI Testing Engine

$43.75  $124.99
 Add to Cart
CT-GenAI PDF + Engine

CT-GenAI PDF + Testing Engine

$57.75  $164.99
 Add to Cart
Questions 1

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?

Options:

A.  

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

B.  

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

C.  

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

D.  

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

Discussion 0
Questions 2

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?

Options:

A.  

A/B testing of prompts

B.  

Iterative prompt modification

C.  

Output analysis

D.  

Integrating user feedback

Discussion 0
Questions 3

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

Options:

A.  

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

B.  

Transitioning from manual execution to complete automation with no human oversight

C.  

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

D.  

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

Discussion 0
Questions 4

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

Options:

A.  

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

B.  

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

C.  

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

D.  

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

Discussion 0
Questions 5

What is a key data-related aspect when defining a GenAI strategy for testing?

Options:

A.  

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

B.  

Prioritize accurate and relevant input data secured through defined quality procedures

C.  

Aggregate data from all available organizational repositories without filtration

D.  

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

Discussion 0
Questions 6

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?

Options:

A.  

Prompt chaining

B.  

Few-shot prompting

C.  

Meta prompting

D.  

Zero-shot prompting

Discussion 0
Questions 7

Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

Options:

A.  

ISO/IEC 42001:2023

B.  

NIST AI RMF 1.0

C.  

ISO/IEC 23053:2022

D.  

EU AI Act

Discussion 0
Questions 8

Which statement about fine-tuning for test tasks is INCORRECT?

Options:

A.  

It adapts a pre-trained model to a domain using task-specific data

B.  

It replaces the model’s general knowledge entirely and prevents overfitting

C.  

It enhances relevance to organizational terminology and formats

D.  

It can be applied to smaller SLMs to improve task performance with lower compute

Discussion 0
Questions 9

Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

Options:

A.  

Some GenAI tools may store/process data without explicit consent

B.  

GenAI outputs can accidentally reveal sensitive information present in inputs

C.  

Strict GDPR compliance eliminates all privacy risk

D.  

Using GenAI without regulatory compliance can lead to legal exposure

Discussion 0
Questions 10

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

Options:

A.  

Maximizing scalability by deploying larger cloud-based LLM clusters

B.  

Maintaining data privacy and minimizing security risks from external services

C.  

Achieving faster responses by reducing model checkpoints and updates

D.  

Focusing primarily on user experience improvements and response formatting

Discussion 0
Questions 11

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

Options:

A.  

It dynamically generates test insights using contextual information

B.  

It produces scripted conversational responses similar to traditional bots

C.  

It focuses primarily on visual dashboards and user navigation features

D.  

It provides fixed responses from predefined rule sets and scripts

Discussion 0
Questions 12

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?

Options:

A.  

No defect; this is acceptable

B.  

Reasoning error in risk calculation logic

C.  

Hallucination

D.  

Dataset bias toward UI features

Discussion 0