Amazon DBS-C01 Online Practice Questions and Exam Preparation

Amazon DBS-C01 Online Questions & Answers

• Question 241:

  A company uses an Amazon RDS for PostgreSQL DB instance for its customer relationship management (CRM) system. New compliance requirements specify that the database must be encrypted at rest.

  Which action will meet these requirements?

  A. Create an encrypted copy of manual snapshot of the DB instance. Restore a new DB instance from the encrypted snapshot.
  B. Modify the DB instance and enable encryption.
  C. Restore a DB instance from the most recent automated snapshot and enable encryption.
  D. Create an encrypted read replica of the DB instance. Promote the read replica to a standalone instance.
  A. Create an encrypted copy of manual snapshot of the DB instance. Restore a new DB instance from the encrypted snapshot.

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  Explanation/Reference:

  https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/encrypt-an-existing- amazon-rds-for-postgresql-db-instance.html You can enable encryption for an Amazon RDS DB instance when you create it, but not after it's created. However, you can add encryption to an unencrypted DB instance by creating a snapshot of your DB instance, and then creating an encrypted copy of that snapshot. You can then restore a DB instance from the encrypted snapshot to get an encrypted copy of your original DB instance. The pattern uses AWS Database Migration Service (AWS DMS) to migrate data and AWS Key Management Service (AWS KMS) for encryption.

• Question 242:

  A company has an ecommerce website that runs on AWS. The website uses an Amazon RDS for MySQL database. A database specialist wants to enforce the use of temporary credentials to access the database.

  Which solution will meet this requirement?

  A. Use MySQL native database authentication.
  B. Use AWS Secrets Manager to rotate the credentials.
  C. Use AWS Identity and Access Management (IAM) database authentication.
  D. Use AWS Systems Manager Parameter Store for authentication.
  C. Use AWS Identity and Access Management (IAM) database authentication.

• Question 243:

  A company is closing one of its remote data centers. This site runs a 100 TB on-premises data warehouse solution. The company plans to use the AWS Schema Conversion Tool (AWS SCT) and AWS DMS for the migration to AWS. The site network bandwidth is 500 Mbps. A Database Specialist wants to migrate the on- premises data using Amazon S3 as the data lake and Amazon Redshift as the data warehouse. This move must take place during a 2-week period when source systems are shut down for maintenance. The data should stay encrypted at rest and in transit.

  Which approach has the least risk and the highest likelihood of a successful data transfer?

  A. Set up a VPN tunnel for encrypting data over the network from the data center to AWS. Leverage AWS SCT and apply the converted schema to Amazon Redshift. Once complete, start an AWS DMS task to move the data from the source to Amazon S3. Use AWS Glue to load the data from Amazon S3 to Amazon Redshift.
  B. Leverage AWS SCT and apply the converted schema to Amazon Redshift. Start an AWS DMS task with two AWS Snowball Edge devices to copy data from on-premises to Amazon S3 with AWS KMS encryption. Use AWS DMS to finish copying data to Amazon Redshift.
  C. Leverage AWS SCT and apply the converted schema to Amazon Redshift. Once complete, use a fleet of 10 TB dedicated encrypted drives using the AWS Import/Export feature to copy data from on-premises to Amazon S3 with AWS KMS encryption. Use AWS Glue to load the data to Amazon redshift.
  D. Set up a VPN tunnel for encrypting data over the network from the data center to AWS. Leverage a native database export feature to export the data and compress the files. Use the aws S3 cp multi-port upload command to upload these files to Amazon S3 with AWS KMS encryption. Once complete, load the data to Amazon Redshift using AWS Glue.
  B. Leverage AWS SCT and apply the converted schema to Amazon Redshift. Start an AWS DMS task with two AWS Snowball Edge devices to copy data from on-premises to Amazon S3 with AWS KMS encryption. Use AWS DMS to finish copying data to Amazon Redshift.

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  Explanation/Reference:

  https://aws.amazon.com/blogs/database/new-aws-dms-and-aws-snowball- integration-enables-mass-database-migrations-and-migrations-of-large-databases/

• Question 244:

  An online bookstore uses Amazon Aurora MySQL as its backend database. After the online bookstore added a popular book to the online catalog, customers began reporting intermittent timeouts on the checkout page. A database specialist determined that increased load was causing locking contention on the database. The database specialist wants to automatically detect and diagnose database performance issues and to resolve bottlenecks faster.

  Which solution will meet these requirements?

  A. Turn on Performance Insights for the Aurora MySQL database. Configure and turn on Amazon DevOps Guru for RDS.
  B. Create a CPU usage alarm. Select the CPU utilization metric for the DB instance. Create an Amazon Simple Notification Service (Amazon SNS) topic to notify the database specialist when CPU utilization is over 75%.
  C. Use the Amazon RDS query editor to get the process ID of the query that is causing the database to lock. Run a command to end the process.
  D. Use the SELECT INTO OUTFILE S3 statement to query data from the database. Save the data directly to an Amazon S3 bucket. Use Amazon Athena to analyze the files for long-running queries.
  A. Turn on Performance Insights for the Aurora MySQL database. Configure and turn on Amazon DevOps Guru for RDS.

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  Explanation/Reference:

  Correct Answer: A from Amazon documents: Performance Insights is a feature of Amazon Aurora MySQL that helps you quickly assess the load on your database and determine when and where to take action. Performance Insights displays a dashboard that shows the database load in terms of average active sessions (AAS), which is the average number of sessions that are actively running SQL statements at any given time. Performance Insights also shows the top SQL statements, waits, hosts, and users that are contributing to the database load. Amazon DevOps Guru is a fully managed service that helps you improve the operational performance and availability of your applications by detecting operational issues and recommending specific actions for remediation. Amazon DevOps Guru applies machine learning to automatically analyze data such as application metrics, logs, events, and traces for behaviors that deviate from normal operating patterns. Amazon DevOps Guru supports Amazon RDS as a resource type and can monitor the performance and availability of your RDS databases. By turning on Performance Insights for the Aurora MySQL database and configuring and turning on Amazon DevOps Guru for RDS, the database specialist can automatically detect and diagnose database performance issues and resolve bottlenecks faster. This solution will allow the database specialist to monitor the database load and identify the root causes of performance problems using Performance Insights, and receive actionable insights and recommendations from Amazon DevOps Guru to improve the operational performance and availability of the database. Therefore, option A is the correct solution to meet the requirements. Option B is not sufficient because creating a CPU usage alarm will only notify the database specialist when the CPU utilization is high, but it will not help diagnose or resolve the database performance issues. Option C is not efficient because using the Amazon RDS query editor to get the process ID of the query that is causing the database to lock and running a command to end the process will require manual intervention and may cause data loss or inconsistency. Option D is not efficient because using the SELECT INTO OUTFILE S3 statement to query data from the database and saving the data directly to an Amazon S3 bucket will incur additional time and cost, and using Amazon Athena to analyze the files for long-running queries will not help prevent or resolve locking contention on the database.

• Question 245:

  A database professional is developing an application that will respond to single-instance requests. The program will query large amounts of client data and offer end users with results.

  These reports may include a variety of fields. The database specialist want to enable users to query the database using any of the fields offered.

  During peak periods, the database's traffic volume will be significant yet changeable. However, the database will see little activity over the rest of the day.

  Which approach will be the most cost-effective in meeting these requirements?

  A. Amazon DynamoDB with provisioned capacity mode and auto scaling
  B. Amazon DynamoDB with on-demand capacity mode
  C. Amazon Aurora with auto scaling enabled
  D. Amazon Aurora in a serverless mode
  D. Amazon Aurora in a serverless mode

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  Explanation/Reference:

  https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Limits.html#limits- items

• Question 246:

  A company developed a new application that is deployed on Amazon EC2 instances behind an Application Load Balancer. The EC2 instances use the security group named sg- application-servers. The company needs a database to store the data from the application and decides to use an Amazon RDS for MySQL DB instance. The DB instance is deployed in private DB subnet.

  What is the MOST restrictive configuration for the DB instance security group?

  A. Only allow incoming traffic from the sg-application-servers security group on port 3306.
  B. Only allow incoming traffic from the sg-application-servers security group on port 443.
  C. Only allow incoming traffic from the subnet of the application servers on port 3306.
  D. Only allow incoming traffic from the subnet of the application servers on port 443.
  A. Only allow incoming traffic from the sg-application-servers security group on port 3306.

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  Explanation/Reference:

  most restrictive approach is to allow only incoming connections from SG of EC2 instance on port 3306

• Question 247:

  A company is load testing its three-tier production web application deployed with an AWS CloudFormation template on AWS. The Application team is making changes to deploy additional Amazon EC2 and AWS Lambda resources to expand the load testing capacity. A Database Specialist wants to ensure that the changes made by the Application team will not change the Amazon RDS database resources already deployed.

  Which combination of steps would allow the Database Specialist to accomplish this? (Choose two.)

  A. Review the stack drift before modifying the template
  B. Create and review a change set before applying it
  C. Export the database resources as stack outputs
  D. Define the database resources in a nested stack
  E. Set a stack policy for the database resources
  B. Create and review a change set before applying it
  E. Set a stack policy for the database resources

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  Explanation/Reference:

  https://docs.amazonaws.cn/en_us/AWSCloudFormation/latest/UserGuide/best- practices.html#cfn-best-practices-changesets

• Question 248:

  A database specialist wants to ensure that an Amazon Aurora DB cluster is always automatically upgraded to the most recent minor version available. Noticing that there is a new minor version available, the database specialist has issues an AWS CLI command to enable automatic minor version updates. The command runs successfully, but checking the Aurora DB cluster indicates that no update to the Aurora version has been made.

  What might account for this? (Choose two.)

  A. The new minor version has not yet been designated as preferred and requires a manual upgrade.
  B. Configuring automatic upgrades using the AWS CLI is not supported. This must be enabled expressly using the AWS Management Console.
  C. Applying minor version upgrades requires sufficient free space.
  D. The AWS CLI command did not include an apply-immediately parameter.
  E. Aurora has detected a breaking change in the new minor version and has automatically rejected the upgrade.
  A. The new minor version has not yet been designated as preferred and requires a manual upgrade.
  D. The AWS CLI command did not include an apply-immediately parameter.

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  Explanation/Reference:

  "When Amazon RDS designates a minor engine version as the preferred minor engine version, each database that meets both of the following conditions is upgraded to the minor engine version automatically" https://

  docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_UpgradeDBInstance.

  Upgrading.html

  Call the modify-db-instance Amazon CLI command. Specify the name of your DB instance for the --db-instance-identifier option and true for the --auto-minor-version-upgrade option. Optionally, specify the --apply-immediately option to

  immediately enable this setting for your DB instance. Run a separate modify-db-instance command for each DB instance in the cluster.

  https://docs.amazonaws.cn/en_us/AmazonRDS/latest/AuroraUserGuide/AuroraMySQL.Up dates.Patching.html#AuroraMySQL.Updates.AMVU

• Question 249:

  A company is using 5 TB Amazon RDS DB instances and needs to maintain 5 years of monthly database backups for compliance purposes. A Database Administrator must provide Auditors with data within 24 hours. Which solution will meet these requirements and is the MOST operationally efficient?

  A. Create an AWS Lambda function to run on the first day of every month to take a manual RDS snapshot. Move the snapshot to the company's Amazon S3 bucket.
  B. Create an AWS Lambda function to run on the first day of every month to take a manual RDS snapshot.
  C. Create an RDS snapshot schedule from the AWS Management Console to take a snapshot every 30 days.
  D. Create an AWS Lambda function to run on the first day of every month to create an automated RDS snapshot.
  A. Create an AWS Lambda function to run on the first day of every month to take a manual RDS snapshot. Move the snapshot to the company's Amazon S3 bucket.

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  Explanation/Reference:

  Unlike automated backups, manual snapshots aren't subject to the backup retention period. Snapshots don't expire. For very long-term backups of MariaDB, MySQL, and PostgreSQL data, we recommend exporting snapshot data to Amazon S3. If the major version of your DB engine is no longer supported, you can't restore to that version from a snapshot. https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_CreateSnapshot.html

• Question 250:

  A business need a data warehouse system that stores data consistently and in a highly organized fashion. The organization demands rapid response times for end-user inquiries including current-year data, and users must have access to the whole 15-year dataset when necessary. Additionally, this solution must be able to manage a variable volume of incoming inquiries. Costs associated with storing the 100 TB of data must be maintained to a minimum.

  Which solution satisfies these criteria?

  A. 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.
  B. 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.
  C. 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. Enable Amazon Redshift Concurrency Scaling.
  D. 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. Leverage Amazon Redshift elastic resize.
  C. 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. Enable Amazon Redshift Concurrency Scaling.

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  Explanation/Reference:

  https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling.html "With the Concurrency Scaling feature, you can support virtually unlimited concurrent users and concurrent queries, with consistently fast query performance. When concurrency scaling is enabled, Amazon Redshift automatically adds additional cluster capacity when you need it to process an increase in concurrent read queries. Write operations continue as normal on your main cluster. Users always see the most current data, whether the queries run on the main cluster or on a concurrency scaling cluster. You're charged for concurrency scaling clusters only for the time they're in use. For more information about pricing, see Amazon Redshift pricing. You manage which queries are sent to the concurrency scaling cluster by configuring WLM queues. When you enable concurrency scaling for a queue, eligible queries are sent to the concurrency scaling cluster instead of waiting in line."