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問題 #369
A company wants to develop an Al application to help its employees check open customer claims, identify details for a specific claim, and access documents for a claim. Which solution meets these requirements?
答案：C
解題說明：
The company wants an AI application to help employees check open customer claims, identify claim details, and access related documents. Agents for Amazon Bedrock can automate tasks by interacting with external systems, while Amazon Bedrock knowledge bases provide a repository of information (e.g., claim details and documents) that the agent can query to respond to employee requests, making this the best solution.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Agents for Amazon Bedrock enable developers to build applications that can perform tasks by interacting with external systems and data sources. When paired with Amazon Bedrock knowledge bases, agents can access structured and unstructured data, such as documents or databases, to provide detailed responses for use cases like customer service or claims management." (Source: AWS Bedrock User Guide, Agents and Knowledge Bases) Detailed Explanation:
Option A: Use Agents for Amazon Bedrock with Amazon Fraud Detector to build the application.Amazon Fraud Detector is for detecting fraudulent activities, not for managing customer claims or accessing documents. This option is irrelevant.
Option B: Use Agents for Amazon Bedrock with Amazon Bedrock knowledge bases to build the application.
This is the correct answer. Agents for Amazon Bedrock can interact with knowledge bases to retrieve claim details and documents, enabling employees to check open claims and access relevant information.
Option C: Use Amazon Personalize with Amazon Bedrock knowledge bases to build the application.Amazon Personalize is for building recommendation systems, not for retrieving claim details or documents. This option does not meet the requirements.
Option D: Use Amazon SageMaker AI to build the application by training a new ML model.Training a new ML model on SageMaker is unnecessary and complex for this use case, as the task can be efficiently handled by Agents and knowledge bases on Amazon Bedrock.
References:
AWS Bedrock User Guide: Agents and Knowledge Bases (https://docs.aws.amazon.com/bedrock/latest
/userguide/agents.html)
AWS AI Practitioner Learning Path: Module on Generative AI and Knowledge Bases Amazon Bedrock Developer Guide: Building AI Applications (https://aws.amazon.com/bedrock/)
問題 #370
A company wants to build a customer-facing generative AI application. The application must block or mask sensitive information. The application must also detect hallucinations.
Which solution will meet these requirements with the LEAST operational overhead?
答案：B
解題說明：
Comprehensive and Detailed Explanation (AWS AI documents):
AWS recommends using managed, purpose-built services to enforce safety, compliance, and responsible AI controls in generative AI applications in order to minimize operational complexity and maintenance effort.
Amazon Bedrock Guardrails are specifically designed to help customers:
Block or mask sensitive information, such as personally identifiable information (PII) Detect and reduce hallucinations by enforcing grounding and response constraints Apply content filters, topic restrictions, and safety policies consistently across generative AI applications Configure safeguards without building or managing custom infrastructure Because Guardrails are fully managed and integrated directly with Amazon Bedrock, they require minimal setup, no custom code for policy enforcement, and no infrastructure management, resulting in the least operational overhead.
Why the other options are less suitable:
A). AWS Lambda policy evaluator requires custom logic, testing, monitoring, and ongoing maintenance.
B). FM default policies alone are insufficient because they do not provide application-specific masking, hallucination detection, or configurable governance controls.
D). Custom EC2-based policy evaluators introduce the highest operational overhead due to server management, scaling, patching, and monitoring.
AWS AI Study Guide References:
Amazon Bedrock overview and safety features
Amazon Bedrock Guardrails for responsible generative AI
AWS best practices for building secure and governed generative AI applications
問題 #371
A company deploys a foundation model (FM). The company notices that the FM is producing answers to user- submitted questions about politics. The company wants to ensure that the model does not send answers to political questions to users.
Which AWS solution will meet this requirement?
答案：B
解題說明：
The verified answer is A. Amazon Bedrock Guardrails . The requirement is to prevent a deployed foundation model from returning answers about a specific unwanted topic: politics. AWS documentation states that Amazon Bedrock Guardrails provides configurable safeguards to help you build safe generative AI applications . It also explains that guardrails provide safety and privacy controls across foundation models and help detect and filter undesirable content in user inputs and model responses. Most importantly for this question, AWS specifically identifies denied topics as a guardrail capability. With denied topics, you can define a set of topics that are undesirable in the context of your application, and the filter helps block them if they are detected in user queries or model responses. That exactly matches the scenario because the company wants political questions blocked before answers are sent to users.
Amazon Bedrock Agents is incorrect because agents are used to build applications that can reason, orchestrate tasks, call APIs, and connect to knowledge sources or action groups. Agents do not primarily exist to block topic categories in model input or output. Amazon SageMaker Clarify is incorrect because AWS describes SageMaker Clarify as a service for fairness, model explainability, feature attribution, and bias detection. It is useful for understanding model behavior and detecting bias, but it is not the correct control for filtering political responses from a generative AI application.
Amazon SageMaker Model Monitor is also incorrect because it monitors deployed model quality, data quality, bias drift, and feature attribution drift. Monitoring can identify problems after deployment, but it does not directly enforce real-time content filtering for foundation model responses. The question asks for a solution that ensures political answers are not sent to users. AWS Bedrock Guardrails is purpose-built for that control by applying safeguards to FM inputs and outputs.
問題 #372
A company needs to perform several ML tasks. The company wants to select the appropriate metric for each task.
Select the correct metric from the following list for each task. Select each metric one time or not at all. (Select THREE.)
答案：A,C,D
解題說明：
Evaluate the quality of machine-translated text by using N-gram overlap with a reference translation.
Measure the semantic similarity between a generated response and a reference response.
Measure how well a binary classifier correctly identifies the positive class.
Explanation:
The three tasks require different evaluation metrics because they assess different characteristics of model output.
For machine translation, the correct metric is BLEU. AWS describes BLEU as a metric that "Measures n-gram overlap, focusing on precision." AWS identifies machine translation as its typical use case. BLEU compares sequences of words or tokens in a candidate translation with one or more reference translations. Higher agreement of relevant N-grams generally produces a better score. Therefore, the first task maps to B.
For semantic similarity, BERTScore is appropriate. AWS explains that BERTScore uses a BERT-family model to create sentence embeddings and compares them using cosine similarity. Unlike simple lexical overlap, this allows semantically related phrases to receive similarity credit even when the exact words differ. Thus, measuring semantic similarity between a generated response and a reference response maps to A.
For a binary classifier, Precision evaluates the reliability of positive predictions. AWS defines it as the fraction of actual positive instances among the examples predicted as positive. In formula form:
Precision = TP / (TP + FP)
A high precision score means that when the classifier predicts the positive class, that prediction is frequently correct. Therefore, the third task maps to C.
R² is not used for any of these tasks. R², or the coefficient of determination, is fundamentally a regression metric used to measure how much variation in a numerical target is explained by a regression model.
The verified mapping is therefore:
Machine translation using N-gram overlap → B. BLEU
Semantic similarity → A. BERTScore
Positive-class prediction quality → C. Precision
問題 #373
A company wants to use Amazon Q Business for its dat
a. The company needs to ensure the security and privacy of the data. Which combination of steps will meet these requirements? (Select TWO.)
答案：B,D
解題說明：
The correct answers are A and E because both directly align with AWS best practices for securing generative AI services and data privacy in enterprise applications.
From the AWS Amazon Q Business documentation:
"AWS Key Management Service (KMS) integrates with Amazon Q Business to encrypt sensitive data at rest. You can use customer-managed KMS keys to meet compliance requirements." And:
"You must configure IAM access controls to manage which users and applications can access Amazon Q Business indexes, ensuring that only authorized users can retrieve information." Explanation of other options:
B . Cross-account access is not a common requirement for internal enterprise use of Amazon Q Business unless explicitly sharing data across organizations. It's not a requirement for securing access.
C . Amazon Inspector is a vulnerability management tool for EC2 and containers. It is unrelated to Amazon Q authentication or security.
D . Allowing public access would violate security and privacy principles and directly contradict the stated requirement.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Q Business Developer Guide - Security and Identity Management
AWS KMS Documentation - Integration with Bedrock and Amazon Q
AWS Certified Machine Learning Specialty Guide - Responsible AI and Governance Section
問題 #374
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