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| Section | Objectives |
|---|---|
| Topic 1: Prompt Engineering for Testing | - Prompt design techniques
|
| Topic 2: Organizational Adoption and Governance | - Enterprise GenAI adoption
|
| Topic 3: Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
|
| Topic 4: Application of GenAI in Software Testing | - Practical use in testing workflows
|
| Topic 5: Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
|
1. You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?
A) Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.
B) Specify that the role is a test analyst with expertise in predictive analytics and risk management.
C) Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.
D) Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
2. Which technique MOST directly reduces hallucinations by grounding the model in project realities?
A) Use longer temperature settings
B) Rely on generic examples only
C) Provide detailed context
D) Randomize prompts each run
3. 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.
A) ii, iii, iv
B) i, ii, iii (Duplicate entry in original source)
C) i, iii, v
D) i, ii, iii
4. 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?
A) Add a step to review the change log for syntax errors before analysis.
B) Specify that the role is a test architect specializing in CI/CD pipelines.
C) Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
D) Include references to version control systems like Git in the constraints.
5. What does an embedding represent in an LLM?
A) Numerical vectors capturing semantic relationships
B) Tokens grouped into context windows
C) Logical rules for reasoning
D) A set of test cases for validation
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: A |
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