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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering for Effective Software Testing | 35% | - Iterative refinement and evaluation of prompts - Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts |
| Topic 2: Managing Risks of Generative AI in Software Testing | 25% | - Hallucinations, bias, inaccuracy, and consistency risks - Data privacy, security, and compliance concerns - Validation, verification, and mitigation strategies |
| Topic 3: LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - AI agents and integration with test tools - Architecture and deployment considerations |
| Topic 4: Introduction to Generative AI for Software Testing | 15% | - Use cases across the testing lifecycle - Capabilities and limitations relevant to testing - Core concepts: Generative AI, LLMs, foundation models |
| Topic 5: Deploying and Integrating GenAI in Test Organisations | 15% | - Measuring value and continuous improvement - Roles, skills, and team readiness - Strategy, governance, and adoption roadmap |
>> Valid CT-GenAI Exam Objectives <<
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NEW QUESTION # 30
Which of the following is NOT a valid form of LLM-driven test data generation?
Answer: B
Explanation:
Generative AI is exceptionally capable of creating structured and unstructured data, but its role is limited to
"generation" and "transformation," not infrastructure management or direct database administration. Creating production database backups (Option A) is a physical data management task involving the copying of actual stateful data from a server to storage; this is handled by database management systems (DBMS) and DevOps pipelines, not LLMs. Conversely, LLMs excel at the logic-based tasks listed in the other options. They can analyze requirements to identify and set boundary values (Option B) for input validation. They are also highly effective at creating combinatorial data (Option C), such as pairwise or all-combinations tables, by understanding the relationships between variables. Finally, one of the most powerful uses of GenAI in testing is generating synthetic datasets (Option D)-creating "fake" but realistically structured data that mimics production patterns without exposing Sensitive Personally Identifiable Information (SPII), thereby supporting privacy-compliant testing.
NEW QUESTION # 31
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?
Answer: A
Explanation:
The statement that "Strict GDPR compliance eliminates all privacy risk" isincorrectbecause compliance is a legal and procedural framework, not a foolproof technical shield against all possible risks. Even within a GDPR-compliant environment, risks such as "model inversion" attacks, accidental data leakage through
"membership inference," or the unintentional generation of Sensitive Personally Identifiable Information (SPII) can still occur. Data privacy in GenAI is complex because LLMs function by processing and sometimes retaining patterns from the data they are fed. As noted in the CT-GenAI syllabus, some tools may process data in ways that are not fully transparent (Option A), and outputs can inadvertently include snippets of sensitive data used during the prompting or training phase (Option B). Furthermore, failing to adhere to regulations like GDPR or the EU AI Act certainly leads to legal and financial exposure (Option D). Therefore, while compliance frameworks significantly mitigate risk, they do not "eliminate" it; a robust GenAI strategy requires ongoing technical controls, data masking, and human oversight to manage residual privacy threats effectively.
NEW QUESTION # 32
When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?
Answer: B
Explanation:
LLMOps(Large Language Model Operations) is the set of practices used to manage the lifecycle of LLMs in production. When an organization integrates an AI chatbot into its test processes, the primary operational concern ismaintaining data privacy and minimizing security risks, especially if using third-party APIs.
Unlike traditional software, LLMs are "black boxes" that process every piece of data sent to them. A core LLMOps responsibility is ensuring that any "Prompt Data" (code, requirements, or logs) is not used by the provider to train their public models and that the communication channels are fully secured. While scalability (Option A) and latency (Option C) are important technical metrics, they are secondary to the catastrophic legal and reputational risk of a data breach. LLMOps in a testing context involves implementing data masking tools, monitoring for "Prompt Injection" attacks, and managing the "Grounding" data in vector databases to ensure it remains current and protected. This ensures the AI remains a safe and reliable asset within the enterprise testing ecosystem, rather than a liability for the organization's intellectual property.
NEW QUESTION # 33
Who typically defines the system prompt in a testing workflow?
Answer: D
Explanation:
In professional Generative AI applications, thesystem prompt(sometimes called the system message) is the foundational set of instructions that defines the AI's persona, boundaries, and overall behavior. In a testing workflow, this is typically defined by atester or test engineerwho is configuring the AI assistant for a specific project. Unlike the user prompt, which changes with every interaction, the system prompt remains relatively static and acts as a "guardrail" to ensure the model stays in its role (e.g., "You are an expert in ISO
26262 automotive testing standards"). By defining the system prompt, the tester ensures that the model consistently uses specific terminology, adheres to data privacy constraints, and formats its output according to the team's requirements. While end users (Option B) provide the task-specific input, they do not usually have the permissions or technical need to alter the underlying system-level instructions. Similarly, while CI servers (Option C) might trigger the prompt, they do not "define" the human-centric logic contained within it.
Properly crafting the system prompt is a core part of setting up an AI-augmented test environment.
NEW QUESTION # 34
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: C
Explanation:
The most effective way to improve an LLM's performance on complex tasks likeimpact analysisis to provide a detailed, multi-stepInstructionorChain-of-Thoughtstructure. Option D is the best improvement because it breaks the "impact analysis" task into logical sub-tasks: mapping changes to modules, identifying related test cases, and prioritizing them based on risk and complexity. This structured approach guides the LLM through the "reasoning" steps a human expert would take, significantly reducing the likelihood of a superficial or incorrect analysis. While specifying a specialized role (Option B) or adding technical references (Option A) can help set the tone, they do not provide the model with the logical framework required to execute the task accurately. By explicitly defining theprocessthe LLM should follow, the tester ensures that the model evaluates the "depth" of the change rather than just listing files. This results in a more robust and actionable regression test suite, which is the primary goal of impact analysis in a modern software development lifecycle.
NEW QUESTION # 35
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