Amazon DBS-C01 Practice Test - Questions Answers, Page 32

The point-in-time restore capability of Amazon RDS for MySQL allows you to create a new DB instance with the same configuration as the original one, but with data restored to a specific time within your backup retention period.You can specify any time within your backup retention period, up to the last five minutes of your DB instance's usage1. This feature is useful for recovering from logical corruption or user errors that affect your database.

However, when you use the point-in-time restore capability, you are creating a new DB instance with a different endpoint. Therefore, you need to change the application connection string to point to the new, restored DB instance.You can also delete or rename the original DB instance if you no longer need it1

Target latency is the amount of time that AWS DMS takes to apply changes from the source database to the target database1. High target latency can indicate performance issues or replication errors in the AWS DMS task.

One possible cause of high target latency is the lack of a primary key or index in the target database. A primary key or index helps AWS DMS identify and apply changes to the corresponding rows in the target database.Without a primary key or index, AWS DMS has to scan the entire table to find the matching rows, which can increase the target latency and consume more CPU and memory resources2.

Another possible cause of high target latency is the resource bottlenecks in the replication instance. The replication instance is the compute resource that runs the AWS DMS task and connects to the source and target endpoints. If the replication instance is under-provisioned or overloaded, it can affect the replication performance and cause high target latency.Some factors that can contribute to resource bottlenecks are insufficient network bandwidth, low disk space, high CPU utilization, or large transaction sizes3.

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected data sets1.Graph databases are designed to store and query data that has complex relationships and interconnections, such as social networks, recommendation engines, fraud detection, and knowledge graphs2.Amazon Neptune supports two popular graph models: Property Graph and Resource Description Framework (RDF), and their respective query languages: Apache TinkerPop Gremlin and SPARQL2.

Amazon Neptune also supports IAM policies to control access to the database resources and operations. You can use IAM database authentication to authenticate users and applications that connect to a Neptune DB cluster. IAM database authentication works with MySQL and PostgreSQL database clients.You can also use IAM roles to manage access to Neptune from other AWS services, such as Amazon EC2, AWS Lambda, and Amazon SageMaker2.

Therefore, Amazon Neptune DB cluster is a suitable solution for the marketing company's requirements, as it can provide a graph database storage solution that is optimized for highly connected data and can limit connections and programmatic access by using IAM policies.

Amazon Aurora PostgreSQL is a fully managed, compatible, and scalable relational database service that supports the PostgreSQL open source database engine1.Amazon Aurora PostgreSQL can reduce the cost of running a database compared to Amazon RDS for SQL Server, which is a commercial database engine that requires licensing fees2.

Babelfish for Aurora PostgreSQL is a new capability for Amazon Aurora PostgreSQL-Compatible Edition that enables Aurora to understand commands from applications written for Microsoft SQL Server3.Babelfish allows Aurora PostgreSQL to support the SQL Server wire-level protocol (TDS) and commonly used T-SQL language and semantics, which reduces the amount of code changes required to migrate applications from SQL Server to Aurora3.Babelfish also provides high availability by replicating data across multiple Availability Zones in a single AWS Region4.

Using AWS Database Migration Service (DMS) to migrate the database to Amazon Aurora PostgreSQL and turning on Babelfish for Aurora PostgreSQL is a suitable solution for reducing the cost of the database with the least effort, as it can preserve the compatibility and availability of the database while minimizing the code changes in the application. The only change required in the application is to update the connection string to use the Babelfish TDS port, which is 1433 by default.

A. Establish an AWS Direct Connect hosted connection between the company's data center and AWS. This will provide a secure and high-bandwidth connection for the Always On data replication and minimize the network latency and data loss.

D. Deploy a new SQL Server Always On availability group DB cluster on Amazon EC2. Configure Always On distributed availability groups between the on-premises DB cluster and the AWS DB cluster. Fail over to the AWS DB cluster when it is time to migrate. This will allow the company to use the same SQL Server version and edition as on-premises, and leverage the distributed availability group feature to span two separate availability groups across different locations. The failover process will be fast and seamless, with minimal downtime and data loss.

F. Configure the third-party backup product to perform backups of the DB cluster on Amazon EC2. This will enable the company to continue using their existing backup solution, which requires system-level access to the databases. Amazon RDS for SQL Server does not support system-level access, so it is not a suitable option for this requirement.

AWS Backup is a fully managed service that simplifies data protection across AWS services, in the cloud, and on premises.You can use AWS Backup to create backup plans that define how and when your backups are created, how long they are stored, and where they are replicated1. You can also use AWS Backup to monitor and audit your backup activity.

Using AWS Backup to create a backup plan and configure cross-Region replication is a cost-effective solution for the company's requirements, as it can automate the nightly backup of the DynamoDB table and store the backups in the us-west-2 Region for one year.You can specify the source and destination Regions, the backup vault, and the retention period for your cross-Region replication rule in your backup plan2.You can also assign your DynamoDB table to your backup plan by using a resource assignment3.

AWS CloudFormation is a service that helps you model and set up your AWS resources so that you can spend less time managing those resources and more time focusing on your applications that run in AWS.You create a template that describes all the AWS resources that you want (like Amazon RDS instances), and CloudFormation takes care of provisioning and configuring those resources for you1.

Using AWS CloudFormation to create RDS resources and share the template with each account is a suitable solution for the company's requirements, as it can:

Automate the deployment of RDS instances with specific compliance parameters, such as security groups, encryption, backup settings, etc.

Reduce the operational overhead and human errors of manually creating RDS instances in each account.

Enable the company to create the databases within one day, as CloudFormation can provision resources in parallel and in a consistent manner.

'When Amazon RDS designates a minor engine version as the preferred minor engine version, each database that meets both of the following conditions is upgraded to the minor engine version automatically' https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_UpgradeDBInstance.Upgrading.html

By default, an application directs its read operations to the primary member in a replica set (i.e. read preference mode 'primary'). But, clients can specify a read preference to send read operations to secondaries. https://www.mongodb.com/docs/manual/core/read-preference/

A read replica instance is an Amazon DocumentDB instance that supports only read operations. An Amazon DocumentDB cluster can have up to 15 replicas in addition to the primary instance.Having multiple replicas enables you to distribute read workloads and increase the read throughput of your cluster1.

The read preference option determines how your MongoDB client or driver routes read requests to instances in your Amazon DocumentDB cluster.The secondary preferred option instructs the client to route read queries to the replicas, unless none are available, in which case the queries are routed to the primary instance2. This option can improve the overall database performance by offloading read requests from the primary instance, which handles all write requests, and balancing them across the replicas.

Using this solution, the company can:

Improve the overall database performance by adding one read replica instance and setting the read preference to secondary preferred.

Avoid creating additional application overhead by using the built-in read preference capabilities of the MongoDB client or driver, without having to update the application code or connection string.

Handle the expected increase in write requests by freeing up resources on the primary instance.