CT-GenAI: Certified Tester Specialist Level – Testing with Generative AIDemo

By ISTQB · Browse Mode
About the Exam

CT-GenAI covers applying generative AI and large language models across the software testing lifecycle, including prompt engineering, risk management, and LLM-powered test solutions. It is intended for testers, test analysts, test automation engineers, test managers, developers, and other professionals who use generative AI in testing, and CTFL is a prerequisite. Passing demonstrates that a candidate can use generative AI responsibly for testing tasks and contribute to a GenAI adoption strategy within an organization.

Exam Topics

  • Introduction to Generative AI for Software Testing11%
  • Prompt Engineering for Effective Software Testing38%
  • Managing Risks of Generative AI in Software Testing17%
  • LLM-Powered Test Infrastructure for Software Testing11%
  • Deploying and Integrating Generative AI in Test Organizations8%

How to Use This Practice Exam

  1. Browse — Read each question, select your answer, and reveal the explanation.
  2. Exam Mode — Simulate real exam conditions with a timed session and score report.
  3. Learn Mode — Spaced repetition schedules questions you struggle with for long-term retention.

Download the Full Exam PDF

Get every question and answer in a clean, printable PDF built for offline study. Purchase once, keep permanent access, and re-download the latest version anytime.

Get PDF
Learn More

Last updated April 25, 2026 at 2:39 PM

Topic filter
Retired questions
Question sort

QuestionQ1

Introduction to Generative AI for Software Testing

Which statement accurately characterizes Classical Machine Learning in the context of AI?

  • A It primarily relies on rule-based systems to model human decision processes.
  • B It utilizes neural networks with multiple hidden layers to automatically discover patterns in raw data.
  • C It processes vast textual datasets to generate new, human-like content by mimicking learned patterns.
  • D It employs a data-driven methodology that involves explicit data preparation and feature engineering.
Explanation

Classical machine learning is data-driven and commonly requires explicit data preparation and manual feature engineering to transform raw inputs into useful model features.

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ2

Introduction to Generative AI for Software Testing

Which of the following statements BEST characterizes the purpose of embeddings in Large Language Models (LLMs) when processing and generating content?

  • A They define the maximum length of text an LLM can process at one time to maintain coherence.
  • B They break down input text into smaller units, such as words or sub-words, for individual processing.
  • C They are numerical representations of tokens that capture their semantic, syntactic, and contextual relationships.
  • D They control the randomness or creativity of an LLM's outputs during text generation.
Explanation

Embeddings are numerical vector representations of tokens or text that encode learned semantic and linguistic relationships, allowing the model to operate on language mathematically. OpenAI describes an embedding as a vector of floating-point numbers whose distances represent text relatedness.

Learn more

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ3

Introduction to Generative AI for Software Testing

In the context of Large Language Models (LLMs), which of the following statements (i–v) about tokenization and the context window are correct?

  1. Tokenization is the process of splitting textual input into smaller units called tokens.
  2. The context window determines the maximum number of tokens an LLM can consider at one time to maintain coherence.
  3. Increasing an LLM’s context-window size generally reduces computational complexity and processing time.
  4. Tokenization primarily consists of converting tokens into high-dimensional vectors to capture their semantic relationships.
  5. A larger context window enables an LLM to maintain coherence across longer passages, such as when analyzing large test logs.
  • A i, ii, and iii
  • B i, ii, and v
  • C ii, iv, and v
  • D iii, iv, and v
Explanation

Tokenization breaks text into tokens, while the context window limits how many tokens the model can process together. Expanding that window allows more of a long passage or log to remain available for coherent analysis, but it generally increases computational cost. Converting tokens into high-dimensional vector representations is the embedding step, not tokenization.

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ4

Introduction to Generative AI for Software Testing

In the context of LLMs, which statement BEST describes an instruction-tuned Large Language Model?

  • A It is a general-purpose model primarily used for broad tasks without specific adaptation.
  • B It is specifically designed to perform complex multi-step logical inference and problem-solving.
  • C It is a foundation model that has been further refined to better follow human instructions and user intent.
  • D It can create new content like images or code by learning patterns from diverse training data.
Explanation

An instruction-tuned LLM is a foundation model that has undergone additional training to respond more effectively to human instructions and align with user intent.

Community Discussion

No comments yet. Be the first to start the discussion!

QuestionQ5

LLM-Powered Test Infrastructure for Software Testing

A test team is reviewing screenshots of a mobile application's GUI together with textual defect reports to identify visual inconsistencies that the reports do not capture.

Which type of Generative AI model is MOST appropriate to help with this task?

  • A Symbolic AI, due to its rule-based system for mimicking human decision-making.
  • B Classical Machine Learning, as it excels at data-driven defect categorization.
  • C Multimodal LLMs, specifically those augmented with vision-language models.
  • D Reasoning LLMs, given their emphasis on logical inference for complex problem-solving.
Explanation

Multimodal LLMs with vision-language capabilities can analyze GUI screenshots and relate their visual details to textual defect reports. This enables them to detect visual inconsistencies that are absent from, or inadequately described by, the text.

Community Discussion

No comments yet. Be the first to start the discussion!

That's the end of the preview

It's free

100% of the questions are free for all users.
No strings attached.

Sign up freeLog in
Topics covered
Introduction to Generative AI for Software TestingPrompt Engineering for Effective Software TestingManaging Risks of Generative AI in Software TestingLLM-Powered Test Infrastructure for Software TestingDeploying and Integrating Generative AI in Test Organizations
Know a question that should be here? Contribute to this exam
Back home