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| Section | Weight | Objectives |
|---|---|---|
| LLM-Powered Test Infrastructure | 10% | - AI agents and integration with test tools - Architecture and deployment considerations - RAG, fine-tuning, and model adaptation |
| Introduction to Generative AI for Software Testing | 15% | - Core concepts: Generative AI, LLMs, foundation models - Capabilities and limitations relevant to testing - Use cases across the testing lifecycle |
| Deploying and Integrating GenAI in Test Organisations | 15% | - Strategy, governance, and adoption roadmap - Measuring value and continuous improvement - Roles, skills, and team readiness |
| Managing Risks of Generative AI in Software Testing | 25% | - Data privacy, security, and compliance concerns - Validation, verification, and mitigation strategies - Hallucinations, bias, inaccuracy, and consistency risks |
| Prompt Engineering for Effective Software Testing | 35% | - Principles and structure of effective prompts - Prompt patterns for test design, data generation, automation - Iterative refinement and evaluation of prompts |
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NEW QUESTION # 31
You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?
Answer: B
Explanation:
Prompt Chainingis a technique where a complex task is decomposed into several smaller, sequential steps, where the output of one step serves as the context or input for the next. This is far more reliable than a "one- shot" approach (Option A) because it reduces the cognitive load on the LLM and allows for intermediate verification. In the scenario of test analysis, the most logical and effective chain begins by extracting discrete test conditionsfrom the raw requirements. Once these conditions are established, the next "link" in the chain is toprioritize them based on risk(impact and likelihood), which requires the model to reason specifically about the importance of each condition. The final step is tomap these prioritized conditions back to the original requirementsto identify any "coverage gaps." This systematic flow (Option B) mirrors the professional test analysis process defined in the ISTQB/CT-GenAI standards. By following this sequence, the tester ensures that the AI-generated output is logically derived and thorough, providing a clear "audit trail" from the initial requirement to the final prioritized test suite.
NEW QUESTION # 32
Consider applying the meta-prompting technique to generate automated test scripts for API testing. You need to test a REST API endpoint that processes user registration with validation rules. Which one of the following prompts is BEST suited to this task?
Answer: C
Explanation:
Option A is the superior choice because it strictly adheres to thestructured prompting patternrecommended in the CT-GenAI syllabus. This pattern divides the prompt into six distinct components:Role, Context, Instruction, Input Data, Constraints, and Output Format.By specifying theRole(Senior Test Automation Engineer), the model accesses relevant technical knowledge. TheInstructionis specific about using pytest and the requests library, and it explicitly lists both positive and negative scenarios. Most importantly, the Constraintssection provides the necessary "guardrails" for the code structure, such as the use of fixtures and clear assertions. Options B, C, and D are increasingly vague and fail to provide the model with the necessary technical boundaries to produce "production-ready" testware. Structured prompting reduces the "probabilistic drift" of the model, ensuring the output is not just functional code, but a script that follows industry-standard testing patterns (like modularity and clean naming conventions), making it directly usable within a CI/CD pipeline.
NEW QUESTION # 33
In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.
Answer: C
Explanation:
Understanding the hierarchy of LLM types is vital for selecting the right tool for specific testing tasks.
Foundation LLMsare trained on massive datasets to predict the next token; they excel at broad, creative
"ideation" (Statement i) but often struggle with following specific instructions or constraints (making Statement iv incorrect).Instruction-tuned LLMshave undergone additional training (Fine-tuning) to follow explicit commands and templates. They are highly effective at structured tasks like converting requirements into Gherkin feature files (Statement ii).Reasoning LLMs(or those utilizing specialized prompting like Chain- of-Thought) are designed to handle complex, multi-stage logic. This makes them the superior choice for diagnostic tasks like root-cause analysis, where the model must synthesize information across logs and requirements to find a defect's origin (Statement iii). Statement v is incorrect because while instruction-tuned models are capable, complex "stepwise reasoning" usually requires specific prompting techniques or the inherent logic of specialized reasoning models. Therefore, the combination of i, ii, and iii represents the correct alignment of model capability to testing functionality.
NEW QUESTION # 34
An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?
Answer: A
Explanation:
This scenario describes a failure in the model's ability to apply logical weight to specific domain concepts, specifically in the context of Risk-Based Testing (RBT). When an LLM ranks a low-impact UI element (a tooltip) higher than a critical functional failure (payment processing), it demonstrates a "Reasoning error in risk calculation logic." While LLMs can follow formulas like $Risk = Likelihood \times Impact$, they may lack the deep semantic understanding of "Impact" within a specific business domain unless explicitly guided.
This is not necessarily a hallucination (Option C), as the model isn't necessarily inventing facts, but rather misapplying the logic of prioritization. It is also distinct from dataset bias (Option D), which would involve a systematic skewing across all outputs. In professional testing, this type of error highlights the necessity of
"human-in-the-loop" verification. Testers must review AI-generated prioritizations to ensure that the logical deductions align with the actual business risk and technical criticality of the features being tested.
NEW QUESTION # 35
An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?
Answer: B
Explanation:
This scenario describes a specialized form ofData Exfiltration(specifically targeting the model's internal
"weights" or training memory). While data exfiltration usually refers to stealing data from a database, in the context of LLMs, it can also refer to techniques that force the model to "reveal" sensitive information it was trained on or data that exists within its current context window. By using long, repetitive, or specifically
"crafted" prompts to overwhelm the model's normal attention mechanisms or safety filters, an attacker may cause the model to output verbatim snippets of proprietary information, PII, or internal documentation that should have remained confidential. This is different fromRequest Manipulation(Option D), which aims to change the model's behavior, orData Poisoning(Option A), which happens during training. In testing, this risk is high when models are fine-tuned on private company repositories. Testers must be aware that if a model is accessible to unauthorized users, those users might use adversarial prompting techniques to extract sensitive code or business logic through these types of data leakage attacks.
NEW QUESTION # 36
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