Amazon DBS-C01 Online Practice Questions and Exam Preparation

Amazon DBS-C01 Online Questions & Answers

• Question 181:

  A Database Specialist is migrating a 2 TB Amazon RDS for Oracle DB instance to an RDS for PostgreSQL DB instance using AWS DMS. The source RDS Oracle DB instance is in a VPC in the us-east-1 Region. The target RDS for PostgreSQL DB instance is in a VPC in the use-west-2 Region.

  Where should the AWS DMS replication instance be placed for the MOST optimal performance?

  A. In the same Region and VPC of the source DB instance
  B. In the same Region and VPC as the target DB instance
  C. In the same VPC and Availability Zone as the target DB instance
  D. In the same VPC and Availability Zone as the source DB instance
  C. In the same VPC and Availability Zone as the target DB instance

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  Explanation/Reference:

  https://docs.aws.amazon.com/dms/latest/userguide/CHAP_ReplicationInstance.VPC.html # CHAP_ReplicationInstance.VPC.Configurations.ScenarioVPCPeer In fact, all the configurations list on above url prefer the replication instance putting into target vpc region / subnet / az. https://docs.aws.amazon.com/dms/latest/sbs/CHAP_SQLServer2Aurora.Steps.CreateRepli cationInstance.html

• Question 182:

  A corporation wishes to move a 1 TB Oracle database from its current location to an Amazon Aurora PostgreSQL DB cluster. The database specialist at the firm noticed that the Oracle database stores 100 GB of large binary objects (LOBs) across many tables. The Oracle database supports LOBs up to 500 MB in size and an average of 350 MB. AWS DMS was picked by the Database Specialist to transfer the data with the most replication instances.

  How should the database specialist improve the transfer of the database to AWS DMS?

  A. Create a single task using full LOB mode with a LOB chunk size of 500 MB to migrate the data and LOBs together
  B. Create two tasks: task1 with LOB tables using full LOB mode with a LOB chunk size of 500 MB and task2 without LOBs
  C. Create two tasks: task1 with LOB tables using limited LOB mode with a maximum LOB size of 500 MB and task 2 without LOBs
  D. Create a single task using limited LOB mode with a maximum LOB size of 500 MB to migrate data and LOBs together
  C. Create two tasks: task1 with LOB tables using limited LOB mode with a maximum LOB size of 500 MB and task 2 without LOBs

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  Explanation/Reference:

  https://docs.aws.amazon.com/dms/latest/userguide/CHAP_BestPractices.html#CHAP_Bes tPractices.LOBS,

  "AWS DMS migrates LOB data in two phases: 1. AWS DMS creates a new row in the target table and populates the row with all data except the associated LOB value. 2.AWS DMS updates the row in the target table with the LOB data." This

  means that we would need two tasks, one per phase and use limited LOB mode for best performance.

• Question 183:

  A company stores session history for its users in an Amazon DynamoDB table. The company has a large user base and generates large amounts of session data.

  Teams analyze the session data for 1 week, and then the data is no longer needed. A database specialist needs to design an automated solution to purge session data that is more than 1 week old.

  Which strategy meets these requirements with the MOST operational efficiency?

  A. Create an AWS Step Functions state machine with a DynamoDB DeleteItem operation that uses the ConditionExpression parameter to delete items older than a week. Create an Amazon EventBridge (Amazon CloudWatch Events) scheduled rule that runs the Step Functions state machine on a weekly basis.
  B. Create an AWS Lambda function to delete items older than a week from the DynamoDB table. Create an Amazon EventBridge (Amazon CloudWatch Events) scheduled rule that triggers the Lambda function on a weekly basis.
  C. Enable Amazon DynamoDB Streams on the table. Use a stream to invoke an AWS Lambda function to delete items older than a week from the DynamoDB table
  D. Enable TTL on the DynamoDB table and set a Number data type as the TTL attribute. DynamoDB will automatically delete items that have a TTL that is less than the current time.
  D. Enable TTL on the DynamoDB table and set a Number data type as the TTL attribute. DynamoDB will automatically delete items that have a TTL that is less than the current time.

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  Explanation/Reference:

  https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/TTL.html

• Question 184:

  The website of a manufacturing firm makes use of an Amazon Aurora PostgreSQL database cluster.

  Which settings will result in the LEAST amount of downtime for the application during failover? (Select three.)

  A. Use the provided read and write Aurora endpoints to establish a connection to the Aurora DB cluster.
  B. Create an Amazon CloudWatch alert triggering a restore in another Availability Zone when the primary Aurora DB cluster is unreachable.
  C. Edit and enable Aurora DB cluster cache management in parameter groups.
  D. Set TCP keepalive parameters to a high value.
  E. Set JDBC connection string timeout variables to a low value.
  F. Set Java DNS caching timeouts to a high value.
  A. Use the provided read and write Aurora endpoints to establish a connection to the Aurora DB cluster.
  C. Edit and enable Aurora DB cluster cache management in parameter groups.
  E. Set JDBC connection string timeout variables to a low value.

  ---

  Explanation/Reference:

  https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraPostgreSQL.Be stPractices.html https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraPostgreSQL.clu ster-cache-mgmt.html https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraPostgreSQL.Be stPractices.html#AuroraPostgreSQL.BestPractices.FastFailover.TCPKeepalives

• Question 185:

  A company is about to launch a new product, and test databases must be re-created from production data. The company runs its production databases on an Amazon Aurora MySQL DB cluster. A Database Specialist needs to deploy a solution to create these test databases as quickly as possible with the least amount of administrative effort.

  What should the Database Specialist do to meet these requirements?

  A. Restore a snapshot from the production cluster into test clusters
  B. Create logical dumps of the production cluster and restore them into new test clusters
  C. Use database cloning to create clones of the production cluster
  D. Add an additional read replica to the production cluster and use that node for testing
  C. Use database cloning to create clones of the production cluster

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  Explanation/Reference:

  https://aws.amazon.com/getting-started/hands-on/aurora-cloning-backtracking/ "Cloning an Aurora cluster is extremely useful if you want to assess the impact of changes to your database, or if you need to perform workload-intensive operations--such as exporting data or running analytical queries, or simply if you want to use a copy of your production database in a development or testing environment. You can make multiple clones of your Aurora DB cluster. You can even create additional clones from other clones, with the constraint that the clone databases must be created in the same region as the source databases.

• Question 186:

  A company is planning to migrate a 40 TB Oracle database to an Amazon Aurora PostgreSQL DB cluster by using a single AWS Database Migration Service (AWS DMS) task within a single replication instance. During early testing, AWS DMS is not scaling to the company's needs. Full load and change data capture (CDC) are taking days to complete.

  The source database server and the target DB cluster have enough network bandwidth and CPU bandwidth for the additional workload. The replication instance has enough resources to support the replication. A database specialist needs to improve database performance, reduce data migration time, and create multiple DMS tasks.

  Which combination of changes will meet these requirements? (Choose two.)

  A. Increase the value of the ParallelLoadThreads parameter in the DMS task settings for the tables.
  B. Use a smaller set of tables with each DMS task. Set the MaxFullLoadSubTasks parameter to a higher value.
  C. Use a smaller set of tables with each DMS task. Set the MaxFullLoadSubTasks parameter to a lower value.
  D. Use parallel load with different data boundaries for larger tables.
  E. Run the DMS tasks on a larger instance class. Increase local storage on the instance.
  B. Use a smaller set of tables with each DMS task. Set the MaxFullLoadSubTasks parameter to a higher value.
  D. Use parallel load with different data boundaries for larger tables.

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  Explanation/Reference:

  Correct Answer: B and D from Amazon documents: AWS Database Migration Service (AWS DMS) is a service that helps you migrate data from one data source to another. AWS DMS supports full load and change data capture (CDC) modes, which enable you to migrate data with minimal downtime. AWS DMS also supports parallel load, which allows you to load data from multiple tables or partitions concurrently. To improve database performance, reduce data migration time, and create multiple DMS tasks, the database specialist should use the following combination of changes: Use a smaller set of tables with each DMS task. Set the MaxFullLoadSubTasks parameter to a higher value. This change will allow the database specialist to split the migration workload into smaller and more manageable units, and increase the parallelism of the full load process. The MaxFullLoadSubTasks parameter specifies the maximum number of tables that are loaded in parallel for each DMS task. By setting this parameter to a higher value, the database specialist can increase the throughput and performance of the full load process. Use parallel load with different data boundaries for larger tables. This change will allow the database specialist to divide the larger tables into smaller chunks based on a partition key or a range of values, and load them in parallel using multiple DMS tasks. Parallel load can significantly reduce the migration time and improve the performance of large tables. Therefore, option B and D are the correct combination of changes to meet the requirements. Option A is incorrect because increasing the value of the ParallelLoadThreads parameter in the DMS task settings for the tables will not improve the performance or reduce the migration time significantly. The ParallelLoadThreads parameter specifies the number of threads that are used to load data from a single table or partition. By increasing this parameter, the database specialist may increase the CPU utilization and network bandwidth consumption of the source and target databases, but not the parallelism of the full load process. Option C is incorrect because using a smaller set of tables with each DMS task and setting the MaxFullLoadSubTasks parameter to a lower value will decrease the parallelism and performance of the full load process. Option E is incorrect because running the DMS tasks on a larger instance class and increasing local storage on the instance will not address the root cause of the performance issue, which is the lack of parallelism and partitioning of the large tables.

• Question 187:

  A company is migrating a mission-critical 2-TB Oracle database from on premises to Amazon Aurora. The cost for the database migration must be kept to a minimum, and both the on-premises Oracle database and the Aurora DB cluster must remain open for write traffic until the company is ready to completely cut over to Aurora.

  Which combination of actions should a database specialist take to accomplish this migration as quickly as possible? (Choose two.)

  A. Use the AWS Schema Conversion Tool (AWS SCT) to convert the source database schema. Then restore the converted schema to the target Aurora DB cluster.
  B. Use Oracle's Data Pump tool to export a copy of the source database schema and manually edit the schema in a text editor to make it compatible with Aurora.
  C. Create an AWS DMS task to migrate data from the Oracle database to the Aurora DB cluster. Select the migration type to replicate ongoing changes to keep the source and target databases in sync until the company is ready to move all user traffic to the Aurora DB cluster.
  D. Create an AWS DMS task to migrate data from the Oracle database to the Aurora DB cluster. Once the initial load is complete, create an AWS Kinesis Data Firehose stream to perform change data capture (CDC) until the company is ready to move all user traffic to the Aurora DB cluster.
  E. Create an AWS Glue job and related resources to migrate data from the Oracle database to the Aurora DB cluster. Once the initial load is complete, create an AWS DMS task to perform change data capture (CDC) until the company is ready to move all user traffic to the Aurora DB cluster.
  A. Use the AWS Schema Conversion Tool (AWS SCT) to convert the source database schema. Then restore the converted schema to the target Aurora DB cluster.
  C. Create an AWS DMS task to migrate data from the Oracle database to the Aurora DB cluster. Select the migration type to replicate ongoing changes to keep the source and target databases in sync until the company is ready to move all user traffic to the Aurora DB cluster.

• Question 188:

  A company has a production Amazon Aurora Db cluster that serves both online transaction processing (OLTP) transactions and compute-intensive reports. The reports run for 10% of the total cluster uptime while the OLTP transactions run all the time. The company has benchmarked its workload and determined that a six-node Aurora DB cluster is appropriate for the peak workload.

  The company is now looking at cutting costs for this DB cluster, but needs to have a sufficient number of nodes in the cluster to support the workload at different times. The workload has not changed since the previous benchmarking exercise.

  How can a Database Specialist address these requirements with minimal user involvement?

  A. Split up the DB cluster into two different clusters: one for OLTP and the other for reporting. Monitor and set up replication between the two clusters to keep data consistent.
  B. Review all evaluate the peak combined workload. Ensure that utilization of the DB cluster node is at an acceptable level. Adjust the number of instances, if necessary.
  C. Use the stop cluster functionality to stop all the nodes of the DB cluster during times of minimal workload. The cluster can be restarted again depending on the workload at the time.
  D. Set up automatic scaling on the DB cluster. This will allow the number of reader nodes to adjust automatically to the reporting workload, when needed.
  D. Set up automatic scaling on the DB cluster. This will allow the number of reader nodes to adjust automatically to the reporting workload, when needed.

• Question 189:

  A development team at an international gaming company is experimenting with Amazon DynamoDB to store in-game events for three mobile games. The most popular game hosts a maximum of 500,000 concurrent users, and the least popular game hosts a maximum of 10,000 concurrent users. The average size of an event is 20 KB, and the average user session produces one event each second. Each event is tagged with a time in milliseconds and a globally unique identifier.

  The lead developer created a single DynamoDB table for the events with the following schema:

  Partition key: game name Sort key: event identifier Local secondary index: player identifier Event time The tests were successful in a small-scale development environment. However, when deployed to production, new events stopped being added to the table and the logs show DynamoDB failures with the ItemCollectionSizeLimitExceededException error code.

  Which design change should a database specialist recommend to the development team?

  A. Use the player identifier as the partition key. Use the event time as the sort key. Add a global secondary index with the game name as the partition key and the event time as the sort key.
  B. Create two tables. Use the game name as the partition key in both tables. Use the event time as the sort key for the first table. Use the player identifier as the sort key for the second table.
  C. Replace the sort key with a compound value consisting of the player identifier collated with the event time, separated by a dash. Add a local secondary index with the player identifier as the sort key.
  D. Create one table for each game. Use the player identifier as the partition key. Use the event time as the sort key.
  D. Create one table for each game. Use the player identifier as the partition key. Use the event time as the sort key.

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  Explanation/Reference:

  The correct answer is D. Create one table for each game. Use the player identifier as the partition key. Use the event time as the sort key.

  The explanation is as follows:

  The ItemCollectionSizeLimitExceededException error occurs when an item collection exceeds the 10 GB limit1. An item collection is a group of items that have the same partition key value but different sort key values2. In this case, the item

  collection is based on the game name, which has only three possible values. This means that all events for each game are stored in the same item collection, which can easily exceed the 10 GB limit given the high volume and size of events.

  To avoid this error, a database specialist should recommend a design change that distributes the events across more partitions and reduces the size of each item collection. Option D achieves this by creating one table for each game, and

  using the player identifier as the partition key. This way, each event is stored in a separate partition based on the player identifier, and sorted by the event time. This design also supports efficient queries by game, player, and time range.

  Option A is incorrect because it still uses a single table for all events, which can cause hot partitions and throttling due to uneven access patterns across games. Also, using the player identifier as the partition key can result in many small

  partitions that are underutilized and waste provisioned capacity. Adding a global secondary index with the game name as the partition key and the event time as the sort key does not solve the problem of item collection size limit, because

  global secondary indexes have their own item collections that are subject to the same limit3.

  Option B is incorrect because it creates two tables with redundant data and increases storage costs. Also, using the game name as the partition key in both tables does not solve the problem of item collection size limit, as explained above.

  Option C is incorrect because it still uses a single table for all events, which can cause hot partitions and throttling due to uneven access patterns across games. Also, replacing the sort key with a compound value consisting of the player

  identifier collated with the event time does not reduce the size of each item collection, because each event still has a unique sort key value. Adding a local secondary index with the player identifier as the sort key does not solve the problem of

  item collection size limit, because local secondary indexes share the same item collections as their base table4.

  References: 1: ItemCollectionSizeLimitExceededException (AWS SDK for Java - 1.12.547)

  2: Best practices for designing and using partition keys effectively - Amazon DynamoDB

  3: Global Secondary Indexes - Amazon DynamoDB 4: Local Secondary Indexes - Amazon DynamoDB

• Question 190:

  An online gaming company is planning to launch a new game with Amazon DynamoDB as its data store. The database should be designated to support the following use cases:

  Update scores in real time whenever a player is playing the game. Retrieve a player's score details for a specific game session.

  A Database Specialist decides to implement a DynamoDB table. Each player has a unique user_id and each game has a unique game_id.

  Which choice of keys is recommended for the DynamoDB table?

  A. Create a global secondary index with game_id as the partition key
  B. Create a global secondary index with user_id as the partition key
  C. Create a composite primary key with game_id as the partition key and user_id as the sort key
  D. Create a composite primary key with user_id as the partition key and game_id as the sort key
  D. Create a composite primary key with user_id as the partition key and game_id as the sort key

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  Explanation/Reference:

  https://aws.amazon.com/blogs/database/amazon-dynamodb-gaming-use-cases-and- design-patterns/

  "EA uses the user ID as the partition key and primary key (a 1:1 modeling pattern)." https://aws.amazon.com/blogs/database/choosing-the-right-dynamodb-partition-key/ "Partition key and sort key: Referred to as a composite primary key, this

  type of key is composed of two attributes. The first attribute is the partition key, and the second attribute is the sort key."