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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering for Effective Software Testing | 35% | - Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts - Iterative refinement and evaluation of prompts |
| LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - AI agents and integration with test tools - Architecture and deployment considerations |
| Managing Risks of Generative AI in Software Testing | 25% | - Validation, verification, and mitigation strategies - Data privacy, security, and compliance concerns - Hallucinations, bias, inaccuracy, and consistency risks |
| 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% | - Roles, skills, and team readiness - Measuring value and continuous improvement - Strategy, governance, and adoption roadmap |
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NEW QUESTION # 20
Which option BEST differentiates the three prompting techniques?
Answer: D
Explanation:
Differentiating between prompting techniques is essential for a tester to select the right tool for the task.Few- shot promptingis characterized by providing the model with a few examples of inputs and desired outputs, allowing it to learn the pattern and format.Prompt Chaininginvolves breaking a complex task into a sequence of smaller, interconnected prompts, where the output of one step becomes the input for the next (e.g., first extract requirements, then generate test cases from those requirements).Meta-promptingis a more advanced technique where the user asks the LLM to help design, write, or refine the prompt itself-essentially using the AI as a "prompt engineer" to optimize the instructions. Option D correctly identifies these core characteristics.
Options A, B, and C contain fundamental mischaracterizations: for instance, Few-shotrequiresexamples (contradicting A), and Chaining is theoppositeof a single prompt (contradicting A). Mastering these distinctions allows testers to move from simple "chatting" to sophisticated AI orchestration that can handle complex, multi-stage testing workflows with high reliability.
NEW QUESTION # 21
Which concept refers to breaking text into smaller units for processing by LLMs?
Answer: A
Explanation:
Tokenizationis the foundational process by which an LLM breaks down raw text into smaller, manageable units called "tokens." These tokens can represent individual words, parts of words (sub-words), or even punctuation marks. This is a critical step because LLMs do not "read" words like humans do; they process numerical representations of these tokens. The way text is tokenized directly impacts the model's efficiency and its ability to understand complex technical terminology used in software testing. For example, a rare technical term might be broken into several sub-word tokens. This process is closely linked to theContext Window(Option C), which is the maximum number of tokens a model can "remember" or process at one time. WhileEmbeddings(Option B) are the numerical vectors that represent the meaning of these tokens, and theTransformer(Option A) is the underlying architecture that processes them, tokenization is the specific mechanism for initial text decomposition. Understanding tokenization is vital for testers when managing long requirement documents to ensure they do not exceed the model's limits.
NEW QUESTION # 22
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: B
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 # 23
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
Answer: C
NEW QUESTION # 24
When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?
Answer: C
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 # 25
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