You have to pass the CT-GenAI exam to receive the certification from ISTQB. To increase the effectiveness of your study and make you familiar with the actual exam pattern, we have prepared this ISTQB Testing with Generative AI sample questions. Our Sample ISTQB Certified Tester - Testing with Generative AI Practice Exam will give you more insight about both the type and the difficulty level of the questions on the ISTQB CT - Testing with Generative AI exam.
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ISTQB Testing with Generative AI Sample Questions:
01. An attacker injects falsified test results into the training dataset of an LLM intended to recommend optimal test coverage strategies. What type of attack vector does this description BEST refer to?
a) Malicious code generation
b) Data exfiltration
c) Request manipulation
d) Data poisoning
02. Which TWO of the following options represent key capabilities of LLMs in test tasks?
Select TWO options.
a) Identifying ambiguities and inconsistencies in requirements.
b) Generating complete application code for deployment.
c) Automating the execution of all test scripts without human intervention.
d) Performing exploratory testing on software applications.
e) Creating diverse test data with various combinations and boundary values.
03. What is the BEST approach for cultivating skills within test teams to specifically support the adoption of Generative AI?
a) Rely mainly on external expert courses with hands-on practice, aiming to integrate AI into all daily test tasks at once.
b) Encourage independent experimentation with various LLMs without following a structured process.
c) Adopt a hands-on, gradual learning process supported by guided exercises, peer learning, and knowledge-sharing communities.
d) Rely mainly on theoretical courses from external experts, aiming to gradually integrate AI into daily test tasks in line with actual learning.
04. A tester is examining a structured prompt used to obtain LLM assistance for performance test analysis. One of the components of this prompt reads: “Test reports from performance testing tools, system monitoring logs during peak usage periods, and application performance benchmarks from previous releases”.
In which component of the six-part prompt structure would this description MOST LIKELY appear?
a) Context
b) Input data
c) Constraints
d) Output format
05. In the context of software testing, which of the following statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs excel at generating test cases from high-level requirements without structured input.
ii. Reasoning LLMs excel at creating test scripts that strictly follow predefined organizational templates.
iii. Instruction-tuned LLMs excel at autonomously prioritizing test execution based on realtime user feedback.
iv. Reasoning LLMs excel at synthesizing data from defect reports to detect trends and prioritize test efforts.
v. Instruction-tuned LLMs excel at generating test cases that adhere to Gherkin language syntax.
a) i, ii, and iii
b) ii, iii, and iv
c) i, ii, and v
d) iv, and v
06. You are leveraging Generative AI to assist in testing an entertainment software application. The Generative AI model generates test cases for user interaction scenarios, test scripts for API interactions, and synthetic test data to address edge cases.
To effectively evaluate the Generative AI model’s performance and to refine prompts, which combination of metrics and actions BEST ensures comprehensive assessment and improvement?
a) Evaluate the diversity of test cases to ensure varied input scenarios and use test execution success rate to validate the functionality of generated API test scripts.
b) Apply accuracy and completeness metrics to validate test cases against entertainment software requirements and rely on time efficiency to compare AI-generated test scripts with manual test efforts.
c) Focus on precision to ensure generated test data meets entertainment software compliance standards, while contextual fit and test execution success rate assesses the alignment and usability of test scripts.
d) Prioritize relevance and contextual fit for all outputs to maintain consistency with entertainment software requirements and include diversity metrics to expand edge case coverage.
07. Consider the realm of Large Language Models (LLMs). Which of the following options BEST explains why context window limitations affect LLM’s text processing capabilities?
Select ONE option.
a) Because context windows restrict temporal processing sequences, preventing LLMs from maintaining chronological consistency across extended text analysis.
b) Because context windows prevent cross-referencing capabilities, limiting LLMs’ ability to connect information across different document sources simultaneously.
c) Because context windows force LLMs to discard earlier information, which may contain relevant details needed for understanding later content.
d) Because context windows constrain parsing granularity levels, restricting LLMs from adjusting between character-level and document-level analysis approaches.
08. Which of the following components of an LLM-powered testing application is responsible for combining user input with structured and semantically similar data to prepare a prompt for the LLM?
Select ONE option.
a) Back-end
b) Front-end
c) Authentication component
d) Post-processing component
09. You are using Generative AI to create test cases for an e-commerce (e-shop) application. The following features have been explicitly mentioned in the project briefing:
- cart management
- discount code application
- order confirmation email generation
Based on these details, which of the following AI-generated test cases MOST LIKELY represents a hallucination?
a) Verify that a user can add multiple items to their cart and proceed to checkout.
b) Verify that a user cannot apply an expired discount code during checkout.
c) Verify that a user receives a confirmation email after successfully placing an order.
d) Verify that a user can create a wishlist to save favorite items for later.
10. Which TWO of the following standards, or parts of them, are MOST relevant to the use of Generative AI in software testing?
Select TWO options.
a) ISO/IEC 25010:2023
b) ISO/IEC 23053:2022
c) ISO/IEC/IEEE 29119-2:2021
d) ISO/IEC 42001:2023
e) ISO/IEC/IEEE 29119-3:2021
Answers:
|
Question: 01 Answer: d |
Question: 02 Answer: a, e |
Question: 03 Answer: c |
Question: 04 Answer: b |
Question: 05 Answer: d |
|
Question: 06 Answer: a |
Question: 07 Answer: c |
Question: 08 Answer: a |
Question: 09 Answer: d |
Question: 10 Answer: b, d |
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