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

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Exam style questions across every CT-GenAI domain

Last Update 3 days ago
Total Questions : 40

Start with our free CT-GenAI practice questions, carefully crafted to mirror the domains, phrasing, and difficulty of the real AI Testing exam. Each CT-GenAI exam question comes with a detailed rationale that explains not just which answer is correct but why the others fall short. That's how concepts stick. Use the free set to benchmark yourself: identify your iSQI weak domains, see where you're losing marks, and build a focused study plan in minutes.

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Question # 1

You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?

i. Retrieve semantically similar specification chunks from the vector database

ii. Feed both retrieved datasets as context for the LLM to generate new test cases

iii. Retrieve relevant historical cases from the relational database

iv. Submit a focused query describing the new test requirement

Options:

A.  

iv —> iii —> i —> ii

B.  

iv —> i —> iii —> ii

C.  

iii —> iv —> i —> ii

D.  

i —> iv —> iii —> ii

Discussion 0
Question # 2

Who typically defines the system prompt in a testing workflow?

Options:

A.  

A tester configuring the assistant

B.  

End user during normal chat use

C.  

CI server automatically without human input

D.  

Product owner in user stories only

Discussion 0
Question # 3

Which option BEST differentiates the three prompting techniques?

Options:

A.  

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

B.  

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

C.  

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

D.  

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

Discussion 0
Question # 4

Which AI approach requires feature engineering and structured data preparation?

Options:

A.  

Symbolic AI

B.  

Generative AI

C.  

Classical Machine Learning

D.  

Deep Learning

Discussion 0
Question # 5

What are the three key phases in adopting GenAI in a test organization?

Options:

A.  

Discovery; initiation and usage definition; utilization and iteration

B.  

Prototype; pilot; decommission

C.  

Training; certification; outsourcing

D.  

Planning; execution; sign-off

Discussion 0
Question # 6

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
Question # 7

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

Options:

A.  

Reliance on predefined templates to generate short, factual answers

B.  

Ability to respond to prompts without explicit user instructions

C.  

Ability to trigger automated actions beyond conversation

D.  

Use of a conversational tone and improved response personalization

Discussion 0
Question # 8

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

Options:

A.  

Automated compliance validation during AI tool deployment

B.  

Failure to update system documentation within the test process

C.  

Difficulty in aligning project milestones with business outcomes

D.  

Violation of established data handling and regulatory compliance standards

Discussion 0
Question # 9

Consider applying the meta-prompting technique to generate automated test scripts for API testing. You need to test a REST API endpoint that processes user registration with validation rules. Which one of the following prompts is BEST suited to this task?

Options:

A.  

Role: Act as a test automation engineer with API testing experience. | Context: You are verifying user registration that enforces field and format validation. | Instruction: Generate pytest scripts using requests for both positive (valid) and negative (invalid email, weak password, missing fields) cases. | Input Data: POST /api/register with validation rules for email and password length. | Constraints: Include fixtures, clear assertions, a

B.  

Role: Act as a test automation engineer. | Context: You are creating tests for a registration endpoint. | Instruction: Generate Python test scripts using pytest covering both valid and invalid inputs. | Input Data: POST /api/register with email and password. | Constraints: Follow pytest structure. | Output Format: Provide scripts.

C.  

Role: Act as an automation tester. | Context: You are validating an API endpoint. | Instruction: Generate Python test scripts that send POST requests and validate responses. | Input Data: User credentials. | Constraints: Include basic scenarios with asserts. | Output Format: Provide organized scripts.

D.  

Role: Act as a software engineer. | Context: You are testing registration logic. | Instruction: Create Python scripts to verify endpoint behavior. | Input Data: POST /api/register with test users. | Constraints: Add checks for status codes. | Output Format: Deliver functional scripts.

Discussion 0
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?

Options:

A.  

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

B.  

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

C.  

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

D.  

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

Discussion 0

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