A company is moving its fraud detection application from on premises to the AWS Cloud and is using Amazon Neptune for data storage. The company has set up a 1 Gbps AWS Direct Connect connection to migrate 25 TB of fraud detection data from the on-premises data center to a Neptune DB instance. The company already has an Amazon S3 bucket and an S3 VPC endpoint, and 80% of the company's network bandwidth is available.
How should the company perform this data load?

• A.Use an AWS SDK with a multipart upload to transfer the data from on premises to the S3 bucket. Use the Copy command for Neptune to move the data in bulk from the S3 bucket to the Neptune DB instance.
• B.Use AWS Database Migration Service (AWS DMS) to transfer the data from on premises to the S3 bucket. Use the Loader command for Neptune to move the data in bulk from the S3 bucket to the Neptune DB instance.
• C.Use AWS DataSync to transfer the data from on premises to the S3 bucket. Use the Loader command for Neptune to move the data in bulk from the S3 bucket to the Neptune DB instance.
• D.Use the AWS CLI to transfer the data from on premises to the S3 bucket. Use the Copy command for Neptune to move the data in bulk from the S3 bucket to the Neptune DB instance.

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A large IT hardware manufacturing company wants to deploy a MySQL database solution in the AWS Cloud.
The solution should quickly create copies of the company's production databases for test purposes. The solution must deploy the test databases in minutes, and the test data should match the latest production data as closely as possible. Developers must also be able to make changes in the test database and delete the instances afterward.
Which solution meets these requirements?

• A.Leverage Amazon Aurora MySQL. Use database cloning to create multiple test copies of the production DB clusters.
• B.Leverage Amazon Aurora MySQL. Restore previous production DB instance snapshots into new test copies of Aurora MySQL DB clusters to allow them to make changes.
• C.Leverage Amazon RDS for MySQL. Use database cloning to create multiple developer copies of the production DB instance.
• D.Leverage Amazon RDS for MySQL with write-enabled replicas running on Amazon EC2. Create the test copies using a mysqidump backup from the RDS for MySQL DB instances and importing them into the new EC2 instances.

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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.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.
• B.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).
• C.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).
• D.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.

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A Database Specialist needs to define a database migration strategy to migrate an on-premises Oracle database to an Amazon Aurora MySQL DB cluster. The company requires near-zero downtime for the data migration. The solution must also be cost-effective.
Which approach should the Database Specialist take?

• A.Dump all the tables from the Oracle database into an Amazon S3 bucket using datapump (expdp). Run data transformations in AWS Glue. Load the data from the S3 bucket to the Aurora DB cluster.
• B.Order an AWS Snowball appliance and copy the Oracle backup to the Snowball appliance. Once the Snowball data is delivered to Amazon S3, create a new Aurora DB cluster. Enable the S3 integration to migrate the data directly from Amazon S3 to Amazon RDS.
• C.Use the AWS Schema Conversion Tool (AWS SCT) to help rewrite database objects to MySQL during the schema migration. Use AWS DMS to perform the full load and change data capture (CDC) tasks.
• D.Use AWS Server Migration Service (AWS SMS) to import the Oracle virtual machine image as an Amazon EC2 instance. Use the Oracle Logical Dump utility to migrate the Oracle data from Amazon EC2 to an Aurora DB cluster.

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An ecommerce company is using Amazon DynamoDB as the backend for its order-processing application. The steady increase in the number of orders is resulting in increased DynamoDB costs. Order verification and reporting perform many repeated GetItem functions that pull similar datasets, and this read activity is contributing to the increased costs. The company wants to control these costs without significant development efforts.
How should a Database Specialist address these requirements?

• A.Use Amazon DynamoDB Streams and Amazon Kinesis Data Firehose to push the data into Amazon Redshift
• B.Use AWS DMS to migrate data from DynamoDB to Amazon DocumentDB
• C.Use DynamoDB Accelerator to offload the reads
• D.Use an Amazon ElastiCache for Redis in front of DynamoDB to boost read performance

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