Amazon SAA-C03 Practice Test - Questions Answers, Page 70

Requirement Analysis: The application was overwhelmed with unexpected data volume, leading to data loss and the need for a replay mechanism.

Amazon SQS Overview: SQS is a fully managed message queuing service that decouples and scales microservices, distributed systems, and serverless applications.

Data Decoupling: By using an SQS queue, the application can store incoming tracker data reliably and process it asynchronously, preventing data loss.

Implementation:

Create an SQS queue.

Modify the web application to send incoming data to the SQS queue.

Configure the application instances to poll the SQS queue and process the messages.

Conclusion: This solution meets the requirements with minimal operational overhead, ensuring data is not lost and can be processed at the application's own pace.

Reference

Amazon SQS: Amazon SQS Documentation

Requirement Analysis: The application involves batch processing of large data transfers between EC2 instances.

Data Transfer Costs: Data transfer within the same Availability Zone (AZ) is typically free, while cross-AZ transfers incur additional costs.

Implementation:

Launch all EC2 instances within the same Availability Zone.

Ensure the instances are part of the same subnet to facilitate seamless data transfer.

Conclusion: Placing all EC2 instances in the same AZ reduces data transfer costs significantly without affecting the application's functionality.

Reference

AWS Pricing: AWS Data Transfer Pricing

To securely integrate Amazon S3 with an application that uses Amazon Cognito for user authentication, the following two steps are essential:

Detailed Explanation:

Step 1: Create an Amazon Cognito Identity Pool (Option A)

Amazon Cognito Identity Pools allow users to obtain temporary AWS credentials to access AWS resources, such as Amazon S3, after successfully authenticating with the Cognito user pool. The identity pool bridges the gap between user authentication and AWS service access by generating temporary credentials using AWS Identity and Access Management (IAM).

Once a user logs in using the Cognito User Pool, the identity pool provides IAM roles with specific permissions that the application can use to access S3 securely. This ensures that each user has appropriate access controls while accessing the S3 bucket.

This is a secure way to ensure that users only have temporary and least-privilege access to the S3 bucket for their documents.

Step 2: Create an Amazon S3 VPC Endpoint (Option C)

By creating an Amazon S3 VPC endpoint, the company ensures that communication between the application (which is hosted in a private subnet) and the S3 bucket occurs over the AWS private network, without the need to traverse the internet. This enhances security and prevents exposure of data to public networks.

The VPC endpoint allows the application to access the S3 bucket privately and securely within the VPC. It also ensures that traffic stays within the AWS network, reducing attack surface and improving overall security.

Why the Other Options Are Incorrect:

Option B: This is incorrect because Amazon Cognito User Pools are used for user authentication, not for generating S3 access tokens. To provide S3 access, you need to use Amazon Cognito Identity Pools, which offer AWS credentials.

Option D: A NAT gateway is unnecessary in this scenario. Using a VPC endpoint for S3 access provides a more secure and cost-effective solution by keeping traffic within AWS.

Option E: Attaching a policy to restrict access based on IP addresses is not scalable or efficient. It would require managing users' dynamic IP addresses, which is not an effective security measure for this use case.

AWS

Reference:

Amazon Cognito Identity Pools

Amazon VPC Endpoints for S3

AWS IAM Identity Center:

IAM Identity Center provides centralized access management for multiple AWS accounts within an organization and integrates seamlessly with existing identity providers (IdPs) through SAML 2.0 federation.

It allows users to authenticate using their existing IdP credentials and gain access to AWS resources without the need to create and manage separate IAM users in each account.

IAM Identity Center also simplifies provisioning and de-provisioning users, as it can automatically synchronize users and groups from the external IdP to AWS, ensuring secure and managed access.

Integration with Existing IdP:

The solution involves configuring IAM Identity Center to connect to the company's IdP using SAML. This setup allows employees to log in with their existing credentials, reducing the complexity of managing separate AWS credentials.

Once connected, IAM Identity Center handles authentication and authorization, granting users access to the AWS accounts based on their assigned roles and permissions.

Why the Other Options Are Incorrect:

Option A: Creating separate IAM users for each employee is not scalable or efficient. Managing thousands of IAM users across multiple AWS accounts introduces unnecessary complexity and operational overhead.

Option B: Using AWS root users with synchronized passwords is a security risk and goes against AWS best practices. Root accounts should never be used for day-to-day operations.

Option D: AWS Resource Access Manager (RAM) is used for sharing AWS resources between accounts, not for federating access for users across accounts. It doesn't provide a solution for authentication via an external IdP.

AWS

Reference:

AWS IAM Identity Center

SAML 2.0 Integration with AWS IAM Identity Center

By setting up IAM Identity Center and connecting it to the existing IdP, the company can efficiently manage access for thousands of employees across multiple AWS accounts with a high degree of operational efficiency and security. Therefore, Option C is the best solution.

Requirement Analysis: The company needs to scale throughput for uploading large files to S3 and downloading them to EC2 instances.

S3 Multipart Uploads: This method allows for the parallel upload of parts of a file, improving upload efficiency and reliability.

Parallel Fetching: Fetching multiple byte-ranges in parallel from S3 improves download performance.

Implementation:

For uploads, use the S3 multipart upload API to upload files in parallel.

For downloads, use the S3 API to request multiple byte-ranges concurrently.

Conclusion: These solutions effectively scale throughput and improve the performance of both uploads and downloads.

Reference

S3 Multipart Upload: Amazon S3 Multipart Upload

Parallel Fetching: S3 Byte-Range Fetches

Requirement Analysis: The product catalog search is causing high CPU utilization on the MySQL DB instance, slowing down the website.

ElastiCache Overview: Amazon ElastiCache for Redis can be used to cache frequently accessed data, reducing load on the database.

Lazy Loading: This caching strategy loads data into the cache only when it is requested, improving response times for repeated queries.

Implementation:

Set up an ElastiCache for Redis cluster.

Modify the application to check the cache before querying the database.

Use lazy loading to populate the cache on cache misses.

Conclusion: This approach reduces database load and improves website performance during product catalog searches.

Reference

Amazon ElastiCache: ElastiCache Documentation

Caching Strategies: ElastiCache Caching Strategies

Requirement Analysis: The Lambda function in the administrator account needs to process messages from an SNS topic in the production account.

IAM Policy for SNS Topic: Allows the Lambda function to subscribe and be invoked by the SNS topic.

SQS Queue for Buffering: Using an SQS queue provides reliable message delivery and buffering between SNS and Lambda, ensuring all messages are processed.

Implementation:

Create an SQS queue in the administrator account.

Set an IAM policy to allow the Lambda function to subscribe to and be invoked by the SNS topic.

Configure the SNS topic to send messages to the SQS queue.

Set up the SQS queue to trigger the Lambda function.

Conclusion: This solution ensures reliable message delivery and processing with appropriate permissions.

Reference

Amazon SNS: Amazon SNS Documentation

Amazon SQS: Amazon SQS Documentation

AWS Lambda: AWS Lambda Documentation

Understanding the Requirement: The company wants to maximize cost savings for their application over the next three years, with the flexibility to change the instance family and sizes within the next six months based on application popularity and usage.

Analysis of Options:

Compute Savings Plan: This plan offers the most flexibility, allowing the company to change instance families, sizes, and regions. It applies to EC2, AWS Fargate, and AWS Lambda, offering significant cost savings with this flexibility.

EC2 Instance Savings Plan: This plan is less flexible than the Compute Savings Plan, as it only applies to EC2 instances and allows changes within a specific instance family.

Zonal Reserved Instances: These provide a discount on EC2 instances but are tied to a specific availability zone and instance type, offering the least flexibility.

Standard Reserved Instances: These offer discounts on EC2 instances but with more restrictions compared to Savings Plans, particularly when changing instance types and families.

Best Option for Flexibility and Savings:

The Compute Savings Plan is the most cost-effective solution because it allows the company to maintain flexibility while still achieving significant cost savings. This is critical for adapting to changing application demands without being locked into specific instance types or families.

AWS Savings Plans

EC2 Instance Types