Amazon SAA-C03 Practice Test - Questions Answers, Page 68

S3 Lifecycle is a feature that allows you to automate the management of your S3 objects based on predefined rules. You can use S3 Lifecycle to delete expired object versions and retain the two most recent versions by creating a lifecycle configuration rule that applies to all objects in the bucket and specifies the expiration action for noncurrent versions. This way, you can reduce the storage costs of your S3 bucket without requiring any application changes or manual intervention. S3 Lifecycle runs once a day and marks the eligible object versions for deletion. You are no longer charged for the objects that are marked for deletion. S3 Lifecycle is the most cost-effective and simple solution among the options.

B) Use an AWS Lambda function to check for older versions and delete all but the two most recent versions. This option is not optimal because it requires you to write, test, and maintain a custom Lambda function that scans the S3 bucket for older versions and deletes them. This can incur additional costs for Lambda invocations and increase the operational complexity and overhead. Moreover, you need to ensure that your Lambda function has the appropriate permissions and error handling mechanisms to perform the deletion operation.

C) Use S3 Batch Operations to delete noncurrent object versions and retain only the two most recent versions. This option is not ideal because S3 Batch Operations is designed for performing large-scale operations on S3 objects, such as copying, tagging, restoring, or invoking a Lambda function. To use S3 Batch Operations to delete noncurrent object versions, you need to provide a manifest file that lists the object versions that you want to delete. This can be challenging and time-consuming to generate and update. Moreover, S3 Batch Operations charges you for each operation that you perform, which can increase your costs.

D) Deactivate versioning on the S3 bucket and retain the two most recent versions. This option is not feasible because deactivating versioning on an S3 bucket does not delete the existing object versions. Instead, it prevents new versions from being created. Therefore, you still need to delete the older versions manually or use another method to do so. Additionally, deactivating versioning can compromise the data protection and recovery capabilities of your S3 bucket.

1Considering four different replication options for data in Amazon S3 | AWS Storage Blog

2Using AWS Lambda with Amazon S3 batch operations - AWS Lambda

3Empty an Amazon S3 bucket with a lifecycle configuration rule

4Amazon S3 - Lifecycle Management - GeeksforGeeks

The AWS Fargate launch type is a serverless way to run containers on Amazon ECS, without having to manage any underlying infrastructure. You only pay for the resources required to run your containers, and AWS handles the provisioning, scaling, and security of the cluster. Amazon EFS is a fully managed, elastic, and scalable file system that can be mounted to multiple containers, and provides high availability and durability. By using AWS Fargate and Amazon EFS, you can run your Docker container image with 50 GB of storage available for temporary files, with the least operational overhead. This solution meets the requirements of the question.

AWS Fargate

Amazon Elastic File System

Using Amazon EFS file systems with Amazon ECS

This solution meets the following requirements:

It is cost-effective, as it only uses serverless components that are charged based on usage and do not require any upfront provisioning or maintenance.

It is scalable, as it can handle any number of recipe records that are uploaded to the S3 bucket without any performance degradation or manual intervention.

It is easy to implement, as it does not require any machine learning knowledge or complex data processing logic. Amazon Comprehend is a natural language processing service that can automatically extract entities such as ingredients from text files. The Lambda function can simply invoke the Comprehend API and store the results in the DynamoDB table.

It is reliable, as it can handle non-food records and errors gracefully. Amazon Comprehend can detect the language and domain of the text files and return an appropriate response. The Lambda function can also implement error handling and logging mechanisms to ensure the data quality and integrity.

Using AWS Lambda with Amazon S3 - AWS Lambda

What Is Amazon Comprehend? - Amazon Comprehend

Working with Tables - Amazon DynamoDB

This option is the most cost-effective and scalable way to allow the Lambda function in the primary account to access the EFS file system in the secondary account. VPC peering enables private connectivity between two VPCs without requiring gateways, VPN connections, or dedicated network connections. The Lambda function can use the VPC peering connection to mount the EFS file system as a local file system and access the files as needed. The solution does not incur additional data transfer or storage costs, and it leverages the existing EFS file system without duplicating or moving the data.

Option A is not cost-effective because it requires creating a new EFS file system and using AWS DataSync to copy the data from the original EFS file system. This would incur additional storage and data transfer costs, and it would not provide real-time access to the files.

Option C is not scalable because it requires creating a second Lambda function in the secondary account and configuring cross-account permissions to invoke it from the primary account. This would add complexity and latency to the solution, and it would increase the Lambda invocation costs.

Option D is not feasible because Lambda layers are not designed to store large amounts of data or provide file system access. Lambda layers are used to share common code or libraries across multiple Lambda functions. Moving the contents of the EFS file system to a Lambda layer would exceed the size limit of 250 MB for a layer, and it would not allow the Lambda function to read or write files to the layer.Reference:

What Is VPC Peering?

Using Amazon EFS file systems with AWS Lambda

What Are Lambda Layers?

This option is the best solution because it allows the company to decouple the analytics software from the user requests and scale the EC2 instances dynamically based on the demand. By using Amazon SQS, the company can create a queue that stores the user requests and acts as a buffer between the users and the analytics software. This way, the software can process the requests at its own pace without losing any data or overloading the EC2 instances. By using EC2 Auto Scaling, the company can create an Auto Scaling group that launches or terminates EC2 instances automatically based on the size of the queue. This way, the company can ensure that there are enough instances to handle the load and optimize the cost and performance of the system. By updating the software to read from the queue, the company can enable the analytics software to consume the requests from the queue and process the data from Amazon S3.

A) Create a copy of the instance Place all instances behind an Application Load Balancer. This option is not optimal because it does not address the root cause of the problem, which is the high CPU utilization of the EC2 instances. An Application Load Balancer can distribute the incoming traffic across multiple instances, but it cannot scale the instances based on the load or reduce the processing time of the analytics software. Moreover, this option can incur additional costs for the load balancer and the extra instances.

B) Create an S3 VPC endpoint for Amazon S3 Update the software to reference the endpoint. This option is not effective because it does not solve the issue of the high CPU utilization of the EC2 instances. An S3 VPC endpoint can enable the EC2 instances to access Amazon S3 without going through the internet, which can improve the network performance and security. However, it cannot reduce the processing time of the analytics software or scale the instances based on the load.

C) Stop the EC2 instances. Modify the instance type to one with a more powerful CPU and more memory. Restart the instances. This option is not scalable because it does not account for the variability of the user load. Changing the instance type to a more powerful one can improve the performance of the analytics software, but it cannot adjust the number of instances based on the demand. Moreover, this option can increase the cost of the system and cause downtime during the instance modification.

1Using Amazon SQS queues with Amazon EC2 Auto Scaling - Amazon EC2 Auto Scaling

2Tutorial: Set up a scaled and load-balanced application - Amazon EC2 Auto Scaling

3Amazon EC2 Auto Scaling FAQs

Amazon Kinesis Data Streams is a scalable and reliable service that can ingest, buffer, and process streaming data in real-time. It can handle large traffic spikes and preserve the order of the incoming data records. AWS Lambda is a serverless compute service that can process the data streams from Kinesis Data Streams without requiring any infrastructure management. It can also scale automatically to match the throughput of the data stream. Amazon DynamoDB is a fully managed, highly available, and fast NoSQL database that can store the processed updates from Lambda. It can also handle high write throughput and provide consistent performance. By using these services, the solutions architect can design a solution that meets the requirements of the company with the least operational overhead.

A: How do you protect your data in transit?

Best Practices:

Implement secure key and certificate management: Store encryption keys and certificates securely and rotate them at appropriate time intervals while applying strict access control; for example, by using a certificate management service, such as AWS Certificate Manager (ACM).

Enforce encryption in transit: Enforce your defined encryption requirements based on appropriate standards and recommendations to help you meet your organizational, legal, and compliance requirements.

Automate detection of unintended data access: Use tools such as GuardDuty to automatically detect attempts to move data outside of defined boundaries based on data classification level, for example, to detect a trojan that is copying data to an unknown or untrusted network using the DNS protocol.

Authenticate network communications: Verify the identity of communications by using protocols that support authentication, such as Transport Layer Security (TLS) or IPsec.

https://wa.aws.amazon.com/wat.question.SEC_9.en.html

This option is the most cost-effective way to reduce the S3 storage costs in this situation. Incomplete multipart uploads are parts of objects that are not completed or aborted by the application. They consume storage space and incur charges until they are deleted. By enabling an S3 Lifecycle policy that deletes incomplete multipart uploads, you can automatically remove them after a specified period of time (such as one day) and free up the storage space. This will reduce the S3 storage costs and also improve the performance of the application by avoiding unnecessary retries or errors.

Option A is not correct because switching from multipart uploads to Amazon S3 Transfer Acceleration will not reduce the S3 storage costs. Amazon S3 Transfer Acceleration is a feature that enables faster data transfers to and from S3 by using the AWS edge network. It is useful for improving the upload speed of large objects over long distances, but it does not affect the storage space or charges. In fact, it may increase the costs by adding a data transfer fee for using the feature.

Option C is not correct because configuring S3 inventory to prevent objects from being archived too quickly will not reduce the S3 storage costs. Amazon S3 Inventory is a feature that provides a report of the objects and their metadata in an S3 bucket. It is useful for managing and auditing the S3 objects, but it does not affect the storage space or charges. In fact, it may increase the costs by generating additional S3 objects for the inventory reports.

Option D is not correct because configuring Amazon CloudFront to reduce the number of objects stored in Amazon S3 will not reduce the S3 storage costs. Amazon CloudFront is a content delivery network (CDN) that distributes the S3 objects to edge locations for faster and lower latency access. It is useful for improving the download speed and availability of the S3 objects, but it does not affect the storage space or charges. In fact, it may increase the costs by adding a data transfer fee for using the service.Reference:

Managing your storage lifecycle

Using multipart upload

Amazon S3 Transfer Acceleration

Amazon S3 Inventory

What Is Amazon CloudFront?

This option is the only solution that meets the requirements because it allows the company to encrypt the data with its own encryption keys and tools outside the AWS Cloud. By encrypting the data at the company's data center before storing the data in the S3 bucket, the company can ensure that the data is encrypted in transit and at rest, and that the company has full control over the encryption keys and processes. This option also avoids the need to use any AWS encryption services or features, which may not be compatible with the company's security policies or compliance standards.

A) Encrypt the data in the S3 bucket with server-side encryption (SSE) that uses an AWS Key Management Service (AWS KMS) customer managed key. This option does not meet the requirements because it does not allow the company to manage the encryption keys outside the AWS Cloud. Although the company can create and use its own customer managed key in AWS KMS, the key is still stored and managed by AWS KMS, which is a service within the AWS Cloud. Moreover, the company still needs to use the AWS encryption features and APIs to encrypt and decrypt the data in the S3 bucket, which may not be compatible with the company's security policies or compliance standards.

B) Encrypt the data in the S3 bucket with server-side encryption (SSE) that uses an AWS Key Management Service (AWS KMS) AWS managed key. This option does not meet the requirements because it does not allow the company to manage the encryption keys outside the AWS Cloud. In this option, the company uses the default AWS managed key in AWS KMS, which is created and managed by AWS on behalf of the company. The company has no control over the key rotation, deletion, or recovery policies. Moreover, the company still needs to use the AWS encryption features and APIs to encrypt and decrypt the data in the S3 bucket, which may not be compatible with the company's security policies or compliance standards.

C) Encrypt the data in the S3 bucket with the default server-side encryption (SSE). This option does not meet the requirements because it does not allow the company to manage the encryption keys outside the AWS Cloud. In this option, the company uses the default server-side encryption with Amazon S3 managed keys (SSE-S3), which is applied to every bucket in Amazon S3. The company has no visibility or control over the encryption keys, which are managed by Amazon S3. Moreover, the company still needs to use the AWS encryption features and APIs to encrypt and decrypt the data in the S3 bucket, which may not be compatible with the company's security policies or compliance standards.

1Protecting data with encryption - Amazon Simple Storage Service

2Protecting data with server-side encryption - Amazon Simple Storage Service

3Protecting data by using client-side encryption - Amazon Simple Storage Service

4AWS Key Management Service Concepts - AWS Key Management Service