Amazon Web Services SAP-C02 - AWS Certified Solutions Architect - Professional

A DAX cluster can be deployed with one or two nodes for development or test workloads. One- and two-node clusters are not fault-tolerant, and we don't recommend using fewer than three nodes for production use. If a one- or two-node cluster encounters software or hardware errors, the cluster can become unavailable or lose cached data.A DAX cluster can be deployed with one or two nodes for development or test workloads. One- and two-node clusters are not fault-tolerant, and we don't recommend using fewer than three nodes for production use. If a one- or two-node cluster encounters software or hardware errors, the cluster can become unavailable or lose cached data.

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/DAX.concepts.cluster.html

AWS IoT Core’spre-provisioning hookallows custom logic (like serial number validation)beforea device is registered. This ensures that only installed devices connect. Lambda is ideal for such logic.

AWS IoT Just-in-Time Provisioning

Amazon S3 Intelligent-Tiering is a storage class that automatically moves objects between two access tiers based on changing access patterns. Objects that are accessed frequently are stored in the frequent access tier and objects that are accessed infrequently are stored in the infrequent access tier. This allows for cost optimization without requiring manual intervention. This makes it an ideal solution for the scenario described, as it can automatically move objects that are infrequently accessed after 30 days to a lower-cost storage tier while still maintaining millisecond retrieval availability.

Deploy DataSync Agent:

Install the AWS DataSync agent as a VM in your Hyper-V environment. This agent facilitates the data transfer between your on-premises storage and AWS.

Configure Source and Destination:

Set up the source location to point to your on-premises NFS server where the image batches are stored.

Configure the destination location to be an Amazon S3 bucket with the Glacier Deep Archive storage class. This storage class is cost-effective for long-term storage with retrieval times of up to 12 hours.

Create DataSync Tasks:

Create and configure DataSync tasks to manage the data transfer. Schedule these tasks to run during non-business hours to minimize bandwidth usage during peak times. The tasks will handle the copying of data batches from the NFS server to the S3 bucket.

Set Bandwidth Limits:

In the DataSync configuration, set bandwidth limits to control the amount of data being transferred at any given time. This ensures that your network's performance is not adversely affected during business hours.

Delete On-Premises Data:

After successfully copying the data to S3 Glacier Deep Archive, configure the DataSync task to delete the data from your on-premises NFS server. This helps manage storage capacity on-premises and ensures data is securely archived on AWS.

This approach leverages AWS DataSync for efficient, secure, and automated data transfer, and S3 Glacier Deep Archive for cost-effective long-term storage.

References

AWS DataSync Overview【41】.

AWS Storage Blog on DataSync Migration【40】.

Amazon S3 Transfer Acceleration Documentation【42】.

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.

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://docs.aws.amazon.com/controltower/latest/userguide/controls.html https://docs.aws.amazon.com/controltower/latest/userguide/strongly-recommended-controls.html#ebs-enable-encryption AWS is now transitioning the previous term 'guardrail' new term 'control'.

For migration planning, AWS provides specific tooling to discover on-premises servers, collect detailed performance and configuration data, and analyze application dependencies. The primary service for this purpose is AWS Application Discovery Service, which integrates with AWS Migration Hub.

The AWS Application Discovery Agent is installed on on-premises physical servers and VMs. It collects detailed information such as CPU, memory, and disk utilization; processes and services running on the system; system configuration details; and network connections between systems. This allows the company to understand how servers communicate, what applications are running, and what dependencies exist between components.

This data is sent to AWS Application Discovery Service and surfaced through AWS Migration Hub, where the company can view discovered servers, group them logically into applications, and analyze migration readiness. Migration Hub can use this collected data to provide recommendations for EC2 instance sizing that are based on actual on-premises utilization metrics, helping choose appropriate EC2 instance types and sizes.

Option C directly describes installing the AWS Application Discovery Agent, using AWS Migration Hub to discover and group servers into applications, and using Migration Hub to generate EC2 instance recommendations. This aligns with AWS’s migration assessment tooling and meets all the stated goals: performance measurement, system configuration listing, network connection understanding, dependency analysis, and instance sizing recommendations.

Option A uses AWS Systems Manager Agent and Application Manager. While Systems Manager can manage servers and collect some information, it is not the primary tool for detailed migration discovery, networking dependency mapping, and migration-focused analysis. It also suggests using AWS Pricing Calculator for instance recommendations, which requires manual input and does not automatically derive recommendations from observed utilization and dependencies.

Option B suggests using the Amazon Inspector agent to gather performance and usage information. Amazon Inspector is designed for vulnerability and security assessments, not for detailed migration discovery or dependency mapping. Additionally, AWS Compute Optimizer currently bases its recommendations on usage metrics from AWS resources, not on-premises servers.

Option D uses the CloudWatch agent to collect metrics and then Migration Hub and Compute Optimizer. The CloudWatch agent is not the standard agent for migration discovery and dependency analysis of on-premises workloads. Also, Compute Optimizer focuses on optimization of existing AWS resources rather than creating recommendations based on on-premises utilization.

Therefore, installing the AWS Application Discovery Agent and using AWS Migration Hub (option C) is the correct solution to satisfy all the requirements for migration assessment and EC2 sizing recommendations.

The company does not have a data inventory and needs to identify which S3 buckets contain sensitive data. The appropriate AWS managed service for discovering and classifying sensitive data in S3 is Amazon Macie. Macie is designed to discover, classify, and report on sensitive data such as PII in S3 buckets. Amazon Inspector is primarily focused on vulnerability management for compute and container resources and does not provide S3 sensitive data classification in the way Macie does.

After identifying sensitive data locations, the company needs to ensure sensitive data is encrypted with a key that only administrators can access. SSE-S3 uses S3-managed keys and does not provide fine-grained administrative control of key usage in the same way as SSE-KMS with a customer managed key. Using AWS KMS customer managed keys allows the company to control access through key policies and IAM policies so that only designated administrator principals can use or manage the key.

The requirement also implies existing objects already encrypted with SSE-S3 need to be re-encrypted with SSE-KMS for sensitive objects. Changing default encryption only affects new objects. Existing objects must be rewritten (copied over themselves or copied to a new location) using SSE-KMS with the customer managed key. An orchestrated workflow is a common approach to iterate over identified objects and perform copy operations with the desired encryption settings.

Option C uses Macie for discovery, creates a KMS customer managed key restricted to administrators, sets bucket default encryption to SSE-KMS for future objects, and uses a Step Functions workflow to re-encrypt existing sensitive objects. This meets both the discovery requirement and the encryption/control requirement.

Option A is incorrect because Inspector is not the right service to inventory sensitive data in S3. Although the use of a customer managed KMS key and bucket policy enforcement is directionally correct for controlling encryption on writes, the first step (sensitive data discovery) is wrong.

Option B is incorrect because AWS managed keys cannot have their key policies modified by customers in the way customer managed keys can. Also, Inspector is not the right tool for sensitive data discovery in S3.

Option D is incorrect for the same reasons: it relies on Macie correctly for discovery but then attempts to modify an AWS managed key policy, which is not the correct method for restricting access. To restrict access, the company should use a KMS customer managed key with an appropriate key policy.

Therefore, using Amazon Macie plus an AWS KMS customer managed key and a workflow to re-encrypt existing sensitive objects is the correct solution.

A Lambda function alias allows you to point to a specific version of a function and also can be updated to point to a new version of the function without modifying the client application. This way, the company can test different versions of the function with different environment variables and,once the optimal parameters are found, update the alias to point to the new version, without the need to update the client application.

By using this approach, the company can simplify the process of updating the environment variables, minimize disruption to users, and reduce the operational overhead.

CloudFormation cannot delete an S3 bucket that contains objects. When the developers attempt to delete temporary stacks, the delete operation fails if the associated S3 bucket still has objects. Because the bucket name is derived from the stack name, failed deletions can also prevent re-creation of stacks with the same names until the bucket is manually emptied and deleted. This causes friction for development and continuous integration workflows.

To resolve this, the solution must ensure that, during stack deletion, all objects are removed from the S3 bucket so that CloudFormation can delete the bucket successfully. CloudFormation does not natively support automatic deletion of bucket contents. However, CloudFormation custom resources can be used to perform custom actions during stack lifecycle events.

Option A proposes associating a Lambda function with a CloudFormation custom resource. The custom resource can be invoked during stack deletion to list and delete all objects (and object versions if versioning is enabled) in the specified bucket. Once the bucket is emptied by the Lambda function, the standard CloudFormation deletion process can successfully delete the bucket resource.

In addition to emptying the bucket, the IAM role used by CloudFormation must have permissions to delete objects in the bucket. If CloudFormation uses an execution role that lacks s3:DeleteObject (and potentially s3:ListBucket) permissions, the bucket-cleanup Lambda function or CloudFormation itself would not be able to remove objects. Option D ensures that CloudFormation operations are invoked by a role that has s3:DeleteObject permissions on bucket objects, enabling the custom resource to perform the deletions.

Option B is incorrect because, although DeletionPolicy can be set to Delete, there is no such capability as CAPABILITY_DELETE_NONEMPTY in CloudFormation, and DeletionPolicy alone does not override the S3 service requirement that buckets must be empty before deletion.

Option C’s Retain DeletionPolicy leaves the bucket behind when the stack is deleted. While an external AWS Config rule could clean up stale buckets, it complicates the design and does not directly solve the problem of re-creating stacks with the same bucket name in a predictable, immediate way.

Option E configures a bucket policy to grant s3:DeleteObject permissions, but this is not sufficient alone. CloudFormation still must assume a role with the appropriate permissions. Bucket policy and IAM role permissions must both be considered, but the key action to resolve the deletion failure is to explicitly empty the bucket through a custom resource, as in option A, and ensure the execution role has delete permissions, as in option D.

Therefore, implementing a Lambda-backed custom resource to empty the S3 bucket during stack deletion (option A) and ensuring the CloudFormation execution role has s3:DeleteObject permissions (option D) resolves the deletion errors and allows developers to delete and recreate stacks freely.

This option uses a cross-Region read replica of the RDS DB instance to provide a standby database in the separate Region. A cross-Region read replica is a copy of the primary database that is updated asynchronously using the native replication features of the database engine. It provides enhanced availability, scalability, and performance for read-heavy workloads. It also enables fast recovery from a regional outage by promoting the read replica to a standalone database. To use a cross-Region read replica, the company needs to set up a second ECS cluster and ECS service on Fargate in the separate Region. The company also needs to create an AWS Lambda function to promote the read replica to the primary database and update Route 53 to route traffic to the second ECS cluster. The company can then update the EventBridge rule to add a target that will invoke the Lambda function in case of a failure.