A large company has a variety of Amazon DB clusters. Each of these clusters has various configurations that adhere to various requirements. Depending on the team and use case, these configurations can be organized into broader categories.
A database administrator wants to make the process of storing and modifying these parameters more systematic. The database administrator also wants to ensure that changes to individual categories of configurations are automatically applied to all instances when required.
Which AWS service or feature will help automate and achieve this objective?

• A.AWS Secrets Manager
• B.AWS Systems Manager Parameter Store
• C.DB parameter group
• D.AWS Config

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A company is using Amazon with Aurora Replicas for read-only workload scaling. A Database Specialist needs to split up two read-only applications so each application always connects to a dedicated replica. The Database Specialist wants to implement load balancing and high availability for the read-only applications.
Which solution meets these requirements?

• A.Use reader endpoints for both the read-only workload applications.
• B.Use custom endpoints for the two read-only applications.
• C.Use a reader endpoint for one read-only application and use an instance endpoint for the other read-onlyapplication.
• D.Use a specific instance endpoint for each replica and add the instance endpoint to each read-onlyapplication connection string.

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To meet new data compliance requirements, a company needs to keep critical data durably stored and readily accessible for 7 years. Data that is more than 1 year old is considered archival data and must automatically be moved out of the Amazon Aurora MySQL DB cluster every week. On average, around 10 GB of new data is added to the database every month. A database specialist must choose the most operationally efficient solution to migrate the archival data to Amazon S3.
Which solution meets these requirements?

• A.Use AWS Database Migration Service (AWS DMS) to continually export the archival data from the DB cluster to Amazon S3. Configure an AWS Data Pipeline process to run weekly that executes a custom SQL script to delete the archival data from the DB cluster.
• B.Configure two AWS Lambda functions: one that exports archival data from the DB cluster to Amazon S3 using the mysqldump utility, and another that deletes the archival data from the DB cluster. Schedule both Lambda functions to run weekly using Amazon EventBridge (Amazon CloudWatch Events).
• C.Create a custom script that exports archival data from the DB cluster to Amazon S3 using a SQL view, then deletes the archival data from the DB cluster. Launch an Amazon EC2 instance with a weekly cron job to execute the custom script.
• D.Configure an AWS Lambda function that exports archival data from the DB cluster to Amazon S3 using a SELECT INTO OUTFILE S3 statement, then deletes the archival data from the DB cluster. Schedule the Lambda function to run weekly using Amazon EventBridge (Amazon CloudWatch Events).

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A gaming company wants to deploy a game in multiple Regions. The company plans to save local high scores in Amazon DynamoDB tables in each Region. A Database Specialist needs to design a solution to automate the deployment of the database with identical configurations in additional Regions, as needed. The solution should also automate configuration changes across all Regions.
Which solution would meet these requirements and deploy the DynamoDB tables?

• A.Create an AWS CLI command to deploy the DynamoDB table to all the Regions and save it for future deployments.
• B.Create an AWS CloudFormation template and deploy the template to all the Regions.
• C.Create an AWS CloudFormation template and use a stack set to deploy the template to all the Regions.
• D.Create DynamoDB tables using the AWS Management Console in all the Regions and create a step-bystep guide for future deployments.

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A company needs a data warehouse solution that keeps data in a consistent, highly structured format. The company requires fast responses for end-user queries when looking at data from the current year, and users must have access to the full 15-year dataset, when needed. This solution also needs to handle a fluctuating number incoming queries. Storage costs for the 100 TB of data must be kept low.
Which solution meets these requirements?

• A.Leverage an Amazon Redshift data warehouse solution using a dense storage instance to store the most recent data. Keep historical data on Amazon S3 and access it using the Amazon Redshift Spectrum layer. Provision enough instances to support high demand.
• B.Leverage an Amazon Redshift data warehouse solution using a dense storage instance type while keeping all the data on local Amazon Redshift storage. Provision enough instances to support high demand.
• C.Leverage an Amazon Redshift data warehouse solution using a dense storage instance to store the most recent data. Keep historical data on Amazon S3 and access it using the Amazon Redshift Spectrum layer. Leverage Amazon Redshift elastic resize.
  https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling.html
  "With the Concurrency Scaling feature, you can support virtually unlimited concurrent users and concurrent queries, with consistently fast query performance. When concurrency scaling is enabled, Amazon Redshift automatically adds additional cluster capacity when you need it to process an increase in concurrent read queries. Write operations continue as normal on your main cluster. Users always see the most current data, whether the queries run on the main cluster or on a concurrency scaling cluster. You're charged for concurrency scaling clusters only for the time they're in use. For more information about pricing, see Amazon Redshift pricing. You manage which queries are sent to the concurrency scaling cluster by configuring WLM queues. When you enable concurrency scaling for a queue, eligible queries are sent to the concurrency scaling cluster instead of waiting in line."
• D.Leverage an Amazon Redshift data warehouse solution using a dense storage instance to store the most recent data. Keep historical data on Amazon S3 and access it using the Amazon Redshift Spectrum layer. Enable Amazon Redshift Concurrency Scaling.

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