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Free DBS-C01 Exam Braindumps
AWS Certified Database - Specialty Questions 131–135 (page: 28)

Updated On: 10-Feb-2026
Page 28 of 67
PDF with 359 Questions

A retail company manages a web application that stores data in an Amazon DynamoDB table. The company is undergoing account consolidation efforts. A database engineer needs to migrate the DynamoDB table from the current AWS account to a new AWS account.

Which strategy meets these requirements with the LEAST amount of administrative work?

  1. Use AWS Glue to crawl the data in the DynamoDB table. Create a job using an available blueprint to export the data to Amazon S3. Import the data from the S3 file to a DynamoDB table in the new account.
  2. Create an AWS Lambda function to scan the items of the DynamoDB table in the current account and write to a file in Amazon S3. Create another Lambda function to read the S3 file and restore the items of a DynamoDB table in the new account.
  3. Use AWS Data Pipeline in the current account to export the data from the DynamoDB table to a file in Amazon S3. Use Data Pipeline to import the data from the S3 file to a DynamoDB table in the new account.
  4. Configure Amazon DynamoDB Streams for the DynamoDB table in the current account. Create an AWS Lambda function to read from the stream and write to a file in Amazon S3. Create another Lambda functionto read the S3 file and restore the items to a DynamoDB table in the new account.

Answer(s): C



A company uses the Amazon DynamoDB table contractDB in us-east-1 for its contract system with the following schema:

orderID (primary key)
timestamp (sort key)
contract (map)
createdBy (string)
customerEmail (string)

After a problem in production, the operations team has asked a database specialist to provide an IAM policy to read items from the database to debug the application. In addition, the developer is not allowed to access the value of the customerEmail field to stay compliant.

Which IAM policy should the database specialist use to achieve these requirements?







Answer(s): A



A company has an application that uses an Amazon DynamoDB table to store user data. Every morning, a single-threaded process calls the DynamoDB API Scan operation to scan the entire table and generate a critical start-of-day report for management. A successful marketing campaign recently doubled the number of items in the table, and now the process takes too long to run and the report is not generated in time.

A database specialist needs to improve the performance of the process. The database specialist notes that, when the process is running, 15% of the table’s provisioned read capacity units (RCUs) are being used.

What should the database specialist do?

  1. Enable auto scaling for the DynamoDB table.
  2. Use four threads and parallel DynamoDB API Scan operations.
  3. Double the table’s provisioned RCUs.
  4. Set the Limit and Offset parameters before every call to the API.

Answer(s): B



A company is building a software as a service application. As part of the new user sign-on workflow, a Python script invokes the CreateTable operation using the Amazon DynamoDB API. After the call returns, the script attempts to call PutItem.

Occasionally, the PutItem request fails with a ResourceNotFoundException error, which causes the workflow to fail. The development team has confirmed that the same table name is used in the two API calls.

How should a database specialist fix this issue?

  1. Add an allow statement for the dynamodb:PutItem action in a policy attached to the role used by the application creating the table.
  2. Set the StreamEnabled property of the StreamSpecification parameter to true, then call PutItem.
  3. Change the application to call DescribeTable periodically until the TableStatus is ACTIVE, then call PutItem.
  4. Add a ConditionExpression parameter in the PutItem request.

Answer(s): C


Reference:

https://docs.aws.amazon.com/amazondynamodb/latest/APIReference/API_PutItem.html



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?

  1. 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.
  2. 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).
  3. 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).
  4. 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.

Answer(s): B



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