Free Amazon Web Services SAP-C02 Practice Exam with Questions & Answers | Set: 12

https://aws.amazon.com/step-functions/use-cases/

The company should purchase the same Reserved Instances for an additional 3-year term with All Upfront payment. The company should purchase a 3-year Compute Savings Plan with All Upfront payment in the management account to cover any additional compute costs. This solution will provide the company with the most cost savings because Reserved Instances and Savings Plans are both pricing models that offer significant discounts compared to On-Demand pricing. Reserved Instances are commitments to use a specific instance type and size in a single Region for a one- or three-year term. You can choose between three payment options: No Upfront, Partial Upfront, or All Upfront. The more you pay upfront, the greater the discount1. Savings Plans are flexible pricing models that offer low prices on EC2 instances, Fargate, and Lambda usage, in exchange for a commitment to a consistent amount of usage (measured in $/hour) for a one- or three-year term. You can choose between two types of Savings Plans: Compute Savings Plans and EC2 Instance Savings Plans. Compute Savings Plans apply to any EC2 instance regardless of Region, instance family, operating system, or tenancy, including those that are part of EMR, ECS, or EKS clusters, or launched by Fargate or Lambda. EC2 Instance Savings Plans apply to a specific instance family within a Region and provide the most savings2. By purchasing the same Reserved Instances for an additional 3-year term with All Upfront payment, the company can lock in the lowest possible price for its EC2 instances that run continuously for 3 years. By purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account, the company can benefit from additional discounts on any other compute usage across its member accounts.

The other options are not correct because:

Purchasing a 1-year Compute Savings Plan with No Upfront payment in each member account would not provide as much cost savings as purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account. A 1-year term offers lower discounts than a 3-year term, and a No Upfront payment option offers lower discounts than an All Upfront payment option. Also, purchasing a Savings Plan in each member account would not allow the company to share the benefits of unused Savings Plan discounts across its organization.

Purchasing a 3-year EC2 Instance Savings Plan with No Upfront payment in the management account to cover EC2 costs in each AWS Region would not provide as much cost savings as purchasing Reserved Instances for an additional 3-year term with All Upfront payment. An EC2 Instance Savings Plan offers lower discounts than Reserved Instances for the same instance family and Region. Also, a No Upfront payment option offers lower discounts than an All Upfront payment option.

Purchasing a 3-year EC2 Instance Savings Plan with All Upfront payment in each member account would not provide as much flexibility or cost savings as purchasing a 3-year Compute Savings Plan with All Upfront payment in the management account. An EC2 Instance Savings Plan applies only to a specific instance family within a Region and does not cover Fargate or Lambda usage. Also, purchasing aSavings Plan in each member account would not allow the company to share the benefits of unused Savings Plan discounts across its organization.

The correct solution is to use an Amazon S3 File Gateway to store the copy of the SMB file share on AWS. An S3 File Gateway enables on-premises applications to store and access objects in Amazon S3 using the SMB protocol. The S3 File Gateway can also be accessed from AWS using the SMB protocol, which provides the ability to use the data from either the data center or AWS if a disaster occurs. The S3 File Gateway supports tiering of data to different S3 storage classes based on the file type. This allows the company to optimizethe storage costs by using S3 Standard-Infrequent Access (S3 Standard-IA) for the metadata files, which are rarely accessed but must be available within 5 minutes, and S3 Glacier Deep Archive for the image files, which are the lowest-cost storage class and suitable for long-term retention of data that is rarely accessed. This solution is the most cost-effective because it does not require any additional hardware, software, or replication services.

The other solutions are incorrect because they either use more expensive or unnecessary services or components, or they do not meet the requirements. For example:

Solution A is incorrect because it uses AWS Outposts with Amazon S3 storage, which is a very expensive and complex solution for the scenario in the question. AWS Outposts is a service that extends AWS infrastructure, services, APIs, and tools to virtually any data center, co-location space, or on-premises facility. It is designed for customers who need low latency and local data processing. Amazon S3 storage on Outposts provides a subset of S3 features and APIs to store and retrieve data on Outposts. However, this solution does not provide SMB access to the data on Outposts, which requires a Windows EC2 instance on Outposts as a file server. This adds more cost and complexity to the solution, and it does not provide the ability to access the data from AWS if a disaster occurs.

Solution B is incorrect because it uses Amazon FSx File Gateway and Amazon FSx for Windows File Server Multi-AZ file system that uses SSD storage, which are both more expensive and unnecessary services for the scenario in the question. Amazon FSx File Gateway is a service that enables on-premises applications to store and access data in Amazon FSx for Windows File Server using the SMB protocol. Amazon FSx for Windows File Server is a fully managed service that provides native Windows file shares with the compatibility, features, and performance that Windows-based applications rely on. However, this solution does not meet the requirements because it does not provide the ability to use different storage classes for the metadata files and image files, and it does not provide the ability to access the data from AWS if a disaster occurs. Moreover, using a Multi-AZ file system that uses SSD storage is overprovisioned and costly for the scenario in the question, which involves rarely accessed data that must be available within 5 minutes.

Solution D is incorrect because it uses an S3 File Gateway that uses S3 Standard-IA for both the metadata files and image files, which is not the most cost-effective solution for the scenario in the question. S3 Standard-IA is a storage class that offers high durability, availability, and performance for infrequently accessed data. However, it is more expensive than S3 Glacier Deep Archive, which is the lowest-cost storage class and suitable for long-term retention of data that is rarely accessed. Therefore, using S3 Standard-IA for the image files, which are likely to be larger and more numerous than the metadata files, is not optimal for the storage costs.

What is S3 File Gateway?

Using Amazon S3 storage classes with S3 File Gateway

Accessing your file shares from AWS

Comprehensive and Detailed Explanation:

A. Using AWS CloudFormation templates allows for the automation of infrastructure deployment. By parameterizing the templates, resources can be provisioned consistently across multiple Regions, reducing administrative overhead and ensuring infrastructure as code practices.

D. Amazon Route 53 latency-based routing directs user requests to the AWS Region that provides the lowest latency, improving access times for users globally and enhancing the user experience.

E. Enabling DynamoDB Streams and adding a second Region to create a global table ensures that data is replicated across Regions, providing high availability and disaster recovery capabilities.

This combination of steps ensures that the application stack is replicated efficiently across Regions, providing disaster recovery, improved performance, and scalability, all while minimizing manual administrative tasks.

Option C is correct because using Auto Scaling groups for the backend services allows the company to scale up or down the number of EC2 instances based on the demand and traffic. This way, the backend services can handle more requests during peak seasons without compromising performance or availability. Using DynamoDB auto scaling allows the company to adjust the provisioned read and write capacity of the table or index automatically based on the actual traffic patterns. This way, the table or index can handle sudden increases or decreases in workload without throttling or overprovisioning1.

Option A is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, increasing the read and write capacity of DynamoDB manually may not be efficient or cost-effective, as it does not account for the variability of the workload. The company may end up paying for unused capacity or experiencing throttling if the workload exceeds the provisioned capacity1.

Option B is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, configuring DynamoDB to use global tables may not be necessary or beneficial for the company, as global tables are mainly used for replicating data across multiple AWS Regions for fast local access and disaster recovery. Global tables do not automatically scale the provisioned capacity of each replica table; they still require manual or auto scaling settings2.

Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB may introduce additional complexity and latency to the application architecture. Amazon SQS is a message queue service that decouples and coordinates the components of a distributed system. AWS Lambda is a serverless compute service that runs code in response to events. Using these services may require significant development effort to integrate them with the backend services and DynamoDB. Moreover, they may not improve the read performance of DynamoDB, which may also be affected by high traffic3.

Auto Scaling groups

DynamoDB auto scaling

AWS Lambda

DynamoDB global tables

AWS Lambda vs EC2: Comparison of AWS Compute Resources - Simform

Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB

AWS Aurora Global Database vs. DynamoDB Global Tables

Amazon Simple Queue Service (SQS)

https://docs.aws.amazon.com/IAM/latest/UserGuide/confused-deputy.html

This is the most secure way to allow org1 to access resources in org2 because it allows for least privilege security access. The customer should create an IAM role and assign the required permissions to the IAM role. The partner company should then use the IAM role’s Amazon Resource Name (ARN) and include the external ID in the IAM role’s trust policy when requesting access to perform the required tasks. This ensures that the partner company can only access the resources that it needs and only from the specific customer account.

in this solution, two transit VIFs are created - one from the DX-A connection and one from the DX-B connection - into the same Direct Connect gateway for high availability. Both the eu-west-1 and us-east-1 transit gateways are then associated with this Direct Connect gateway. The transit gateways are then peered with each other to support cross-Region routing. This solution meets the requirements of the company by creating a highly available connection between the on-premises data center and the VPCs in both the eu-west-1 and us-east-1 regions, and by enabling direct traffic routing between VPCs in those regions.

Option C is the correct and most efficient solution, aligning with AWS best practices for secure and private connectivity:

Create VPC Endpoints for Systems Manager and Amazon S3:

Systems Manager VPC Endpoints: By creating interface VPC endpoints for Systems Manager (com.amazonaws.region.ssm, com.amazonaws.region.ec2messages, and com.amazonaws.region.ssmmessages), the EC2 instances can communicate with Systems Manager services without requiring internet access. This setup ensures that patching operations can be conducted securely within the AWS network.

Amazon S3 VPC Endpoint: A gateway VPC endpoint for Amazon S3 (com.amazonaws.region.s3) allows EC2 instances to access S3 buckets privately. This is essential for accessing application data stored in S3 without traversing the public internet.

https://aws.amazon.com/blogs/mt/self-service-vpcs-in-aws-control-tower-using-aws-service-catalog/

https://docs.aws.amazon.com/vpc/latest/tgw/tgw-transit-gateways.htmlhttps://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-ec2-transitgatewayattachment.html

To develop a migration plan with accurate inventory and dependency data:

AWS Migration Hub provides a single view for tracking migration tasks and resources across multiple AWS services.

The AWS Application Discovery Agent (installed on servers) collects detailed data about running processes, system performance, and network connections.

Migration Hub Strategy Recommendations leverages this data to automatically identify application patterns, generate recommended AWS target services, and provide a detailed migration plan.This approach ensures accurate data collection, detailed dependency mapping, and tailored recommendations—crucial for a successful and right-sized migration to AWS.

By integrating a Lambda pre-provisioning hook into the AWS IoT Core provisioning template, the serial number can be validated against the CA before the device is allowed to send data.

Using allow-auto-registration ensures that only installed devices can register and send data, meeting the security requirement that sensors cannot send data until physically installed.

This approach follows AWS IoT’s secure onboarding pattern for managing device identity and authorization.

AWS DataSync is an online data transfer service that is designed to help customers get their data to and from AWS quickly, easily, and securely. Using DataSync, you can copy data from your on-premises NFS or SMB shares directly to Amazon S3, Amazon EFS, or Amazon FSx for Windows File Server. DataSync uses a purpose-built, parallel transfer protocol for speeds up to 10x faster than open source tools. DataSync also has built-in verification of data both in flight and at rest, so you can be confident that your data was transferred successfully. DataSync allows you to apply filters to select which files or folders to transfer, based on file name, size, or modification time. You can also schedule your DataSync tasks to run daily, weekly, or monthly, or on demand. DataSync is integrated with AWS Direct Connect, so you can take advantage of your existing private connection to AWS. DataSync is also a fully managed service, so you do not need to provision, configure, or maintain any infrastructure for data transfer.

Option A is incorrect because the Amazon S3 CopyObject API operation does not support filtering or scheduling, and it would require you to write and maintain custom scripts to automate the backup process.

Option B is incorrect because AWS Backup does not support filtering or transferring data from on-premises sources to Amazon S3. AWS Backup is a fully managed backup service that makes it easy to centralize and automate the backup of data across AWS services.

Option D is incorrect because AWS Snowball Edge is a physical device that is used for offline data transfer when network bandwidth is limited or unavailable. It is not suitable for daily backups or incremental transfers. AWS Snowball Edge also does not support filtering or scheduling.

1: Considering four different replication options for data in Amazon S3

2: Protect your file and backup archives using AWS DataSync and Amazon S3 Glacier

3: AWS DataSync FAQs

The best option is to use Amazon SQS and AWS Lambda to create a serverless order processing application. Amazon SQS is a fully managed message queue service that can decouple the order receiving and processing components, making the application more scalable and fault-tolerant. AWS Lambda is a serverless compute service that can automatically scale to handle the incoming messages from the SQS queue and process them according to the business logic. AWS Lambda can also integrate with Amazon DynamoDB to store the processed orders in a fast and flexible NoSQL database. This approach eliminates the need to provision, manage, or scale any servers or containers, and reduces the operational overhead and cost.

Option A is not reliable because using an EC2-hosted database to receive the orders introduces a single point of failure and a scalability bottleneck. EC2 instances also require more management and configuration than serverless services.

Option C is not reliable because using AWS Step Functions to receive the orders adds unnecessary complexity and cost to the application. AWS Step Functions is a service that coordinates multiple AWS services into a serverless workflow, but it is not designed to handle high-volume, sporadic, or unpredictable traffic patterns. AWS Step Functions also charges per state transition, which can be expensive for a large number of orders. Launching an ECS container to process each order also requires more resources and management than invoking a Lambda function.

Option D is not reliable because using Amazon Kinesis Data Streams to receive the orders is not suitable for this use case. Amazon Kinesis Data Streams is a service that enables real-time processing of streaming data at scale, but it is not meant for asynchronous message queuing. Amazon Kinesis Data Streams requires consumers to poll the data from the stream, which can introduce latency and complexity. Amazon Kinesis Data Streams also charges per shard hour, which can be expensive for a sporadic traffic pattern.

Amazon SQS

AWS Lambda

Amazon DynamoDB

AWS Step Functions

Amazon ECS

https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/rds-proxy.html