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NEW QUESTION # 347
A company wants to fine-tune an ML model that is hosted on Amazon Bedrock. The company wants to use its own sensitive data that is stored in private databases in a VPC. The data needs to stay within the company's private network.
Which solution will meet these requirements?
Answer: C
Explanation:
The company wants to fine-tune an ML model on Amazon Bedrock using sensitive data stored in private databases within a VPC, ensuring the data remains within its private network. AWS PrivateLink provides a secure, private connection between a VPC and AWS services like Amazon Bedrock, allowing data to stay within the company's network without traversing the public internet. This meets the requirement for maintaining data privacy during fine-tuning.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"AWS PrivateLink enables you to securely connect your VPC to Amazon Bedrock without exposing data to the public internet. This is particularly useful for fine-tuning models with sensitive data, as it ensures that data remains within your private network." (Source: AWS Bedrock User Guide, Security and Networking) Detailed Option A: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role.While IAM service roles control access to Amazon Bedrock, they do not address the requirement of keeping data within the private network during data transfer. This option is insufficient.
Option B: Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy.IAM resource policies define permissions for Bedrock resources but do not ensure that data stays within the private network. This option is incorrect.
Option C: Use AWS PrivateLink to connect the VPC and Amazon Bedrock.This is the correct answer. AWS PrivateLink creates a secure, private connection between the VPC and Amazon Bedrock, ensuring that sensitive data does not leave the private network during fine-tuning, as required.
Option D: Use AWS Key Management Service (AWS KMS) keys to encrypt the data.While AWS KMS can encrypt data, encryption alone does not guarantee that data remains within the private network during transfer. This option does not fully meet the requirement.
Reference:
AWS Bedrock User Guide: Security and Networking (https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) AWS Documentation: AWS PrivateLink (https://aws.amazon.com/privatelink/) AWS AI Practitioner Learning Path: Module on Security and Networking for AI/ML Services
NEW QUESTION # 348
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt.
Which consideration will inform the company's decision?
Answer: A
Explanation:
The context window determines how much information can fit into a single prompt when using a large language model (LLM) like those on Amazon Bedrock.
* Context Window:
* The context window is the maximum amount of text (measured in tokens) that a language model can process in a single pass.
* For LLM applications, the size of the context window limits how much input data, such as text for sentiment analysis, can be fed into the model at once.
* Why Option B is Correct:
* Determines Prompt Size: The context window size directly informs how much information (e.
g., words or sentences) can fit in one prompt.
* Model Capacity: The larger the context window, the more information the model can consider for generating outputs.
* Why Other Options are Incorrect:
* A. Temperature: Controls randomness in model outputs but does not affect the prompt size.
* C. Batch size: Refers to the number of training samples processed in one iteration, not the amount of information in a prompt.
* D. Model size: Refers to the number of parameters in the model, not the input size for a single prompt.
NEW QUESTION # 349
A company is deploying AI/ML models by using AWS services. The company wants to offer transparency into the models' decision-making processes and provide explanations for the model outputs.
Answer: A
Explanation:
* Amazon SageMaker Model Cards document model details, performance, intended use cases, and risk considerations. They support responsible AI by improving transparency and governance.
* Rekognition is computer vision.
* Comprehend is NLP for entity/sentiment.
* Lex is conversational AI.
# Reference:
AWS Documentation - SageMaker Model Cards
NEW QUESTION # 350
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?
Answer: B
Explanation:
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/)
NEW QUESTION # 351
A company wants to develop ML applications to improve business operations and efficiency.
Select the correct ML paradigm from the following list for each use case. Each ML paradigm should be selected one or more times. (Select FOUR.)
* Supervised learning
* Unsupervised learning
Answer:
Explanation:
NEW QUESTION # 352
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