ISTQB Testing - GenAI CT-GenAI Practice Exams 240 Questions

Are you preparing for the ISTQB Certified Tester - Testing with Generative AI (CT-GenAI) certification and want to assess your readiness with realistic, high-quality exam-style practice questions?

This comprehensive practice exam course has been designed to mirror the real CT-GenAI certification exam as closely as possible..

With 6 full-length practice tests containing 240 questions in total, you will gain the confidence and knowledge required to pass the ISTQB CT-GenAI certification on your very first attempt. Each question is carefully written to match the difficulty, structure, and exam-style wording you will face on test day.

Every question comes with detailed explanations for both correct and incorrect answers, ensuring that you not only know the right answer but also understand why the other options are wrong. This unique approach deepens your understanding and prepares you for any variation of the question that may appear in the real exam.

Our ISTQB CT-GenAI practice exams will help you identify your strong areas and pinpoint where you need improvement. By completing these tests under timed conditions, you will build the exam discipline and confidence required to succeed.

This course has been completely rebuilt and validated against the latest official ISTQB CT-GenAI syllabus. It now provides 100% Learning Objective coverage with corrected K-level distribution (K1/K2/K3) exactly aligned to the real exam blueprint.

This CT-GenAI Practice Test Course Includes:

• 6 full-length practice exams with 40 questions each (240 total)

Exam Details – ISTQB CT-GenAI Certification

• Exam Body: ISTQB (International Software Testing Qualifications Board)

Detailed Syllabus and Topic Weightage

The ISTQB CT-GenAI exam is structured around 5 major syllabus areas. Below is a detailed breakdown along with the approximate exam weightage:

1. Introduction to Generative AI for Software Testing (~17.5%)

• Understand the role and relevance of Generative AI in software testing.

2. Prompt Engineering for Effective Software Testing (~27.5%)

• Define the structure of an effective prompt: Role, Context, Instruction, Input, Constraints, and Output.

• Test analysis and design

3. Managing Risks of Generative AI in Software Testing (~25%)

• Identify hallucinations, reasoning errors, and biases in Generative AI systems.

4. LLM-Powered Test Infrastructure (~12.5%)

• Explain architectural patterns for integrating LLMs into test automation frameworks.

5. Deploying and Integrating Generative AI in Test Organizations (~17.5%)

• Define the organizational roadmap for adopting Generative AI in testing.

Learning Outcomes

By the end of this course, learners will be able to:

• Explain Generative AI fundamentals and their testing implications.

Relative Weightage: Chapter 2 (Prompt Engineering) is the most heavily tested, followed by Chapter 3 (Risks).

Practice Test Structure

• 6 Full-Length Tests

• Each test contains 40 exam-style questions

• Detailed explanation for each correct & incorrect option

• Prevents memorization, ensures real exam readiness

• Instant scoring, pass/fail status, weak areas highlighted

Sample Practice Questions (CT-GenAI)

Question 1 (Scenario-based):
A senior tester must generate test cases for a requirements document that contains ambiguous acceptance criteria and conflicting business rules across three related features. The tester needs the LLM to first clarify the conflicts before generating test cases. Which prompting approach is MOST appropriate for this scenario?.

Options:
A. Zero-shot prompting, providing the full requirements document and requesting complete test case output in a single prompt.
B. Few-shot prompting, supplying three example test cases from a previous project as context before requesting new test cases.
C. Prompt chaining, first prompting the LLM to identify and resolve ambiguities, then using that output to generate test cases.
D. Role prompting alone, instructing the LLM to act as a senior test analyst and generate test cases directly from the requirements.

Answer: C

Explanation:
A. This is incorrect because zero-shot prompting submits a single prompt without intermediate clarification steps, making it unsuitable when ambiguities must be resolved before test case generation can produce accurate results. A single prompt cannot first resolve conflicts and then generate test cases as discrete, dependent operations. The ambiguity in the requirements requires an approach that sequences clarification before generation.
B. This is incorrect because few-shot prompting provides examples to guide output format and style but does not incorporate a conflict resolution step before test case generation. Providing prior examples does not address the need to first identify and resolve ambiguities in the current requirements. This technique improves output quality but does not sequence conflict clarification as a prerequisite step.
C.This is correct because prompt chaining sequences the conflict clarification step as a prerequisite output that informs the subsequent test case generation prompt, directly addressing the need to resolve ambiguities before generating accurate test cases, as per reference 2.2.5. This approach decomposes the task into dependent stages that match the tester's stated requirement. The sequential dependency between clarification and generation is the defining advantage of this technique for this scenario.
D. This is incorrect because role prompting establishes a persona but does not create a structured sequence that ensures ambiguities are resolved before test cases are generated. Without a chaining mechanism, the model may generate test cases based on misinterpreted conflicting rules despite the assigned role. Role prompting affects response style rather than enforcing a task dependency sequence.

Question 2 (Knowledge-based):
Which term BEST describes the behavior of a generative AI system that produces a confident and fluent response containing information that is factually incorrect or entirely fabricated?

Options:

A. Bias

B. Reasoning error

C. Hallucination

D. Non-determinism

Answer: C

Explanation:
A: This is incorrect because bias refers to systematic skewing of outputs based on imbalanced training data or model assumptions, producing consistently skewed rather than entirely fabricated content. Bias does not describe the production of confident, fluent, but factually incorrect or invented information.

B: This is incorrect because a reasoning error refers to a logical flaw in the model's inferential process, such as incorrect deductions or invalid conclusions from valid premises. While related, it does not specifically describe the generation of confident, fluent, fabricated content.

C: This is correct because hallucination describes the generative AI behavior of producing responses that appear confident and fluent but contain factually incorrect or entirely fabricated content, as per reference section 3.1.1. This term specifically identifies the failure mode where the model generates plausible-sounding but false information. Other terms describe related but distinct failure modes.

D) This is incorrect because non-determinism refers to the variability in LLM outputs when given identical inputs, producing different results across runs. It describes output variability, not the specific failure of generating confidently stated but factually incorrect or fabricated content.

Question 3 (Scenario-based):

Which TWO of the following correctly identify sources of training data bias in generative AI systems as recognized in the context of software testing? (Select TWO)

Options:
A. Insufficient computational resources allocated to the model during the inference phase of operation.
B.Historical data reflecting past human decisions that encode systemic inequities or outdated practices.
C.Underrepresentation of certain demographic groups or technical domains within the datasets used to train the model.
D.Use of cloud-based deployment environments for serving the trained model to end users.

Answer: B, C

Explanation:
A) This is incorrect because computational resource allocation during inference is a performance and infrastructure concern, not a source of training data bias. Bias originates in the composition and characteristics of training datasets, not in the hardware or compute capacity available at inference time.
B) This is correct because training on historical human-generated content perpetuates existing biases embedded in that data, as per reference section 3.1.1. When LLMs are trained on records reflecting past systemic inequities or obsolete methodologies, those biases are encoded in the model's output tendencies. This is a primary recognized source of bias in generative AI systems relevant to testing applications.
C) This is correct because training datasets that underrepresent specific groups or domains produce models with skewed outputs and reduced accuracy for those contexts, as per reference section 3.1.1. This form of sampling bias is a recognized definition of training data bias in generative AI systems. In software testing, this can result in LLMs generating test cases or analyses that systematically overlook edge cases relevant to underrepresented domains.
D) This is incorrect because deployment environment selection is an operational infrastructure decision with no causal relationship to training data bias. Bias is determined by the properties of the data used during model training, not by the cloud or on-premises environment in which the model is later deployed.

Preparation Strategy & Guidance

• 6 Full-Length Mock Exams: 40 questions each, timed & scored

Why This Course is Valuable

• Designed to replicate the real ISTQB CT-GenAI exam experience — including structure, scoring logic, scenario complexity, wording precision, and cognitive depth.

Top Reasons to Take These Practice Exams

• 6 full-length practice exams (240 total questions)

Money-Back Guarantee

This course comes with a 30-day unconditional money-back guarantee.
If it doesn’t meet your expectations, get a full refund — no questions asked.

Who This Course is For

• Testers preparing for ISTQB CT-GenAI certification

What You’ll Learn

• Understand LLMs, transformers, and embeddings for testing

Requirements / Prerequisites

• ISTQB Foundation Level Certification (mandatory)