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問題 #15
A law firm wants to use Amazon Bedrock foundation models (FMs) to review and negotiate business contracts. The law firm requires customer data and business information to be protected.
Which option is a potential legal risk of this use case?
答案：C
解題說明：
Incorrect contract analysis represents a significant legal and operational risk when generative AI is used to review or negotiate business agreements.
Foundation models can generate plausible but inaccurate interpretations of contractual language. These errors may result in incorrect obligations, overlooked clauses, misrepresented liabilities, or inappropriate negotiation recommendations.
Such outcomes can expose a law firm and its clients to contractual disputes, financial losses, and potential professional liability.
AWS responsible AI principles emphasize accuracy, reliability, human oversight, and appropriate evaluation of AI-generated outputs, particularly in high-stakes applications.
Option B describes encryption, which protects sensitive information. Option C provides private connectivity through AWS PrivateLink. Option D improves inference performance and potentially reduces costs.
These are protective or optimization mechanisms rather than inherent legal risks.
Therefore, incorrect contract analysis is the correct answer.
問題 #16
A company is using Amazon SageMaker to develop AI models.
Select the correct SageMaker feature or resource from the following list for each step in the AI model lifecycle workflow. Each SageMaker feature or resource should be selected one time or not at all. (Select TWO.) SageMaker Clarify SageMaker Model Registry SageMaker Serverless Inference
答案：
解題說明：
Reference:
AWS SageMaker Documentation: Model Registry (https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html) AWS SageMaker Documentation: Serverless Inference (https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-inference.html) AWS AI Practitioner Study Guide (conceptual alignment with SageMaker features for model lifecycle management and inference) Let's format this question according to the specified structure and provide a detailed, verified answer based on AWS AI Practitioner knowledge and official AWS documentation. The question focuses on selecting an AWS database service that supports storage and queries of embeddings as vectors, which is relevant to generative AI applications.
問題 #17
A company wants to detect unauthorized API actions for foundation models (FMs) in Amazon Bedrock.
Which solution meets these requirements?
答案：B
解題說明：
AWS CloudTrail provides auditing and monitoring capabilities by recording API activity across AWS services, including Amazon Bedrock.
CloudTrail events can include details such as the requesting identity, API operation, request timestamp, source IP address, and relevant error information.
These records help security teams investigate unauthorized or suspicious attempts to access Amazon Bedrock resources.
CloudTrail captures Bedrock management events, while certain data events require additional logging configuration. Organizations can analyze these events and integrate monitoring workflows with other AWS security services.
AWS Trusted Advisor provides infrastructure optimization and security recommendations but does not directly audit Bedrock model inputs and outputs.
Amazon QuickSight provides business intelligence capabilities, while Amazon Personalize generates personalized recommendations.
Therefore, AWS CloudTrail is the appropriate service for monitoring API activity and investigating unauthorized access attempts involving foundation models.
問題 #18
A company is developing a new foundation model (FM) for multilingual text generation. The model must work across multiple languages and dialects. The model must adhere to the principles of responsible AI.
Which model training process will meet these requirements?
答案：A
解題說明：
Continued pre-training extends the training of an existing foundation model by exposing it to additional datasets that improve its knowledge and capabilities.
For multilingual text generation, a diverse dataset containing multiple languages and dialects helps the model learn different linguistic structures, expressions, and communication patterns.
Including representative language variations also supports responsible AI objectives by reducing the risk that particular linguistic communities are underrepresented.
Amazon Bedrock supports continued pre-training for compatible foundation models using additional unlabeled training data.
Prompt management alone does not improve the model ' s underlying multilingual knowledge. Training separate models and combining them introduces unnecessary complexity.
Model distillation from a single-language teacher may also restrict linguistic diversity.
Therefore, continued pre-training on a representative multilingual dataset is the most appropriate strategy, although fairness and performance must still be evaluated separately.
問題 #19
A financial company is developing a generative AI application for loan approval decisions. The company needs the application output to be responsible and fair.
Which solution meets these requirements?
答案：C
解題說明：
The verified answer is A. Review the training data to check for biases. Include data from all demographics in the training data. The application is being used for loan approval decisions, so fairness is critical. AWS Machine Learning Lens guidance states that teams should analyze whether training data adequately represents the diversity of the user population and check for existing biases in labels or features that could be perpetuated by the model. AWS also recommends evaluating and preparing representative training data, analyzing training data for potential biases, verifying that the data accurately represents the population on which the model will be deployed, and addressing representation gaps.
This directly matches option A. For loan decisions, biased or unrepresentative training data can cause unfair outcomes for demographic groups. Reviewing the training data and including representative data from all demographics helps reduce the risk that the model learns patterns that disadvantage underrepresented groups.
Option B is incorrect because a deep learning model with many hidden layers does not automatically make a system fair. In fact, more complex models can be harder to interpret and may still learn biased patterns from biased data.
Option C is incorrect because secrecy conflicts with responsible AI principles. Financial loan decisions often require transparency, explainability, governance, and auditability. Hiding the decision process does not make outputs fair.
Option D is incorrect because monitoring only a static test dataset is insufficient. A static dataset may not represent changing real-world populations, drift, or emerging bias. AWS guidance recommends tracking fairness metrics over time and detecting emerging bias in deployment.
Therefore, the correct solution is to review and balance representative training data across demographics.
問題 #20
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