Pass the Amazon Web Services AWS Certified Professional SAP-C02 Questions and answers with CertsForce

https://docs.aws.amazon.com/elasticloadbalancing/latest/application/load-balancer-access-logs.html

B and E are correct. A multi-node Amazon EKS cluster provides high availability for the Kubernetes control and workload layer, while Amazon Managed Service for Prometheus and Amazon Managed Grafana reduce monitoring overhead compared with self-managed Prometheus and Grafana. Amazon ECR enhanced scanning is also appropriate because it uses Amazon Inspector to scan container images for operating system and programming language package vulnerabilities. For storage, Amazon FSx for NetApp ONTAP is the correct multiprotocol shared file system because it supports access patterns such as NFS and SMB through a fully managed service. EFS plus FSx for Windows splits storage across two services and does not provide one multiprotocol storage platform. A single-node EKS cluster is not highly available.

To enhance the network connectivity solution ' s availability, fault tolerance, and security in a cost-effective manner, adding a dynamic private IP AWS Site-to-Site VPN as a secondary path is a viable option. This VPN serves as a resilient backup for the Direct Connect connection, ensuring continuous data flow even if the primary path fails. Implementing MACsec (Media Access Control Security) on the Direct Connect connection further secures the data in transit by providing encryption, thus addressing the security requirement. This solution strikes a balance between cost and operational efficiency, avoiding the higher expenses associated with provisioning an additional Direct Connect connection.

AWS Documentation on AWS Direct Connect and AWS Site-to-Site VPN provides insights into setting up resilient and secure network connections. Additionally, information on MACsec offers guidance on how to implement encryption for Direct Connect connections, aligning with best practices for secure and highly available network architectures.

The current Redshift configuration uses eight ra3.4xlarge nodes with on-demand pricing and is paused outside an 8-hour daily window. The ingestion workload runs only 3 hours per day and has about 85% CPU utilization during ingestion, which indicates that the existing node size and count are adequate for performance. The main opportunity is cost optimization for a workload that is time-bound and does not require a continuously running provisioned cluster.

Amazon Redshift Serverless is designed for such intermittent or variable workloads. With Redshift Serverless, you configure compute capacity in terms of Redshift Processing Units (RPUs) and pay per use based on the RPU-hours consumed when queries and data ingestion jobs are actually running. When there is no activity, there is no compute charge. Redshift Serverless can also be created from an existing provisioned Redshift snapshot, allowing an easy migration path from a node-based cluster to a serverless endpoint without re-ingesting or manually moving data.

Option C creates a new Redshift Serverless endpoint with 64 RPUs from the most recent snapshot. This takes advantage of snapshots that already exist on the current cluster. The internal applications are then updated to read from the new serverless endpoint, and the existing cluster is deleted to stop incurring node-based charges. For a workload that runs only a few hours per day, moving to serverless compute is usually more cost-effective than maintaining a provisioned cluster, even if that cluster is paused outside the ingestion window, because serverless eliminates the need to manage cluster sizing and pausing and optimizes billing around actual usage.

Option A proposes staying on provisioned RA3 nodes and additionally purchasing 1-year Reserved Nodes. Reserved Nodes apply discount to node hours whether or not the cluster is paused or fully utilized, and they are designed to be cost-effective for steady, predictable, long-running workloads. For an 8-hour-per-day workload, especially with only 3 hours of intensive ingestion, Reserved Nodes are likely to be less cost-optimal than an on-demand, usage-based serverless model. Also, concurrency scaling is primarily for handling bursty query concurrency, not for cost reduction of a regular ingestion workload.

Option B replaces the existing eight ra3.4xlarge nodes with six ra3.16xlarge nodes and enables auto scaling. ra3.16xlarge provides significantly more capacity per node and is generally more expensive. The original workload already runs at acceptable utilization (about 85% during ingestion), so scaling up to larger nodes is not necessary for performance. Adding auto scaling on top of this likely increases costs and complexity without a clear benefit for a predictable, time-bound daily ingestion pattern.

Option D uses Redshift Spectrum and unloads data from the cluster to Amazon S3 for querying. Redshift Spectrum allows querying data stored in S3 using external tables from within a Redshift cluster, and is charged per amount of data scanned. However, the option still depends on maintaining a Redshift cluster and does not fundamentally optimize the compute cost of the ingestion workload. It also changes query patterns and data access architecture for the internal applications and may increase query cost if large volumes of S3 data are scanned by Spectrum.

Therefore, using Redshift Serverless created from the existing snapshot and paying only for compute usage during ingestion and queries, as described in option C, is the best way to optimize cost for this specific workload pattern.

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.

https://aws.amazon.com/kinesis/data-firehose/faqs/

Create an S3 Bucket:

Log in to the AWS Management Console and navigate to Amazon S3.

Create a new S3 bucket that will serve as the destination for the application data.

Deploy AWS Storage Gateway:

Download and deploy the AWS Storage Gateway virtual machine (VM) on your on-premises environment. This VM can be deployed on VMware ESXi, Microsoft Hyper-V, or Linux KVM.

Configure the File Gateway:

Configure the deployed Storage Gateway as a file gateway. This will enable it to present Amazon S3 buckets as SMB file shares to your on-premises applications.

Create a New File Share:

Within the Storage Gateway configuration, create a new file share that is associated with the S3 bucket you created earlier. This file share will use the SMB protocol, allowing your on-premises applications to access the S3 bucket as if it were a local SMB file share.

Copy Data to the File Gateway:

Use your preferred method (such as robocopy, rsync, or similar tools) to copy data from the on-premises storage to the newly created file gateway endpoint. This data will be stored in the S3 bucket, maintaining accessibility through SMB.

Ensure Secure and Efficient Data Transfer:

AWS Storage Gateway ensures that all data in transit is encrypted using TLS, providing secure data transfer to AWS. It also provides local caching for frequently accessed data, improving access performance for on-premises applications.

This approach allows your existing on-premises applications to continue accessing data via SMB while leveraging the scalability and durability of Amazon S3.

References

AWS Storage Gateway Overview【67】.

AWS DataSync and Storage Gateway Hybrid Architecture【66】.

AWS S3 File Gateway Details【68】.

This option allows the company to use DAX to improve the performance and reduce the latency of the DynamoDB queries by caching the results in memory1. By updating the application to use DAX, the company can reduce the load on the DynamoDB tables and avoid throttling errors1. By creating an Auto Scaling group for the EC2 instances, the company can adjust the number of instances based on the demand and ensure high availability2. By creating an ALB, the company can distribute the incoming traffic across multiple EC2 instances and improve fault tolerance3. By updating the Route 53 record to use a simple routing policy that targets the ALB’s DNS alias, the company can route users to the ALB endpoint and leverage its health checks and load balancing features4. By configuring scheduled scaling for the EC2 instances before the content updates, the company can anticipate and handle traffic spikes during peak periods5.

What is Amazon DynamoDB Accelerator (DAX)?

What is Amazon EC2 Auto Scaling?

What is an Application Load Balancer?

Choosing a routing policy

Scheduled scaling for Amazon EC2 Auto Scaling

They all mention using spot instances and EKS based on EC2. A spot instance is not appropriate for a production server and the company is developing new application designed for AWS Fargate, which means we must plan the future cost improvement including AWS Fargate. https://aws.amazon.com/savingsplans/compute-pricing/

https://docs.aws.amazon.com/vpn/latest/clientvpn-admin/scenario-peered.html

According to the AWS documentation1, AWS App2Container (A2C) is a command line tool for migrating and modernizing Java and .NET web applications into container format. AWS A2C analyzes and builds an inventory of applications running in bare metal, virtual machines, Amazon Elastic Compute Cloud (EC2) instances, or in the cloud. You can use AWS A2C to generate container images for your applications and deploy them on Amazon ECS or Amazon EKS.

Option A meets the requirements of the scenario because it allows you to migrate your existing Java application to AWS and minimize the administrative overhead to maintain the servers. You can use AWS A2C to analyze your application dependencies, extract application artifacts, and generate a Dockerfile. You can then store your container images in Amazon ECR, which is a fully managed container registry service. You can use AWS Fargate as the launch type for your Amazon ECS cluster, which is a serverless compute engine that eliminates the need to provision and manage servers for your containers. You can grant the ECS task execution role permission to access the ECR image repository, which allows your tasks to pull images from ECR. You can configure Amazon ECS to use an ALB, whichis a load balancer that distributes traffic across multiple targets in multiple Availability Zones using HTTP or HTTPS protocols. You can use the ALB to interact with your application.

S3 Event Notifications is a feature that allows users to receive notifications when certain events happen in an S3 bucket, such as object creation or deletion1. Users can configure S3 Event Notifications to invoke an AWS Lambda function when a user stores an image in the bucket. Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources2. The Lambda function can resize the image in place and store the original file in the same S3 bucket. This way, the solution can handle significant variance in demand and be reliable at enterprise scale. Thesolution can also rerun processing jobs in the event of failure by using the retry and dead-letter queue features of Lambda2.

S3 Lifecycle is a feature that allows users to manage their objects so that they are stored cost-effectively throughout their lifecycle3. Users can create an S3 Lifecycle policy to move all stored images to S3 Standard-Infrequent Access (S3 Standard-IA) after 6 months. S3 Standard-IA is a storage class designed for data that is accessed less frequently, but requires rapid access when needed4. It offers a lower storage cost than S3 Standard, but charges a retrieval fee. Therefore, moving the images to S3 Standard-IA after 6 months can reduce the storage cost for the solution.

Option A is incorrect because using AWS Step Functions to process the S3 event that occurs when a user stores an image is not necessary or cost-effective. AWS Step Functions is a service that lets users coordinate multiple AWS services into serverless workflows. However, for this use case, a single Lambda function can handle the image resizing task without needing Step Functions.

Option B is incorrect because using Amazon EventBridge to process the S3 event that occurs when a user uploads an image is not necessary or cost-effective. Amazon EventBridge is a serverless event bus service that makes it easy to connect applications with data from a variety of sources. However, for this use case, S3 Event Notifications can directly invoke the Lambda function without needing EventBridge.

Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) to process the S3 event that occurs when a user stores an image is not necessary or cost-effective. Amazon SQS is a fully managed message queuing service that enables users to decouple and scale microservices, distributed systems, and serverless applications. However, for this use case, S3 Event Notifications can directly invoke the Lambda function without needing SQS. Moreover, storing the resized file in an S3 bucket that uses S3 Standard-IA will incur a retrieval fee every time the file is accessed, which may not be cost-effective for frequently accessed files.

By creating an AMI that has Grafana pre-installed and storing the existing dashboards in Amazon Elastic File System (Amazon EFS) it allows for faster and more efficient scaling, and by creating an Auto Scaling group that uses the new AMI and setting the Auto Scaling group ' s minimum, desired, and maximum number of instances to one and creating an Application Load Balancer that serves at least two Availability Zones, it ensures high availability and minimized downtime.

https://aws.amazon.com/blogs/aws/new-attributes-based-access-control-with-aws-single-sign-on/