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| Section | Weight | Objectives |
|---|---|---|
| AI Application Development | 30% | - Understand foundational concepts for building AI applications
|
| AWS AI Services Overview | 26% | - Describe AWS AI services for specific use cases
|
| Responsible AI | 20% | - Explain the principles of responsible AI
|
| AI/ML Fundamentals | 24% | - Explain the fundamental concepts of AI and ML
|
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問題 #102
A company stores customer personally identifiable information (PII) data. The company must store the PII data within the company's AWS Region.
Which aspect of governance does this describe?
答案：A
解題說明：
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Data residency refers to requirements that dictate where data is physically stored and processed, often to meet regulatory, legal, or organizational policies.
AWS governance guidance emphasizes that:
* PII data may be legally required to remain within a specific geographic region
* Customers can control data location by selecting AWS Regions
* Data residency is a core component of data governance and compliance
Why the other options are incorrect:
* Data mining (A) refers to extracting insights from data.
* Pre-training bias (C) relates to model training data, not storage location.
* Geolocation routing (D) concerns request routing, not data governance.
AWS AI document references:
* AWS Data Governance and Residency
* Responsible AI and Data Management on AWS
* Security and Compliance in AWS Regions
問題 #103
A student at a university is copying content from generative AI to write essays.
Which challenge of responsible generative AI does this scenario represent?
答案：A
解題說明：
The scenario where a student copies content from generative AI to write essays represents the challenge of plagiarism in responsible AI use.
* Plagiarism:
* Occurs when someone uses content generated by AI (or any source) without proper attribution, claiming it as their own.
* This is a key challenge with generative AI models, which can produce human-like text that might be misused for academic or other purposes.
* Why Option C is Correct:
* Represents Unauthorized Use: Copying content directly from AI without attribution is a clear case of plagiarism.
* Ethical Concern: Highlights the ethical considerations around using AI-generated content responsibly.
* Why Other Options are Incorrect:
* A. Toxicity: Refers to harmful or offensive content generation, not content copying.
* B. Hallucinations: When AI generates incorrect or nonsensical information, not plagiarism.
* D. Privacy: Involves the misuse or exposure of personal information, not copying content.
問題 #104
A company wants to use Amazon Bedrock. The company needs to review which security aspects the company is responsible for when using Amazon Bedrock.
答案：D
解題說明：
Comprehensive and Detailed
With Amazon Bedrock, AWS handles infrastructure security and patching (shared responsibility model).
Customers are responsible for securing their data (encryption, IAM, policies) both in transit and at rest.
Provisioning infrastructure (D) and platform patching (A, B) are AWS responsibilities.
Reference:
AWS Shared Responsibility Model
問題 #105
A company is building a job recommendation system based on job posting data and job seeker user profiles.
The system shows bias in job recommendations based on gender for user profiles that are otherwise equivalent.
Which principle should the company follow to address this issue, according to AWS best practices for responsible AI?
答案：A
解題說明：
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Fairness is a core principle of AWS Responsible AI. It requires that AI systems:
* Do not produce biased or discriminatory outcomes
* Treat individuals and groups equitably
* Are evaluated and mitigated for bias across protected attributes
AWS Responsible AI guidance specifically highlights fairness as the principle used to identify, measure, and mitigate bias in AI systems such as recommendation engines.
Why the other options are incorrect:
* Governance (A) focuses on oversight and policies.
* Explainability (B) explains decisions but does not directly mitigate bias.
* Controllability (C) focuses on human oversight and intervention.
AWS AI document references:
* AWS Responsible AI Principles
* Bias and Fairness in ML Systems
* Responsible AI Best Practices on AWS
問題 #106
A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution's decisions to be explainable.
Which factor relates to the explainability of the AI solution's decisions?
答案：B
解題說明：
The financial institution needs an AI solution for loan approval decisions to be explainable for security and audit purposes. Explainability refers to the ability to understand and interpret how a model makes decisions. Model complexity directly impacts explainability: simpler models (e.g., logistic regression) are more interpretable, while complex models (e.g., deep neural networks) are harder to explain, often behaving like "black boxes." Exact Extract from AWS AI Documents:
From the Amazon SageMaker Developer Guide:
"Model complexity affects the explainability of AI solutions. Simpler models, such as linear regression, are inherently more interpretable, while complex models, such as deep neural networks, may require additional tools like SageMaker Clarify to provide insights into their decision-making processes." (Source: Amazon SageMaker Developer Guide, Explainability with SageMaker Clarify) Detailed Option A: Model complexityThis is the correct answer. The complexity of the model directly influences how easily its decisions can be explained, a critical factor for audit and security purposes in loan approvals.
Option B: Training timeTraining time refers to how long it takes to train the model, which does not directly impact the explainability of its decisions.
Option C: Number of hyperparametersWhile hyperparameters affect model performance, they do not directly relate to explainability. A model with many hyperparameters might still be explainable if it is a simple model.
Option D: Deployment timeDeployment time refers to the time taken to deploy the model to production, which is unrelated to the explainability of its decisions.
Reference:
Amazon SageMaker Developer Guide: Explainability with SageMaker Clarify (https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-explainability.html) AWS AI Practitioner Learning Path: Module on Responsible AI and Explainability AWS Documentation: Explainable AI (https://aws.amazon.com/machine-learning/responsible-ai/)
問題 #107
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