Amazon DBS-C01 Exam Dumps PDF
AWS Certified Database - Specialty
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- Last Update on March 25, 2023
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A. Use a client library that supports the TTL functionality for DynamoDB.
B. Include a query filter expression to ignore items with an expired TTL.
C. Set the ConsistentRead parameter to true when querying the table.
D. Create a local secondary index on the TTL attribute.
A. Dump all the tables from the Oracle database into an Amazon S3 bucket usingdatapump (expdp). Run data transformations in AWS Glue. Load the data from the S3bucket to the Aurora DB cluster.
B. Order an AWS Snowball appliance and copy the Oracle backup to the Snowballappliance. Once the Snowball data is delivered to Amazon S3, create a new Aurora DBcluster. Enable the S3 integration to migrate the data directly from Amazon S3 to AmazonRDS.
C. Use the AWS Schema Conversion Tool (AWS SCT) to help rewrite database objects toMySQL during the schema migration. Use AWS DMS to perform the full load and changedata capture (CDC) tasks.
D. Use AWS Server Migration Service (AWS SMS) to import the Oracle virtual machineimage as an Amazon EC2 instance. Use the Oracle Logical Dump utility to migrate theOracle data from Amazon EC2 to an Aurora DB cluster.
A. Create multiple AWS DMS tasks to migrate the large table.
B. Configure the AWS DMS replication instance with Multi-AZ.
C. Increase the capacity of the AWS DMS replication server.
D. Establish an AWS Direct Connect connection between the on-premises data center andAWS.
E. Enable an Amazon RDS Multi-AZ configuration.
F. Enable full large binary object (LOB) mode to migrate all LOB data for all large tables.
A. Enable Amazon DynamoDB Streams on the table. Create an AWS Lambda functiontriggered by the stream. Write the log entries to an Amazon S3 object.
B. Create a backup plan in AWS Backup to back up the DynamoDB table once a day.Create an AWS Lambda function that restores the backup in another table and comparesboth tables for changes. Generate the log entries and write them to an Amazon S3 object.
C. Enable AWS CloudTrail logs on the table. Create an AWS Lambda function that readsthe log files once an hour and filters DynamoDB API actions. Write the filtered log files toAmazon S3.
D. Enable Amazon DynamoDB Streams on the table. Create an AWS Lambda functiontriggered by the stream. Write the log entries to an Amazon Kinesis Data Firehose deliverystream with buffering and Amazon S3 as the destination.
A. Leverage an Amazon Redshift data warehouse solution using a dense storage instancetype while keeping all the data on local Amazon Redshift storage. Provision enoughinstances to support high demand.
B. Leverage an Amazon Redshift data warehouse solution using a dense storage instanceto store the most recent data. Keep historical data on Amazon S3 and access it using theAmazon Redshift Spectrum layer. Provision enough instances to support high demand.
C. Leverage an Amazon Redshift data warehouse solution using a dense storage instanceto store the most recent data. Keep historical data on Amazon S3 and access it using theAmazon Redshift Spectrum layer. Enable Amazon Redshift Concurrency Scaling.
D. Leverage an Amazon Redshift data warehouse solution using a dense storage instanceto store the most recent data. Keep historical data on Amazon S3 and access it using theAmazon Redshift Spectrum layer. Leverage Amazon Redshift elastic resize.