Amazon DBS-C01 Online Practice Questions and Exam Preparation

Amazon DBS-C01 Online Questions & Answers

• Question 71:

  A Database Specialist is migrating an on-premises Microsoft SQL Server application database to Amazon RDS for PostgreSQL using AWS DMS. The application requires minimal downtime when the RDS DB instance goes live.

  What change should the Database Specialist make to enable the migration?

  A. Configure the on-premises application database to act as a source for an AWS DMS full load with ongoing change data capture (CDC)
  B. Configure the AWS DMS replication instance to allow both full load and ongoing change data capture (CDC)
  C. Configure the AWS DMS task to generate full logs to allow for ongoing change data capture (CDC)
  D. Configure the AWS DMS connections to allow two-way communication to allow for ongoing change data capture (CDC)
  A. Configure the on-premises application database to act as a source for an AWS DMS full load with ongoing change data capture (CDC)

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  Explanation/Reference:

  "requires minimal downtime when the RDS DB instance goes live" in order to do CDC: "you must first ensure that ARCHIVELOG MODE is on to provide information to LogMiner. AWS DMS uses LogMiner to read information from the archive

  logs so that AWS DMS can capture changes"

  https://docs.aws.amazon.com/dms/latest/sbs/chap-oracle2postgresql.steps.configureoracle.html

  "If you want to capture and apply changes (CDC), then you also need the following privileges."

• Question 72:

  A company's database specialist implements an AWS Database Migration Service (AWS DMS) task for change data capture (CDC) to replicate data from an on- premises Oracle database to Amazon S3. When usage of the company's application increases, the database specialist notices multiple hours of latency with the CDC.

  Which solutions will reduce this latency? (Choose two.)

  A. Configure the DMS task to run in full large binary object (LOB) mode.
  B. Configure the DMS task to run in limited large binary object (LOB) mode.
  C. Create a Multi-AZ replication instance.
  D. Load tables in parallel by creating multiple replication instances for sets of tables that participate in common transactions.
  E. Replicate tables in parallel by creating multiple DMS tasks for sets of tables that do not participate in common transactions.
  B. Configure the DMS task to run in limited large binary object (LOB) mode.
  E. Replicate tables in parallel by creating multiple DMS tasks for sets of tables that do not participate in common transactions.

• Question 73:

  A company has a 20 TB production Amazon Aurora DB cluster. The company runs a large batch job overnight to load data into the Aurora DB cluster. To ensure the company's development team has the most up-to-date data for testing, a copy of the DB cluster must be available in the shortest possible time after the batch job completes.

  How should this be accomplished?

  A. Use the AWS CLI to schedule a manual snapshot of the DB cluster. Restore the snapshot to a new DB cluster using the AWS CLI.
  B. Create a dump file from the DB cluster. Load the dump file into a new DB cluster.
  C. Schedule a job to create a clone of the DB cluster at the end of the overnight batch process.
  D. Set up a new daily AWS DMS task that will use cloning and change data capture (CDC) on the DB cluster to copy the data to a new DB cluster. Set up a time for the AWS DMS stream to stop when the new cluster is current.
  C. Schedule a job to create a clone of the DB cluster at the end of the overnight batch process.

• Question 74:

  A company has an AWS CloudFormation stack that defines an Amazon RDS DB instance. The company accidentally deletes the stack and loses recent data from the DB instance. A database specialist must change the CloudFormation template for the RDS resource to reduce the chance of accidental data loss from the DB instance in the future.

  Which combination of actions should the database specialist take to meet this requirement? (Choose three.)

  A. Set the DeletionProtection property to True.
  B. Set the MultiAZ property to True.
  C. Set the TerminationProtection property to True.
  D. Set the DeleteAutomatedBackups property to False.
  E. Set the DeletionPolicy attribute to No.
  F. Set the DeletionPolicy attribute to Retain.
  A. Set the DeletionProtection property to True.
  D. Set the DeleteAutomatedBackups property to False.
  F. Set the DeletionPolicy attribute to Retain.

• Question 75:

  A business just transitioned from an on-premises Oracle database to Amazon Aurora PostgreSQL. Following the move, the organization observed that every day around 3:00 PM, the application's response time is substantially slower. The firm has determined that the problem is with the database, not the application.

  Which set of procedures should the Database Specialist do to locate the erroneous PostgreSQL query most efficiently?

  A. Create an Amazon CloudWatch dashboard to show the number of connections, CPU usage, and disk space consumption. Watch these dashboards during the next slow period.
  B. Launch an Amazon EC2 instance, and install and configure an open-source PostgreSQL monitoring tool that will run reports based on the output error logs.
  C. Modify the logging database parameter to log all the queries related to locking in the database and then check the logs after the next slow period for this information.
  D. Enable Amazon RDS Performance Insights on the PostgreSQL database. Use the metrics to identify any queries that are related to spikes in the graph during the next slow period.
  D. Enable Amazon RDS Performance Insights on the PostgreSQL database. Use the metrics to identify any queries that are related to spikes in the graph during the next slow period.

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  Explanation/Reference:

  https://aws.amazon.com/blogs/database/optimizing-and-tuning-queries-in-amazon-rds- postgresql-based-on-native-and-external-tools/

  "AWS recently released a feature called Amazon RDS Performance Insights, which provides an easy-to-understand dashboard for detecting performance problems in terms of load." "AWS recently released a feature called Amazon RDS Performance Insights, which provides an easy-to-understand dashboard for detecting performance problems in terms of load."

• Question 76:

  A company developed an AWS CloudFormation template used to create all new Amazon DynamoDB tables in its AWS account. The template configures provisioned throughput capacity using hard-coded values. The company wants to change the template so that the tables it creates in the future have independently configurable read and write capacity units assigned.

  Which solution will enable this change?

  A. Add values for the rcuCount and wcuCount parameters to the Mappings section of the template. Configure DynamoDB to provision throughput capacity using the stack's mappings.
  B. Add values for two Number parameters, rcuCount and wcuCount, to the template. Replace the hard-coded values with calls to the Ref intrinsic function, referencing the new parameters.
  C. Add values for the rcuCount and wcuCount parameters as outputs of the template. Configure DynamoDB to provision throughput capacity using the stack outputs.
  D. Add values for the rcuCount and wcuCount parameters to the Mappings section of the template. Replace the hard-coded values with calls to the Ref intrinsic function, referencing the new parameters.
  B. Add values for two Number parameters, rcuCount and wcuCount, to the template. Replace the hard-coded values with calls to the Ref intrinsic function, referencing the new parameters.

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  Explanation/Reference:

  Input parameter and FindInMap You can use an input parameter with the Fn::FindInMap function to refer to a specific value in a map. For example, suppose you have a list of regions and environment types that map to a specific AMI ID. You can select the AMI ID that your stack uses by using an input parameter (EnvironmentType). To determine the region, use the AWS::Region pseudo parameter, which gets the AWS Region in which you create the stack. https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/parameters-section- structure.html

• Question 77:

  An electric utility company wants to store power plant sensor data in an Amazon DynamoDB table. The utility company has over 100 power plants and each power plant has over 200 sensors that send data every 2 seconds. The sensor data includes time with milliseconds precision, a value, and a fault attribute if the sensor is malfunctioning. Power plants are identified by a globally unique identifier. Sensors are identified by a unique identifier within each power plant. A database specialist needs to design the table to support an efficient method of finding all faulty sensors within a given power plant.

  Which schema should the database specialist use when creating the DynamoDB table to achieve the fastest query time when looking for faulty sensors?

  A. Use the plant identifier as the partition key and the measurement time as the sort key. Create a global secondary index (GSI) with the plant identifier as the partition key and the fault attribute as the sort key.
  B. Create a composite of the plant identifier and sensor identifier as the partition key. Use the measurement time as the sort key. Create a local secondary index (LSI) on the fault attribute.
  C. Create a composite of the plant identifier and sensor identifier as the partition key. Use the measurement time as the sort key. Create a global secondary index (GSI) with the plant identifier as the partition key and the fault attribute as the sort key.
  D. Use the plant identifier as the partition key and the sensor identifier as the sort key. Create a local secondary index (LSI) on the fault attribute.
  D. Use the plant identifier as the partition key and the sensor identifier as the sort key. Create a local secondary index (LSI) on the fault attribute.

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  Explanation/Reference:

  Plant id as partition key and Sensor id as a sort key. Fault can be identified quickly using the local secondary index and associated plant and sensor can be identified easily.

• Question 78:

  A company is developing a multi-tier web application hosted on AWS using Amazon Aurora as the database. The application needs to be deployed to production and other non- production environments. A Database Specialist needs to specify different MasterUsername and MasterUserPassword properties in the AWS CloudFormation templates used for automated deployment. The CloudFormation templates are version controlled in the company's code repository. The company also needs to meet compliance requirement by routinely rotating its database master password for production.

  What is most secure solution to store the master password?

  A. Store the master password in a parameter file in each environment. Reference the environment-specific parameter file in the CloudFormation template.
  B. Encrypt the master password using an AWS KMS key. Store the encrypted master password in the CloudFormation template.
  C. Use the secretsmanager dynamic reference to retrieve the master password stored in AWS Secrets Manager and enable automatic rotation.
  D. Use the ssm dynamic reference to retrieve the master password stored in the AWS Systems Manager Parameter Store and enable automatic rotation.
  C. Use the secretsmanager dynamic reference to retrieve the master password stored in AWS Secrets Manager and enable automatic rotation.

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  Explanation/Reference:

  "By using the secure string support in CloudFormation with dynamic references you can better maintain your infrastructure as code. You'll be able to avoid hard coding passwords into your templates and you can keep these runtime configuration parameters separated from your code. Moreover, when properly used, secure strings will help keep your development and production code as similar as possible, while continuing to make your infrastructure code suitable for continuous deployment pipelines." https://aws.amazon.com/blogs/mt/using-aws-systems-manager-parameter-store-secure- string-parameters-in-aws-cloudformation-templates/ https://aws.amazon.com/blogs/security/how-to-use-aws-secretsmanager-rotate- credentials-amazon-rds-database-types-oracle/

• Question 79:

  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.
  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).

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  Explanation/Reference:

  https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraMySQL.Integrat ing.SaveIntoS3.html

• Question 80:

  A business that specializes in internet advertising is developing an application that will show adverts to its customers. The program stores data in an Amazon DynamoDB database. Additionally, the application caches its reads using a

  DynamoDB Accelerator (DAX) cluster. The majority of reads come via the GetItem and BatchGetItem queries. The application does not need consistency of readings.

  The application cache does not behave as intended after deployment. Specific extremely consistent queries to the DAX cluster are responding in several milliseconds rather than microseconds.

  How can the business optimize cache behavior in order to boost application performance?

  A. Increase the size of the DAX cluster.
  B. Configure DAX to be an item cache with no query cache
  C. Use eventually consistent reads instead of strongly consistent reads.
  D. Create a new DAX cluster with a higher TTL for the item cache.
  C. Use eventually consistent reads instead of strongly consistent reads.