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NEW QUESTION # 20
A bank is building a chatbot to answer customer questions about opening a bank account. The chatbot will use public bank documents to generate responses. The company will use Amazon Bedrock and prompt engineering to improve the chatbot's responses.
Which prompt engineering technique meets these requirements?
Answer: C
Explanation:
Directional stimulus prompting guides the foundation model to produce outputs aligned with business context.
It's particularly effective for aligning responses with public documents and improving coherence. From Bedrock Prompt Engineering Techniques documentation:
"Directional stimulus prompting provides structured prompts to steer the model output towards desired formats or behaviors using specific linguistic cues."
NEW QUESTION # 21
A financial company wants to build workflows for human review of ML predictions. The company wants to define confidence thresholds for its use case and adjust the threshold over time.
Which AWS service meets these requirements?
Answer: A
Explanation:
The correct answer is B because Amazon Augmented AI (Amazon A2I) allows developers to integrate human review workflows into ML systems. It supports defining confidence thresholds, such that only low- confidence predictions are sent to human reviewers.
From AWS documentation:
"Amazon A2I provides built-in human review workflows for ML predictions. You can configure confidence thresholds to determine when human review is triggered, enabling continual adjustment based on accuracy needs." This supports use cases where business decisions (like financial approvals) require manual oversight for edge cases.
Explanation of other options:
A). Amazon Personalize is used for recommendation systems, not human review.
C). Amazon Inspector is for security assessment and vulnerability scanning.
D). AWS Audit Manager is used for compliance audits, not ML prediction reviews.
Referenced AWS AI/ML Documents and Study Guides:
* Amazon A2I Developer Guide - Confidence Threshold Configuration
* AWS ML Specialty Guide - Human-in-the-Loop Systems
* AWS Responsible AI Documentation - Review and Escalation Workflows
NEW QUESTION # 22
An ML research team develops custom ML models. The model artifacts are shared with other teams for integration into products and services. The ML team retains the model training code and dat a. The ML team wants to builk a mechanism that the ML team can use to audit models.
Which solution should the ML team use when publishing the custom ML models?
Answer: B
Explanation:
The ML research team needs a mechanism to audit custom ML models while sharing model artifacts with other teams. Amazon SageMaker Model Cards provide a structured way todocument model details, including intended uses, training data, and inference performance, making them ideal for auditing and ensuring transparency when publishing models.
Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Amazon SageMaker Model Cards enable you to document critical details about your machine learning models, such as intended uses, training data, evaluation metrics, and inference details. Model Cards support auditing by providing a centralized record that can be reviewed by teams to understand model behavior and limitations." (Source: Amazon SageMaker Developer Guide, SageMaker Model Cards) Detailed Option A: Create documents with the relevant information. Store the documents in Amazon S3.While storing documents in S3 is feasible, it lacks the structured format and integration with SageMaker that Model Cards provide, making it less suitable for auditing purposes.
Option B: Use AWS AI Service Cards for transparency and understanding models.AWS AI Service Cards are not a standard feature in AWS documentation. This option appears to be a distractor and is not a valid solution.
Option C: Create Amazon SageMaker Model Cards with Intended uses and training and inference details.This is the correct answer. SageMaker Model Cards are specifically designed to document model details for auditing, transparency, and collaboration, meeting the team's requirements.
Option D: Create model training scripts. Commit the model training scripts to a Git repository.Sharing training scripts in a Git repository provides access to code but does not offer a structured auditing mechanism for model details like intended uses or inference performance.
Reference:
Amazon SageMaker Developer Guide: SageMaker Model Cards (https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html) AWS AI Practitioner Learning Path: Module on Model Governance and Auditing AWS Documentation: Responsible AI with SageMaker (https://aws.amazon.com/sagemaker/)
NEW QUESTION # 23
A company used Amazon Bedrock to build an AI assistant. The AI assistant received a user prompt that instructed the assistant to provide the guidelines that the company used to configure the assistant. The prompt also instructed the assistant to display the full text of the initial setup. The AI assistant revealed its system prompt in the response.
Which type of prompt attack has occurred?
Answer: B
Explanation:
The attack is specifically prompt leakage because the user ' s objective is to obtain confidential system-level instructions or configuration information from the AI application. AWS defines prompt leakage as user prompts designed to "extract or reveal the system prompt, developer instructions, or other confidential configuration details." The scenario precisely matches this definition. The user asks the assistant to disclose the guidelines used to configure it and to reproduce the complete initial setup. The assistant then reveals its system prompt. The defining outcome is therefore unauthorized disclosure of protected prompt instructions.
Prompt leakage is related to the broader category of prompt attacks , and a leakage attempt can involve prompt-injection techniques. However, when AWS asks for the specific type of attack and the attacker is intentionally trying to expose the hidden system prompt or developer instructions, prompt leakage is the more precise classification.
A jailbreak generally attempts to bypass model safeguards or restrictions so the model performs behavior that would normally be prohibited. Although jailbreaks and prompt leakage can overlap, the core purpose in this scenario is disclosure of system configuration rather than simply bypassing a safety policy.
Prompt injection is the broader technique of introducing malicious instructions designed to override or manipulate existing instructions. For example, an attacker might tell the application to ignore previous instructions. AWS includes jailbreaks, prompt injections, and prompt leakages within its prompt-attack filtering capabilities.
A denied topic violation occurs when content concerns a topic explicitly configured as prohibited in Amazon Bedrock Guardrails. It does not describe system-prompt disclosure.
AWS recommends protecting generative AI applications by using Bedrock Guardrails prompt-attack detection, clearly defined system instructions, controlled access to tools and resources, input evaluation, output validation, and security testing.
Because confidential system instructions were explicitly requested and subsequently disclosed, the exact AWS-aligned classification is B. Prompt leakage .
NEW QUESTION # 24
A company wants to implement a large language model (LLM)-based chatbot to provide customer service agents with real-time contextual responses to customers' inquiries. The company will use the company's policies as the knowledge base.
Answer: A
Explanation:
* Retraining or pre-training is costly and unnecessary for just using company policies.
* Fine-tuning adapts models but is inefficient for frequently changing company documents.
* Retrieval-Augmented Generation (RAG) is the best approach - it retrieves relevant policy documents from a knowledge base and feeds them into the model context in real time, ensuring accurate and up-to-date responses.
# Reference:
AWS Documentation - RAG with Amazon Bedrock
NEW QUESTION # 25
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