To achieve the professional designation of ISTQB Certified Tester - Testing with Generative AI from the ISTQB, candidates must clear the CT-GenAI Exam with the minimum cut-off score. For those who wish to pass the ISTQB Testing with Generative AI certification exam with good percentage, please take a look at the following reference document detailing what should be included in ISTQB CT - Testing with Generative AI Exam preparation.
The ISTQB CT-GenAI Exam Summary, Body of Knowledge (BOK), Sample Question Bank and Practice Exam provide the basis for the real ISTQB Certified Tester - Testing with Generative AI (CT-GenAI) exam. We have designed these resources to help you get ready to take ISTQB Certified Tester - Testing with Generative AI (CT-GenAI) exam. If you have made the decision to become a certified professional, we suggest you take authorized training and prepare with our online premium ISTQB Testing with Generative AI Practice Exam to achieve the best result.
ISTQB CT-GenAI Exam Summary:
| Exam Name | ISTQB Certified Tester - Testing with Generative AI |
| Exam Code | CT-GenAI |
| Exam Fee | USD $199 |
| Exam Duration | 60 Minutes |
| Number of Questions | 40 |
| Passing Score | 65% |
| Format | Multiple Choice Questions |
| Schedule Exam | Pearson VUE |
| Sample Questions | ISTQB CT - Testing with Generative AI Exam Sample Questions and Answers |
| Practice Exam | ISTQB Certified Tester - Testing with Generative AI (CT-GenAI) Practice Test |
ISTQB Testing with Generative AI Syllabus Topics:
| Topic | Details |
|---|---|
Introduction to Generative AI for Software Testing - 100 minutes |
|
| Generative AI Foundations and Key Concepts |
- Recall different types of AI: symbolic AI, classical machine learning, deep learning, and generative AI - Explain the basics of generative AI and large language models - Distinguish between foundation, instruction-tuned and reasoning LLMs - Write and execute a given prompt addressing a test task using a multimodal LLM model |
| Leveraging Generative AI in Software Testing: Core Principles |
- Give examples of key LLM capabilities for test tasks - Compare interaction models when using GenAI for software testing |
Prompt Engineering for Effective Software Testing - 365 minutes |
|
| Effective Prompt Development |
- Give examples of the structure of prompts used in generative AI for software testing - Differentiate core prompting techniques for software testing - Distinguish between system prompts and user prompts |
| Applying Prompt Engineering Techniques to Software Test tasks |
- Apply generative AI to test analysis tasks - Apply generative AI to test design and test implementation tasks - Apply generative AI to test monitoring and control task - Select and apply appropriate prompting techniques for a given context and test task |
| Evaluate Generative AI Results and Refine Prompts for Software Test Tasks |
- Understand the metrics for evaluating the results of generative AI on test tasks - Give examples of techniques for evaluating and iteratively refining prompts |
Managing Risks of Generative AI in Software Testing - 160 minutes |
|
| Hallucinations, Reasoning Errors and Biases |
- Recall the definitions of hallucinations, reasoning errors and biases in Generative AI systems - Identify hallucinations, reasoning errors and biases in LLM output - Summarize mitigation techniques for GenAI hallucinations, reasoning errors and biases in software test tasks - Recall mitigation techniques for non-deterministic behavior of LLMs |
| Data Privacy and Security Risks of Generative AI in Software Testing |
- Explain key data privacy and security risks associated with using generative AI in software testing - Give examples of data privacy and vulnerabilities in using Generative AI in software testing - Summarize mitigation strategies to protect data privacy and enhance security in Generative AI for software testing |
| Energy Consumption and Environmental Impact of Generative AI for Software Testing | - Explain the impact of task characteristics and model usage on the energy consumption of Generative AI in software testing |
| AI Regulations, Standards and Best Practice Frameworks | - Recall examples of AI regulations, standards and best practice frameworks relevant to Generative AI in software testing |
LLM-Powered Test Infrastructure for Software Testing - 110 minutes |
|
| Architectural Approaches for LLM-Powered Test Infrastructure |
- Explain key architectural components and concepts of LLM-powered test infrastructure - Summarize Retrieval-Augmented Generation - Explain the role and application of LLM-powered agents in automating test processes |
| Fine-Tuning and LLMOps: Operationalizing Generative AI for Software Testing |
- Explain the fine-tuning of language models for specific test tasks - Explain LLMOps and its role in deploying and managing LLMs for test tasks |
Deploying and Integrating Generative AI in Test organizations - 80 minutes |
|
| Roadmap for the Adoption of Generative AI in Software Testing |
- Recall the risks of shadow AI - Explain the key aspects to consider when defining a Generative AI strategy for software testing - Summarize key criteria for selecting LLMs/SLMs for software test tasks in a given context - Recall key phases in the adoption of Generative AI in a test organization |
| Manage Change when Adopting Generative AI for Software Testing |
- Explain the essential skills and knowledge areas required for testers to work effectively with generative AI in test processes - Recall strategies for cultivating AI skills within test teams to support the adoption of Generative AI in test activities - Recognize how test processes and responsibilities shift within a test organization when adopting Generative AI |
Both ISTQB and veterans who’ve earned multiple certifications maintain that the best preparation for a ISTQB CT-GenAI professional certification exam is practical experience, hands-on training and practice exam. This is the most effective way to gain in-depth understanding of ISTQB CT - Testing with Generative AI concepts. When you understand techniques, it helps you retain ISTQB Testing with Generative AI knowledge and recall that when needed.
