CT-GenAI ISTQB Certified Tester - Testing with Generative AI Practice Questions
Prepare for CT-GenAI with more than an answer.
- Exam fee
- $199 USD
- Level
- Specialist
- Valid for
- Lifetime
Domains covered on the exam 5
- Introduction to Generative AI for Software Testing12%
- Prompt Engineering for Effective Software Testing43%
- Managing Risks of Generative AI in Software Testing20%
- LLM-Powered Test Infrastructure for Software Testing13%
- Deploying and Integrating Generative AI in Test Organizations12%
- 1
When applying 'Meta Prompting' in a software testing context, what is the primary objective?
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Correct answer: B
Meta prompting involves asking the model to create a prompt for a specific task. For example, 'Write a prompt that would make an LLM generate comprehensive SQL injection test vectors'. It leverages the model's knowledge of its own optimal instruction format.
- 2
Which of the following represents a 'Negative Constraint' in prompt engineering for test data generation?
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Correct answer: D
Negative constraints explicitly tell the model what not to do. This is crucial for privacy and compliance in testing, preventing the accidental leakage of real data or PII.
- 3
A QA team is analyzing a prompt used for generating regression test summaries. The prompt is: "Summarize these test logs." The output is inconsistent and misses key failures. Which component is missing from this prompt to make it effective?
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Correct answer: B
The prompt lacks specific instructions (e.g., 'Focus on ERROR and FATAL lines') and output formatting (e.g., 'Present as a bulleted list of failures with timestamps'). Without these, the model guesses the user's intent.
- 4
What is the primary risk of using 'Prompt Injection' techniques in a testing context against a GenAI-powered chatbot application?
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Correct answer: C
Prompt Injection involves crafting inputs that trick the model into ignoring its original instructions (System Prompt). In a testing context, this is a security vulnerability that must be tested for, as it can lead to data leakage or unauthorized actions.
- 5
Which of the following is a primary data privacy concern when using public LLMs for generating test data based on production samples?
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Correct answer: C
Public LLMs often use input data for training (unless opted out). Sending real production PII violates GDPR/CCPA and risks data exposure. Data must be sanitized/anonymized before use in prompts.
- 6
A financial institution is implementing a Generative AI solution to assist in creating test cases for a legacy mainframe system. The system logs are verbose, often exceeding 50,000 words per transaction cycle. The team attempts to paste these logs directly into a standard LLM to identify error patterns, but the model returns incomplete analysis or errors regarding input length. Which fundamental constraint of Large Language Models is the team encountering, and what is the most architecturaly sound resolution?
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Correct answer: C
The context window limits the amount of text (tokens) the model can process in a single interaction. Mainframe logs often exceed standard limits. Chunking the data or using extended context models is the correct architectural solution.
- 7
Which of the following scenarios best illustrates the application of a Multimodal Large Language Model (MLLM) in a software testing context?
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Correct answer: B
Multimodal models can process multiple types of input simultaneously, such as images (screenshots) and text (requirements). Comparing visual UI elements against textual specs is a prime use case for MLLMs in testing.
- 8
In the context of Generative AI for software testing, how does 'Tokenization' fundamentally impact the cost and processing of test artifacts?
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Correct answer: C
Tokenization is the process of converting text into tokens. LLMs process text in tokens, not words. API costs and context window limits are defined in tokens, making it the fundamental unit of consumption.
- 9
A test manager is explaining the difference between Discriminative AI and Generative AI to stakeholders. Which comparison accurately reflects their roles in software testing?
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Correct answer: D
Discriminative models classify data (e.g., pass/fail, spam/not spam), while Generative models create new data (e.g., generating a Python script or a Gherkin scenario) based on training distributions.
- 10
When using an LLM to generate unit tests, a developer notices the output varies significantly each time the same prompt is submitted. Which parameter should be adjusted to make the output more deterministic for regression testing purposes?
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Correct answer: A
Temperature controls the randomness of predictions. A lower temperature (near 0) makes the model more deterministic and focused, choosing the most likely next token, which is desirable for code generation consistency.
