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NEW QUESTION # 313
Which prompting attack directly exposes the configured behavior of a large language model (LLM)?
Answer: D
Explanation:
* A prompt template defines how the model is structured and guided (system prompts, roles, guardrails).
* An attack that reveals or leaks this prompt template is known as a prompt extraction attack.
* The other options (persona switching, exploiting friendliness, ignoring prompts) describe adversarial techniques but do not directly expose the internal configured behavior.
# Reference:
AWS Responsible AI - Prompt Injection & Extraction Attacks
NEW QUESTION # 314
A company has developed an ML model for image classification. The company wants to deploy the model to production so that a web application can use the model.
The company needs to implement a solution to host the model and serve predictions without managing any of the underlying infrastructure.
Which solution will meet these requirements?
Answer: D
NEW QUESTION # 315
A company needs a generative AI (GenAI) application to explain its reasoning steps before giving final answers.
Which prompt engineering technique will meet this requirement?
Answer: B
Explanation:
The verified answer is B. Chain-of-thought prompting. The requirement is for the GenAI application to explain reasoning steps before providing final answers. AWS prompt-engineering guidance identifies chain-of-thought prompting as a technique used for logical reasoning and complex problem solving. AWS guidance also recommends considering chain-of-thought or tree-of-thought prompting for logical reasoning tasks.
Chain-of-thought prompting encourages the model to work through intermediate reasoning steps before producing a final response. That matches the question exactly. The phrase "explain its reasoning steps before giving final answers" is the defining clue.
Few-shot prompting is incorrect because it provides examples of the desired input-output pattern. AWS describes few-shot prompting as including a few examples to help a model calibrate its output to meet expectations. It can guide tone, format, or task structure, but it is not specifically the technique for step-by-step reasoning.
Prompt templating is incorrect because a prompt template standardizes reusable prompt structure. Templates can include variables, instructions, and formatting rules, but the technique itself does not specifically require the model to reason step by step.
Zero-shot prompting is incorrect because it gives the model an instruction without examples. AWS guidance describes zero-shot prompting as a prompt with no example input-output pair. It is useful for direct tasks, but it does not specifically meet the reasoning-step requirement.
NEW QUESTION # 316
A company created an AI voice model that is based on a popular presenter. The company is using the model to create advertisements. However, the presenter did not consent to the use of his voice for the model. The presenter demands that the company stop the advertisements.
Which challenge of working with generative AI does this scenario demonstrate?
Answer: D
NEW QUESTION # 317
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?
Answer: C
Explanation:
Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model arrives at a certain decision, making it suitable for classifying genes into categories.
* Option A (Correct): "Decision trees": This is the correct answer because decision trees provide a clear and interpretable representation of how input features influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions.
* Option B: "Linear regression" is incorrect because it is used for regression tasks, not classification.
* Option C: "Logistic regression" is incorrect as it does not provide the same level of interpretability in documenting decision-making processes.
* Option D: "Neural networks" is incorrect because they are often considered "black boxes" and do not easily explain how they arrive at their outputs.
AWS AI Practitioner References:
* Interpretable Machine Learning Models on AWS: AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.
NEW QUESTION # 318
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