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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing Activities for Generative AI | 30% | - Prompt-Based Testing
|
| Topic 2: Fundamentals of Generative AI | 20% | - AI Development Lifecycle
|
| Topic 3: Tools for Testing Generative AI | 20% | - Using Tools for Common Testing Activities
|
| Topic 4: Risks and Testing Challenges for Generative AI | 30% | - Testing Challenges for Generative AI
|
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NEW QUESTION # 18
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
Answer: B
Explanation:
While a basic chatbot is primarily designed for textual interaction and information retrieval, anLLM- powered agent(or AI Agent) is characterized by itsagency-the ability to use tools and trigger actions in the external world. In a software testing context, an agent does not just "talk" about testing; it can actually perform testing tasks. For example, an agent could be given the goal to "verify the login module," and it would independently decide to call an API, generate a test script, execute it against a test environment, and then analyze the results to report a bug in Jira. This ability totrigger automated actions(Option C) through
"function calling" or tool integration is what makes agents far more powerful than simple conversational interfaces (Option D). Agents can reason about "how" to achieve a goal, selecting the appropriate tools (like Selenium, Postman, or specialized internal utilities) to complete the task. This moves the AI from being a passive advisor to an active participant in the test automation ecosystem, requiring testers to focus more on goal definition and result validation.
NEW QUESTION # 19
Which of the following is NOT a valid form of LLM-driven test data generation?
Answer: D
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 # 20
A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?
Answer: D
Explanation:
This scenario describes a form ofRequest Manipulation, specifically a type of "Prompt Injection" or
"Adversarial Prompting." In this attack vector, the user (or an external attacker) provides malicious or deceptive input-in this case, via an image in a multimodal LLM-to bypass the model's intended constraints or to steer its logic toward an unintended outcome. By crafting an image that tricks the LLM into seeing
"acceptance criteria" that aren't actually there, the attacker manipulates the model's request processing to generate false validation results. This is different fromData Poisoning(Option A), which involves corrupting the training data before the model is even built. It is also distinct fromData Exfiltration(Option B), which aims to steal data from the model. In a testing environment, request manipulation is a significant risk because it can lead to "Silent Failures," where the AI reports that tests have passed or requirements are met based on deceptive input, thereby compromising the integrity of the entire Quality Assurance process.
NEW QUESTION # 21
Which setting can reduce variability by narrowing the sampling distribution during inference?
Answer: A
Explanation:
In the context of LLM inference,Temperatureis a hyperparameter that controls the randomness or
"creativity" of the model's output. When the temperature is set high, the model's probability distribution is
"flattened," meaning it is more likely to select less-probable tokens, leading to more diverse and sometimes unpredictable text. For software testing, where precision and repeatability are paramount,lowering the temperature(Option C) is the standard practice. A temperature of 0.0 makes the model "deterministic," meaning it will consistently choose the token with the highest probability. This narrows the sampling distribution and significantly reduces variability between runs. While a larger context window (Option D) allows the model to process more information, it does not directly control the randomness of token selection.
Similarly, the "learning rate" (Option B) is a parameter used during thetrainingorfine-tuningphase, not during inference. For generating test cases or scripts that must follow strict logic, a lower temperature ensures that the model remains focused and produces consistent results.
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: 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 # 23
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