CT-GenAI Exam Guide
This CT-GenAI exam focuses on practical knowledge and real-world application scenarios related to the subject area. It evaluates your ability to understand core concepts, apply best practices, and make informed decisions in realistic situations rather than relying solely on memorization.
This page provides a structured exam guide, including exam focus areas, skills measured, preparation recommendations, and practice questions with explanations to support effective learning.
Exam Overview
The CT-GenAI exam typically emphasizes how concepts are used in professional environments, testing both theoretical understanding and practical problem-solving skills.
Skills Measured
- Understanding of core concepts and terminology
- Ability to apply knowledge to practical scenarios
- Analysis and evaluation of solution options
- Identification of best practices and common use cases
Preparation Tips
Successful candidates combine conceptual understanding with hands-on practice. Reviewing measured skills and working through scenario-based questions is strongly recommended.
Practice Questions for CT-GenAI Exam
The following practice questions are designed to reinforce key CT-GenAI exam concepts and reflect common scenario-based decision points tested in the certification.
Question#1
Which statement BEST describes vision-language models (VLMs)?
A. VLMs are a subset of multimodal LLMs integrating visual and textual information.
B. VLMs are unrelated to multimodal LLMs and focus only on UI automation.
C. VLMs are a superset of multimodal LLMs.
D. VLMs process audio and video but not images.
Question#2
What is a key data-related aspect when defining a GenAI strategy for testing?
A. Neglect legacy data sources as they provide limited immediate relevance to testing tasks
B. Prioritize accurate and relevant input data secured through defined quality procedures
C. Aggregate data from all available organizational repositories without filtration
D. Use only auto-generated synthetic data to avoid dependency on enterprise repositories
Question#3
Which statement BEST contrasts interaction style and scope?
A. Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.
B. Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing
C. Chatbots require API integration; LLM apps do not.
D. Both are identical aside from UI theme.
Question#4
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?
A. Some GenAI tools may store/process data without explicit consent
B. GenAI outputs can accidentally reveal sensitive information present in inputs
C. Strict GDPR compliance eliminates all privacy risk
D. Using GenAI without regulatory compliance can lead to legal exposure
Question#5
Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?
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
Disclaimer
This page is for educational and exam preparation reference only. It is not affiliated with ISTQB, Certified Tester - Testing with Generative AI, or the official exam provider. Candidates should refer to official documentation and training for authoritative information.
