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質問 # 63
A company wants to generate synthetic data responses for multiple prompts from a large volume of dat a. The company wants to use an API method to generate the responses. The company does not need to generate the responses immediately.
正解：D
質問 # 64
A retail company wants to build an ML model to recommend products to customers. The company wants to build the model based on responsible practices. Which practice should the company apply when collecting data to decrease model bias?
正解：A
解説：
The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations." (Source: AWS AI Practitioner Learning Path, Module on Responsible AI) Detailed Explanation:
* Option A: Use data from only customers who match the demography of the company's overall customer base.Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.
* Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.
* Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.
* Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company's customer base and could introduce unrelated biases, failing to address fairness.
References:
AWS AI Practitioner Learning Path: Module on Responsible AI
Amazon SageMaker Developer Guide: Bias and Fairness in ML (https://docs.aws.amazon.com/sagemaker
/latest/dg/clarify-bias.html)
AWS Documentation: Responsible AI Practices (https://aws.amazon.com/machine-learning/responsible-ai/)
質問 # 65
A food service company wants to develop an ML model to help decrease daily food waste and increase sales revenue. The company needs to continuously improve the model's accuracy.
Which solution meets these requirements?
正解：D
解説：
Amazon SageMaker is AWS's fully managed ML service that supports retraining and deploying models with new, recent data for continuous improvement. This directly meets the requirement to iterate and continuously improve model accuracy.
A is correct:
"Amazon SageMaker enables teams to retrain models using the most recent data, ensuring ongoing improvement in model accuracy." (Reference: Amazon SageMaker Overview) B (Amazon Personalize) is for recommendations, not general ML or waste reduction.
C (CloudWatch) is for monitoring, not ML training or deployment.
D (Rekognition) is for image/video analysis.
質問 # 66
What is an example of structured data?
正解：C
解説：
The correct answer is C - A CSV file that consists of measurement data, which is a classic example of structured data. According to AWS documentation, structured data is data that is organized in a predefined schema, typically stored in tabular formats with fixed rows, columns, and data types. CSV files, relational databases, and spreadsheets fall into this category because their structure allows deterministic querying and processing. Measurement data contained in a CSV file is easy to analyze with SQL, Amazon Athena, Amazon Redshift, or SageMaker Data Wrangler. In contrast, options A and D (text comments and transcriptions) are examples of unstructured data, requiring NLP techniques. Option B (video files) represents unstructured multimedia data, requiring computer vision. Structured data is essential for supervised ML models, business analytics, and statistical modeling. AWS emphasizes that structured data provides the simplest path to feature engineering and model training due to its consistent format.
Referenced AWS Documentation:
* AWS Data Analytics Whitepaper - Structured vs. Unstructured Data
* Amazon SageMaker Data Wrangler Documentation
質問 # 67
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?
正解：B
解説：
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 Explanation:
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.
References:
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
質問 # 68
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