CT-GenAI Exam Preparation: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 & CT-GenAI Practice Labs

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ISQI CT-GenAI Exam Syllabus Topics:

SectionWeightObjectives
Fundamentals of Generative AI20%- Generative AI Concepts
  • 1. Emergent capabilities and limitations
  • 2. AI model behavior
  • 3. Hallucinations
  • 4. Alignment and guardrails
  • 5. Model types (Base, Instruction-tuned, RAG)
  • 6. Training data and context windows
- AI Terminology
  • 1. Large Language Models (LLMs)
  • 2. Deep Learning
  • 3. Generative AI (GenAI)
  • 4. Transformer architecture
  • 5. Machine Learning (ML)
  • 6. Artificial Intelligence (AI)
  • 7. Tokens and prompts
- AI Development Lifecycle
  • 1. Data collection, preparation, and curation
  • 2. Evaluation
  • 3. Model training and fine-tuning
  • 4. Deployment and monitoring
Tools for Testing Generative AI20%- Testing Tools Overview
  • 1. Selecting appropriate tools for specific testing needs
  • 2. Categories of GenAI testing tools
- Using Tools for Common Testing Activities
  • 1. Simulation and monitoring tools
  • 2. Security testing tools
  • 3. Model evaluation tools
  • 4. Prompt testing tools
Risks and Testing Challenges for Generative AI30%- Quality Risks Specific to Generative AI
  • 1. Incorrect or fabricated outputs (hallucinations)
  • 2. Dependency on external components
  • 3. Input sensitivity (prompt brittleness)
  • 4. Inappropriate output for the context
  • 5. Inconsistent responses across runs
  • 6. Offensive, harmful, or biased content
- Testing Challenges for Generative AI
  • 1. Coverage challenges
  • 2. Complexity of the AI component
  • 3. Test oracle problem
  • 4. Regulatory and compliance considerations
  • 5. Subjectivity of quality assessment
  • 6. Ethical testing concerns
  • 7. Non-deterministic output behavior
Testing Activities for Generative AI30%- Model and Output Evaluation
  • 1. Automated evaluation methods
  • 2. Checkpoint testing
  • 3. Output correctness and quality assessment
  • 4. Human evaluation methods
  • 5. Metamorphic testing
- Prompt-Based Testing
  • 1. Prompt engineering basics
  • 2. Test data creation with GenAI
  • 3. Test case design using prompts
- Requirements-Based Testing
  • 1. AI-related quality requirements
  • 2. Functional requirements for AI-based systems
  • 3. Non-functional requirements for AI-based systems
- Traceability and Documentation
  • 1. Documentation requirements for AI testing
  • 2. Test coverage of AI model components

>> Real CT-GenAI Testing Environment <<

ISQI CT-GenAI Exam | Real CT-GenAI Testing Environment - Ensure you a High Passing Rate of CT-GenAI Exam

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q28-Q33):

NEW QUESTION # 28
You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

Answer: D


NEW QUESTION # 29
What is a key data-related aspect when defining a GenAI strategy for testing?

Answer: A

Explanation:
A successful Generative AI strategy for testing is heavily dependent on the quality of the data used for grounding (RAG) and prompting. The principle of "Garbage In, Garbage Out" is magnified with LLMs; therefore, a key strategic pillar is the prioritization of accurate, relevant, and high-quality input data. This involves establishing defined quality procedures to ensure that the requirements, codebases, and historical defect logs fed into the model are "clean" and representative of the current system state. Strategy must avoid the "unfiltered" approach (Option C), as including contradictory or obsolete data can lead to hallucinations or irrelevant test cases. While synthetic data (Option D) is a powerful tool for privacy, it cannot entirely replace the nuanced reality found in secured enterprise data. Furthermore, legacy data (Option A) often contains valuable insights for regression testing. Consequently, the strategy should focus on building a robust data pipeline that ensures only verified, contextually appropriate information is utilized, thereby increasing the reliability of AI-generated testware and ensuring it aligns with the organization's quality standards.


NEW QUESTION # 30
The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

Answer: A

Explanation:
In the evaluation of GenAI outputs for testing, metrics must align with the specific nature of the task. For anomaly identification, the goal is to correctly identify true issues without an overwhelming number of false positives; therefore,Precisionis the critical metric (the ratio of true anomalies to the total flagged).
Conversely, forpartition testing(identifying valid and invalid input classes), the goal is thoroughness and ensuring no significant category is missed.Recallis the most appropriate metric here, as it measures the model's ability to "call back" or cover all possible relevant partitions from the requirement set. As highlighted in the CT-GenAI syllabus, evaluating AI effectiveness often requires a combination of these model- performance metrics. While "Accuracy" (Option D) provides a general view, it is often misleading in imbalanced testing scenarios (like anomaly detection where anomalies are rare). By using Precision and Recall together, a test organization can quantitatively assess if the AI is both trustworthy in its alerts and comprehensive in its test design coverage.


NEW QUESTION # 31
What is a primary compliance concern related to Shadow AI in organizational test environments?

Answer: B

Explanation:
Shadow AIrefers to the use of artificial intelligence tools and services within an organization without explicit approval or oversight from the IT or Security departments. In a software testing environment, this often occurs when testers use public, consumer-grade LLMs to analyze proprietary code or sensitive requirement documents to speed up their work. The primary compliance concern is theviolation of established data handling and regulatory compliance standards(such as GDPR, HIPAA, or SOC2). When sensitive test data is fed into a "shadow" AI tool, that data may be stored on external servers or used to train future iterations of the model, leading to massive data leaks and legal exposure. This bypasses the organization's security controls, such as data masking and role-based access. Unlike "authorized" AI which undergoes a rigorous vendor risk assessment, Shadow AI creates an invisible attack surface. For a test organization, mitigating this risk involves providing approved, secure AI alternatives and implementing strict policies and monitoring to ensure that internal intellectual property is never processed by unvetted external services.


NEW QUESTION # 32
You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement

Answer: C

Explanation:
A Retrieval-Augmented Generation (RAG) workflow is designed to "ground" an LLM's output in specific, verifiable data. The logical flow begins with an initial input or "focused query" (Step iv) that defines the tester's goal-in this case, generating cases for a new payments rule. The system then uses this query to perform a semantic search in avector database(Step i) to find the most relevant "chunks" of the new API specification. Following this, the system retrieves complementary data from therelational database(Step iii), such as historical test cases that might provide structural patterns or regression context. Finally, all the retrieved information-the new specs and the historical context-is bundled together and "fed" into the LLM as part of an augmented prompt (Step ii). This ensures the LLM doesn't hallucinate rules but instead synthesizes the new requirements with established organizational testing standards. Following the order in Option B ensures that the model is provided with the most relevant and logically organized context prior to generating the final testware.


NEW QUESTION # 33
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