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| Section | Weight | Objectives |
|---|---|---|
| Applications of Foundation Models | 28% | - Describe use cases for text, image, video, and code generation - Explain integration of foundation models into applications - Recognize tools for building and deploying generative AI solutions - Identify AWS services for generative AI: Amazon Bedrock, Amazon Titan |
| Security, Compliance, and Governance for AI Solutions | 14% | - Data protection and privacy in AI workflows - Compliance requirements and regulations - Security controls for AI data and models - Governance frameworks for AI lifecycle |
| Fundamentals of AI and ML | 20% | - Identify types of ML: supervised, unsupervised, reinforcement learning - Recognize key concepts: data, models, training, inference, evaluation - Describe common ML workflows and lifecycle - Define artificial intelligence (AI), machine learning (ML), and deep learning |
| Guidelines for Responsible AI | 14% | - Define responsible AI principles: fairness, transparency, privacy, safety - Identify risks and mitigation strategies for AI systems - Explain bias detection and reduction - Describe ethical and societal impacts of AI |
| Fundamentals of Generative AI | 24% | - Define generative AI and foundation models - Describe concepts: prompts, embeddings, fine-tuning, retrieval-augmented generation (RAG) - Differentiate between generative AI and traditional ML - Explain capabilities and use cases of generative AI |
AmazonのAIF-C01認定試験に合格することはきっと君の職業生涯の輝い将来に大変役に立ちます。Xhs1991を選ぶなら、君がAmazonのAIF-C01認定試験に合格するということできっと喜んでいます。Xhs1991のAmazonのAIF-C01問題集を購入するなら、君がAmazonのAIF-C01認定試験に合格する率は１００パーセントです。あなたはXhs1991の学習教材を購入した後、私たちは一年間で無料更新サービスを提供することができます。
質問 # 116
A company wants to use its documents as a knowledge base for a large language model (LLM) in a Retrieval Augmented Generation (RAG) solution.
Which solution will meet these requirements?
正解：D
質問 # 117
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?
正解：B
解説：
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 Reference:
Amazon Bedrock overview and safety features
Amazon Bedrock Guardrails for responsible generative AI
AWS best practices for building secure and governed generative AI applications
質問 # 118
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
正解：
解説：
Explanation:
SageMaker Model Registry, SageMaker Serverless interference
This question requires selecting the appropriate Amazon SageMaker feature for two distinct steps in the AI model lifecycle. Let's break down each step and evaluate the options:
Step 1: Managing different versions of the model
The goal here is to identify a SageMaker feature that supports version control and management of machine learning models. Let's analyze the options:
SageMaker Clarify: This feature is used to detect bias in models and explain model predictions, helping with fairness and interpretability. It does not provide functionality for managing model versions.
SageMaker Model Registry: This is a centralized repository in Amazon SageMaker that allows users to catalog, manage, and track different versions of machine learning models. It supports model versioning, approval workflows, and deployment tracking, making it ideal for managing different versions of a model.
SageMaker Serverless Inference: This feature enables users to deploy models for inference without managing servers, automatically scaling based on demand. It is focused on inference (predictions), not on managing model versions.
Conclusion for Step 1: The SageMaker Model Registry is the correct choice for managing different versions of the model.
Exact Extract Reference: According to the AWS SageMaker documentation, "The SageMaker Model Registry allows you to catalog models for production, manage model versions, associate metadata, and manage approval status for deployment." (Source: AWS SageMaker Documentation - Model Registry,
https://docs.aws.amazon.com/sagemaker/latest/dg/model-registry.html).
Step 2: Using the current model to make predictions
The goal here is to identify a SageMaker feature that facilitates making predictions (inference) with a deployed model. Let's evaluate the options:
SageMaker Clarify: As mentioned, this feature focuses on bias detection and explainability, not on performing inference or making predictions.
SageMaker Model Registry: While the Model Registry helps manage and catalog models, it is not used directly for making predictions. It can store models, but the actual inference process requires a deployment mechanism.
SageMaker Serverless Inference: This feature allows users to deploy models for inference without managing infrastructure. It automatically scales based on traffic and is specifically designed for making predictions in a cost-efficient, serverless manner.
Conclusion for Step 2: SageMaker Serverless Inference is the correct choice for using the current model to make predictions.
Exact Extract Reference: The AWS documentation states, "SageMaker Serverless Inference is a deployment option that allows you to deploy machine learning models for inference without configuring or managing servers. It automatically scales to handle inference requests, making it ideal for workloads with intermittent or unpredictable traffic." (Source: AWS SageMaker Documentation - Serverless Inference, https://docs.aws.
amazon.com/sagemaker/latest/dg/serverless-inference.html).
Why Not Use the Same Feature Twice?
The question specifies that each SageMaker feature or resource should be selected one time or not at all. Since SageMaker Model Registry is used for version management and SageMaker Serverless Inference is used for predictions, each feature is selected exactly once. SageMaker Clarify is not applicable to either step, so it is not selected at all, fulfilling the question's requirements.
References:
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.
質問 # 119
Which THREE of the following principles of responsible AI are most critical to this scenario? (Choose 3)
* Explainability
* Fairness
* Privacy and security
* Robustness
* Safety
正解：
解説：
Explanation:
This question maps responsible AI principles to specific AI system practices as defined in AWS Responsible AI Guidelines and Amazon Bedrock Responsible AI documentation.
Scenario 1:
Encrypt the application data, and isolate the application on a private network.
# Principle: Privacy and Security
From AWS documentation:
"Protecting user data through encryption, secure network isolation, and access control aligns with the Responsible AI principle of privacy and security. AWS recommends securing all data used by AI systems, both in transit and at rest, to maintain trust and regulatory compliance." Scenario 2:
Evaluate how different population groups will be impacted.
# Principle: Fairness
From AWS documentation:
"The fairness principle ensures that AI models do not discriminate or generate biased outcomes across population groups. Fairness assessment involves evaluating performance metrics across demographic segments and mitigating any bias detected." Scenario 3:
Test the application with unexpected data to ensure the application will work in unique situations.
# Principle: Robustness
From AWS documentation:
"Robustness refers to an AI system's ability to maintain reliable performance under varied, noisy, or unexpected input conditions. Testing for robustness helps ensure the model generalizes well and behaves safely in edge cases." Referenced AWS AI/ML Documents and Study Guides:
* AWS Responsible AI Practices Whitepaper - Core Principles of Responsible AI
* Amazon Bedrock Documentation - Responsible AI and Safety Controls
* AWS Certified Machine Learning Specialty Guide - AI Governance and Model Evaluation
質問 # 120
A company is using Amazon SageMaker Studio notebooks to build and train ML models. The company stores the data in an Amazon S3 bucket. The company needs to manage the flow of data from Amazon S3 to SageMaker Studio notebooks.
Which solution will meet this requirement?
正解：A
解説：
To manage the flow of data from Amazon S3 to SageMaker Studio notebooks securely, using a VPC with an S3 endpoint is the best solution.
* Amazon SageMaker and S3 Integration:
* Configuring SageMaker to use a Virtual Private Cloud (VPC) with an S3 endpoint allows the data flow between Amazon S3 and SageMaker Studio notebooks to occur over a private network.
* This setup ensures that traffic between SageMaker and S3 does not traverse the public internet, enhancing security and performance.
* Why Option C is Correct:
* Secure Data Transfer: Ensures secure, private connectivity between SageMaker and S3, reducing exposure to potential security risks.
* Direct Access to S3: Using an S3 endpoint in a VPC allows direct access to data in S3 without leaving the AWS network.
* Why Other Options are Incorrect:
* A. Amazon Inspector: Focuses on identifying security vulnerabilities, not managing data flow.
* B. Amazon Macie: Monitors for sensitive data but does not manage data flow between S3 and SageMaker.
* D. S3 Glacier Deep Archive: Is a storage class for archiving data, not for managing active data flow.
質問 # 121
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