A company is running its customer feedback application on Amazon Aurora MySQL. The company runs a report every day to extract customer feedback, and a team reads the feedback to determine if the customer comments are positive or negative. It sometimes takes days before the company can contact unhappy customers and take corrective measures. The company wants to use machine learning to automate this workflow.
Which solution meets this requirement with the LEAST amount of effort?

• A.Export the Aurora MySQL database to Amazon S3 by using AWS Database Migration Service (AWS DMS). Use Amazon Comprehend to run sentiment analysis on the exported files.
• B.Export the Aurora MySQL database to Amazon S3 by using AWS Database Migration Service (AWS DMS). Use Amazon SageMaker to run sentiment analysis on the exported files.
• C.Set up Aurora native integration with Amazon Comprehend. Use SQL functions to extract sentiment analysis.
• D.Set up Aurora native integration with Amazon SageMaker. Use SQL functions to extract sentiment analysis.

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A company is developing a new web application. An AWS CloudFormation template was created as a part of the build process.
Recently, a change was made to an AWS::RDS::DBInstance resource in the template. The CharacterSetName property was changed to allow the application to process international text. A change set was generated using the new template, which indicated that the existing DB instance should be replaced during an upgrade.
What should a database specialist do to prevent data loss during the stack upgrade?

• A.Create a snapshot of the DB instance. Modify the template to add the DBSnapshotIdentifier property with the ID of the DB snapshot. Update the stack.
• B.Modify the stack policy using the aws cloudformation update-stack command and the set-stack-policy command, then make the DB resource protected.
• C.Create a snapshot of the DB instance. Update the stack. Restore the database to a new instance.
• D.Deactivate any applications that are using the DB instance. Create a snapshot of the DB instance.
  Modify the template to add the DBSnapshotIdentifier property with the ID of the DB snapshot. Update the stack and reactivate the applications.

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A company is building a new web platform where user requests trigger an AWS Lambda function that performs an insert into an Amazon Aurora MySQL DB cluster. Initial tests with less than 10 users on the new platform yielded successful execution and fast response times. However, upon more extensive tests with the actual target of 3,000 concurrent users, Lambda functions are unable to connect to the DB cluster and receive too many connections errors.
Which of the following will resolve this issue?

• A.Edit the my.cnf file for the DB cluster to increase max_connections
• B.Increase the instance size of the DB cluster
• C.Change the DB cluster to Multi-AZ
• D.Increase the number of Aurora Replicas

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A business is transferring its on-premises database workloads to the Amazon Web Services (AWS) Cloud. A database professional migrating an Oracle database with a huge table to Amazon RDS has picked AWS DMS. The database professional observes that AWS DMS is consuming considerable time migrating the data.
Which activities would increase the pace of data migration? (Select three.)

• 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 and AWS.
• 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.

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A database specialist manages a critical Amazon RDS for MySQL DB instance for a company. The data stored daily could vary from .01% to 10% of the current database size. The database specialist needs to ensure that the DB instance storage grows as needed.
What is the MOST operationally efficient and cost-effective solution?

• A.Configure RDS instance Auto Scaling.
• B.Modify the DB instance allocated storage to meet the forecasted requirements.
• C.Monitor the Amazon CloudWatch FreeStorageSpace metric daily and add storage as required.
• D.Configure RDS Storage Auto Scaling.

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