Amazon DBS-C01 Online Practice Questions and Exam Preparation

Amazon DBS-C01 Online Questions & Answers

• Question 121:

  A company just migrated to Amazon Aurora PostgreSQL from an on-premises Oracle database. After the migration, the company discovered there is a period of time every day around 3:00 PM where the response time of the application is noticeably slower. The company has narrowed down the cause of this issue to the database and not the application.

  Which set of steps should the Database Specialist take to most efficiently find the problematic PostgreSQL query?

  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.

• Question 122:

  A company is looking to move an on-premises IBM Db2 database running AIX on an IBM POWER7 server. Due to escalating support and maintenance costs, the company is exploring the option of moving the workload to an Amazon Aurora PostgreSQL DB cluster.

  What is the quickest way for the company to gather data on the migration compatibility?

  A. Perform a logical dump from the Db2 database and restore it to an Aurora DB cluster. Identify the gaps and compatibility of the objects migrated by comparing row counts from source and target tables.
  B. Run AWS DMS from the Db2 database to an Aurora DB cluster. Identify the gaps and compatibility of the objects migrated by comparing the row counts from source and target tables.
  C. Run native PostgreSQL logical replication from the Db2 database to an Aurora DB cluster to evaluate the migration compatibility.
  D. Run the AWS Schema Conversion Tool (AWS SCT) from the Db2 database to an Aurora DB cluster. Create a migration assessment report to evaluate the migration compatibility.
  D. Run the AWS Schema Conversion Tool (AWS SCT) from the Db2 database to an Aurora DB cluster. Create a migration assessment report to evaluate the migration compatibility.

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  Explanation/Reference:

  Reference:

  https://docs.aws.amazon.com/SchemaConversionTool/latest/userguide/Schema- Conversion- Tool.pdf

  Converts DB/DW schema from source to target (including procedures / views / secondary indexes / FK and constraints)

  Mainly for heterogeneous DB migrations and DW migrations

• Question 123:

  A software development company is using Amazon Aurora MySQL DB clusters for several use cases, including development and reporting. These use cases place unpredictable and varying demands on the Aurora DB clusters, and can cause momentary spikes in latency. System users run ad-hoc queries sporadically throughout the week. Cost is a primary concern for the company, and a solution that does not require significant rework is needed.

  Which solution meets these requirements?

  A. Create new Aurora Serverless DB clusters for development and reporting, then migrate to these new DB clusters.
  B. Upgrade one of the DB clusters to a larger size, and consolidate development and reporting activities on this larger DB cluster.
  C. Use existing DB clusters and stop/start the databases on a routine basis using scheduling tools.
  D. Change the DB clusters to the burstable instance family.
  A. Create new Aurora Serverless DB clusters for development and reporting, then migrate to these new DB clusters.

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  Explanation/Reference:

  https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Concepts.DBInstance Class.html

• Question 124:

  A company with branch offices in Portland, New York, and Singapore has a three-tier web application that leverages a shared database. The database runs on Amazon RDS for MySQL and is hosted in the us-west-2 Region. The application has a distributed front end deployed in the us-west-2, ap-southheast-1, and us-east-2 Regions.

  This front end is used as a dashboard for Sales Managers in each branch office to see current sales statistics. There are complaints that the dashboard performs more slowly in the Singapore location than it does in Portland or New York. A solution is needed to provide consistent performance for all users in each location.

  Which set of actions will meet these requirements?

  A. Take a snapshot of the instance in the us-west-2 Region. Create a new instance from the snapshot in the ap-southeast-1 Region. Reconfigure the ap-southeast-1 front-end dashboard to access this instance.
  B. Create an RDS read replica in the ap-southeast-1 Region from the primary RDS DB instance in the us- west-2 Region. Reconfigure the ap-southeast-1 front-end dashboard to access this instance.
  C. Create a new RDS instance in the ap-southeast-1 Region. Use AWS DMS and change data capture (CDC) to update the new instance in the ap-southeast-1 Region. Reconfigure the ap-southeast-1 front-end dashboard to access this instance.
  D. Create an RDS read replica in the us-west-2 Region where the primary instance resides. Create a read replica in the ap-southeast-1 Region from the read replica located on the us- west-2 Region. Reconfigure the ap-southeast-1 front-end dashboard to access this instance.
  B. Create an RDS read replica in the ap-southeast-1 Region from the primary RDS DB instance in the us- west-2 Region. Reconfigure the ap-southeast-1 front-end dashboard to access this instance.

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  Explanation/Reference:

  https://aws.amazon.com/rds/features/read-replicas/

  "Amazon RDS Read Replicas provide enhanced performance and durability for RDS database (DB) instances. They make it easy to elastically scale out beyond the capacity constraints of a single DB instance for read-heavy database

  workloads. You can create one or more replicas of a given source DB Instance and serve high-volume application read traffic from multiple copies of your data, thereby increasing aggregate read throughput. "

  https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_ReadRepl.XRgn.html

• Question 125:

  A retail company uses Amazon Redshift for its 1 PB data warehouse. Several analytical workloads run on a Redshift cluster. The tables within the cluster have grown rapidly. End users are reporting poor performance of daily reports that run on the transaction fact tables.

  A database specialist must change the design of the tables to improve the reporting performance. All the changes must be applied dynamically. The changes must have the least possible impact on users and must optimize the overall table size.

  Which solution will meet these requirements?

  A. Use the STL SCAN view to understand how the tables are getting scanned. Identify the columns that are used in filter and group by conditions. Create a temporary table with the identified columns as sort keys and compression as Zstandard (ZSTD) by copying the data from the original table. Drop the original table. Give the temporary table the same name that the original table had.
  B. Run an explain plan to analyze the queries on the tables. Consider recommendations from Amazon Redshift Advisor. Identify the columns that are used in filter and group by conditions. Convert the recommended columns from Redshift Advisor into sort keys with compression encoding set to RAW. Set the rest of the column compression encoding to AZ64.
  C. Run an explain plan to analyze the queries on the tables. Consider recommendations from Amazon Redshift Advisor. Identify the columns that are used in filter and group by conditions. Convert the recommended columns from Redshift Advisor into sort keys with compression encoding set to I_ZO. Set the rest of the column compression encoding to Zstandard (ZSTD).
  D. Run an explain plan to analyze the queries on the tables. Consider recommendations from Amazon Redshift Advisor. Identify the columns that are used in filter and group by conditions. Create a deep copy of the table with the identified columns as sort keys and compression for all columns as Zstandard (ZSTD) by using a bulk insert. Drop the original table. Give the copy table the same name that the original table had.
  D. Run an explain plan to analyze the queries on the tables. Consider recommendations from Amazon Redshift Advisor. Identify the columns that are used in filter and group by conditions. Create a deep copy of the table with the identified columns as sort keys and compression for all columns as Zstandard (ZSTD) by using a bulk insert. Drop the original table. Give the copy table the same name that the original table had.

• Question 126:

  A media company hosts a highly available news website on AWS but needs to improve its page load time, especially during very popular news releases. Once a news page is published, it is very unlikely to change unless an error is identified. The company has decided to use Amazon ElastiCache.

  What is the recommended strategy for this use case?

  A. Use ElastiCache for Memcached with write-through and long time to live (TTL)
  B. Use ElastiCache for Redis with lazy loading and short time to live (TTL)
  C. Use ElastiCache for Memcached with lazy loading and short time to live (TTL)
  D. Use ElastiCache for Redis with write-through and long time to live (TTL)
  A. Use ElastiCache for Memcached with write-through and long time to live (TTL)

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  Explanation/Reference:

  The recommended strategy for this use case is option A: use ElastiCache for Memcached with write-through and long time to live (TTL).

  Amazon ElastiCache is a fully managed in-memory data store service that supports two open source engines: Memcached and Redis. Amazon ElastiCache can be used to improve the performance and scalability of web applications by caching frequently accessed data in memory, reducing the load and latency of database queries. Memcached and Redis have different features and use cases. Memcached is a simple, high-performance, distributed caching system that supports a large number of concurrent connections and large object sizes. Redis is an advanced, feature-rich, in-memory data structure store that supports data persistence, replication, transactions, pub/sub, Lua scripting, and various data types. For this use case, Memcached is more suitable than Redis because the news website does not need the advanced features of Redis, such as data persistence or replication. The news website only needs a fast and simple caching solution that can handle high traffic and large objects. Write-through and lazy loading are two common caching strategies that determine when and how data is written to the cache. Write-through is a strategy that writes data to the cache whenever it is written to the database. Lazy loading is a strategy that writes data to the cache only when it is requested for the first time. For this use case, write-through is more suitable than lazy loading because the news website needs to improve its page load time, especially during very popular news releases. Write-through ensures that the cache always has the most up-to-date data and avoids cache misses or stale data. Lazy loading may cause cache misses or stale data if the data is not cached or updated in time. Time to live (TTL) is a parameter that specifies how long an item can remain in the cache before it expires and is deleted. TTL can be used to control the cache size and freshness. For this use case, long TTL is more suitable than short TTL because the news website has a low probability of changing its data once a news page is published. Long TTL allows the data to stay in the cache longer and reduces the frequency of cache updates or evictions. Short TTL may cause unnecessary cache updates or evictions if the data does not change frequently. Therefore, option A is the recommended strategy for this use case because it uses ElastiCache for Memcached with write-through and long TTL, which provides a fast and simple caching solution that can handle high traffic and large objects, and ensures that the cache always has the most up-to-date and relevant data.

• Question 127:

  A company runs online transaction processing (OLTP) workloads on an Amazon RDS for PostgreSQL Multi-AZ DB instance. The company recently conducted tests on the database after business hours, and the tests generated additional database logs. As a result, free storage of the DB instance is low and is expected to be exhausted in 2 days.

  The company wants to recover the free storage that the additional logs consumed. The solution must not result in downtime for the database.

  Which solution will meet these requirements?

  A. Modify the rds.log_retention_period parameter to 0. Reboot the DB instance to save the changes.
  B. Modify the rds.log_retention_period parameter to 1440. Wait up to 24 hours for database logs to be deleted.
  C. Modify the temp file_limit parameter to a smaller value to reclaim space on the DB instance.
  D. Modify the rds.log_retention_period parameter to 1440. Reboot the DB instance to save the changes.
  B. Modify the rds.log_retention_period parameter to 1440. Wait up to 24 hours for database logs to be deleted.

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  Explanation/Reference:

  Correct Answer: B

  from Amazon documents:

  The rds.log_retention_period parameter specifies how long your RDS for PostgreSQL DB instance keeps its log files. The default setting is 3 days (4,320 minutes), but you can set this value to anywhere from 1 day (1,440 minutes) to 7 days

  (10,080 minutes)123. By reducing the log retention period, you can free up storage space on your DB instance without affecting its availability or performance. To modify the rds.log_retention_period parameter, you need to use a custom DB

  parameter group for your RDS for PostgreSQL instance. You can modify the parameter value using the AWS Management Console, the AWS CLI, or the RDS API1. The parameter change is applied immediately, but it may take up to 24

  hours for the database logs to be deleted2. Therefore, you do not need to reboot the DB instance to save the changes or to reclaim the storage space.

  Therefore, option B is the correct solution to meet the requirements. Option A is incorrect because setting the rds.log_retention_period parameter to 0 disables log retention and prevents you from viewing or downloading any database logs1.

  Rebooting the DB instance is also unnecessary and may cause downtime. Option C is incorrect because the temp file_limit parameter controls the maximum size of temporary files that a session can generate, not the size of database logs.

  Modifying this parameter will not reclaim any storage space on the DB instance. Option D is incorrect because rebooting the DB instance is not required to save the changes or to reclaim the storage space.

• Question 128:

  A financial services company is using AWS Database Migration Service (AWS OMS) to migrate Its databases from on-premises to AWS. A database administrator is working on replicating a database to AWS from on-premises using full load and change data capture (CDC). During the CDC replication, the database administrator observed that the target latency was high and slowly increasing.

  What could be the root causes for this high target latency? (Select TWO.)

  A. There was ongoing maintenance on the replication instance
  B. The source endpoint was changed by modifyng the task
  C. Loopback changes had affected the source and target instances-
  D. There was no primary key or index in the target database.
  E. There were resource bottlenecks in the replication instance
  D. There was no primary key or index in the target database.
  E. There were resource bottlenecks in the replication instance

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  Explanation/Reference:

  Target latency is the amount of time that AWS DMS takes to apply changes from the source database to the target database1. High target latency can indicate performance issues or replication errors in the AWS DMS task.

  One possible cause of high target latency is the lack of a primary key or index in the target database. A primary key or index helps AWS DMS identify and apply changes to the corresponding rows in the target database. Without a primary key

  or index, AWS DMS has to scan the entire table to find the matching rows, which can increase the target latency and consume more CPU and memory resources2.

  Another possible cause of high target latency is the resource bottlenecks in the replication instance. The replication instance is the compute resource that runs the AWS DMS task and connects to the source and target endpoints. If the

  replication instance is under- provisioned or overloaded, it can affect the replication performance and cause high target latency. Some factors that can contribute to resource bottlenecks are insufficient network bandwidth, low disk space, high

  CPU utilization, or large transaction sizes3.

• Question 129:

  A company is concerned about the cost of a large-scale, transactional application using Amazon DynamoDB that only needs to store data for 2 days before it is deleted. In looking at the tables, a Database Specialist notices that much of the data is months old, and goes back to when the application was first deployed.

  What can the Database Specialist do to reduce the overall cost?

  A. Create a new attribute in each table to track the expiration time and create an AWS Glue transformation to delete entries more than 2 days old.
  B. Create a new attribute in each table to track the expiration time and enable DynamoDB Streams on each table.
  C. Create a new attribute in each table to track the expiration time and enable time to live (TTL) on each table.
  D. Create an Amazon CloudWatch Events event to export the data to Amazon S3 daily using AWS Data Pipeline and then truncate the Amazon DynamoDB table.
  C. Create a new attribute in each table to track the expiration time and enable time to live (TTL) on each table.

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  Explanation/Reference:

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

• Question 130:

  An Amazon RDS EBS-optimized instance with Provisioned IOPS (PIOPS) storage is using less than half of its allocated IOPS over the course of several hours under constant load. The RDS instance exhibits multi-second read and write latency, and uses all of its maximum bandwidth for read throughput, yet the instance uses less than half of its CPU and RAM resources.

  What should a Database Specialist do in this situation to increase performance and return latency to sub- second levels?

  A. Increase the size of the DB instance storage
  B. Change the underlying EBS storage type to General Purpose SSD (gp2)
  C. Disable EBS optimization on the DB instance
  D. Change the DB instance to an instance class with a higher maximum bandwidth
  D. Change the DB instance to an instance class with a higher maximum bandwidth

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  Explanation/Reference:

  https://docs.amazonaws.cn/en_us/AmazonRDS/latest/UserGuide/CHAP_BestPractices.ht ml