AWS Certified Database DBS-C01 Release Date

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Question 64

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?

Options:

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.

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).

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).

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.

Question 65

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?

Options:

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.

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.

Question 66

An information management services company is storing JSON documents on premises. The company is using a MongoDB 3.6 database but wants to migrate to

AWS. The solution must be compatible, scalable, and fully managed. The solution also must result in as little downtime as possible during the migration.

Which solution meets these requirements?

Options:

Create an AWS Database Migration Service (AWS DMS) replication instance, a source endpoint for MongoDB, and a target endpoint of Amazon DocumentDB (with MongoDB compatibility).

Create an AWS Database Migration Service (AWS DMS) replication instance, a source endpoint for MongoDB, and a target endpoint of a MongoDB image that is hosted on Amazon EC2

Use the mongodump and mongorestore tools to migrate the data from the source MongoDB deployment to Amazon DocumentDB (with MongoDB compatibility).

Use the mongodump and mongorestore tools to migrate the data from the source MongoDB deployment to a MongoDB image that is hosted on Amazon EC2.

Question 67

A company developed an AWS CloudFormation template used to create all new Amazon DynamoDB tables in its AWS account. The template configures provisioned throughput capacity using hard-coded values. The company wants to change the template so that the tables it creates in the future have independently configurable read and write capacity units assigned.

Which solution will enable this change?

Options:

Add values for the rcuCount and wcuCount parameters to the Mappings section of the template. Configure DynamoDB to provision throughput capacity using the stack’s mappings.

Add values for two Number parameters, rcuCount and wcuCount, to the template. Replace the hard-coded values with calls to the Ref intrinsic function, referencing the new parameters.

Add values for the rcuCount and wcuCount parameters as outputs of the template. Configure DynamoDB to provision throughput capacity using the stack outputs.

Add values for the rcuCount and wcuCount parameters to the Mappings section of the template. Replace the hard-coded values with calls to the Ref intrinsic function, referencing the new parameters.