ISTQB Certified Tester-Testing with Generative AI(CT-GenAI)

Explore how generative AI applies to software testing, from foundational concepts to prompt engineering and testing large language models. Examine risks, compliance, and deploying AI agents.

Explore the ISTQB exam structure: 40 questions, 60 minutes, 65% passing, and how k1 remember, k2 understand, and k3 apply questions assess memory, understanding, and application.

Explore official ISTQB resources, focusing on the syllabus and sample exams, and learn how certification enables AI testing, regulatory awareness, risk management, and leadership discussions.

Explore generative AI foundations and key concepts, with how large language models apply to software testing. Learn tokenization, embedding, context window, non-determinism, and multimodal visual language models.

Leverage generative AI to enhance software testing by analyzing requirements, generating test cases and data, automating tests, and analyzing results. Distinguish chatbot versus LLM-powered testing tools and create end-to-end documentation.

Explore ISTQB chapter one mock questions, learn to negate distractors, and apply concepts from symbolic AI, context windows, and tokenization in LLM testing.

Master effective prompt development for software testing by detailing six prompt aspects: role, context, instruction, input data, constraints, output format, and techniques like prompt chaining, few-shot prompting, and evaluation metrics.

Apply prompt engineering techniques to software testing, guiding test analysis, design and automation, regression, and monitoring with AI while selecting prompt chaining, few-shot, meta prompting, and hybrid approaches.

Evaluate generative ai outputs for software test tasks using precision, recall, accuracy, and diversity; assess contextual fit, execution success, and time efficiency, then refine prompts iteratively to improve results.

Delve into ISTQB chapter 2 mock questions, mapping input data and output format in six-part prompts, and compare few-shot, meta prompting, and prompt improvement for Gherkin test cases.

Explore the risks of ai outputs, including hallucinations, reasoning errors, and biases, and learn detection and mitigation strategies to ensure accurate, secure, and compliant testing.

Assess privacy and security risks of generative AI in software testing, including exfiltration, poisoning, and code attacks, and apply mitigations such as data minimization, anonymization, secure infrastructure, and human reviews.

Highlight environmental impact and energy demand of AI, urging text prompts to reduce energy drain, and outline regulations: UAE act, IEC 23053, IEC 42001, and NIST AI RMF.

Explore Istqb chapter 3 mock questions on hallucination definitions, bias, and handling ai outputs; analyze test-case design, data privacy, data poisoning, and responsible generative ai practices.

Explore architectural approaches for lm-powered test infrastructure, including front-end, back-end, and vector databases, plus retrieval augmented generation (rag) and fine-tuning. Examine production lm-ops, agents, and risks like bias and privacy.

Explore fine tuning and llm operations to operationalize generative ai for software testing, comparing rag and fine tuning, and addressing data privacy, vendor tools, and in-house deployments.

engages in a mock discussion for chapter four, applying Rag to align test outputs with the latest banking requirements and exploring fine-tuning and autonomous LM-powered test agents.

Explore how to adopt generative ai for software testing, set measurable objectives, select models, govern data and shadow ai risks, and manage change to enable testers as ai-assisted specialists.

Explore ISTQB Chapter 5 mock questions on generative AI testing, including Shadoweye risks, LM selection criteria, adoption phases, and roles of testers and test managers.

Register for the ISTQB exam, with costs from 150 to 250 USD, offering remote or test-center formats, 40 questions in 1 hour, closed book, 65% to pass, and camera monitoring.

Master last-minute exam techniques for ISTQB testing with generative AI by practicing timed mocks, reviewing the syllabus, using process of elimination, and staying calm.