Amazon DBS-C01 Practice Test - Questions Answers, Page 29

Explanation from Amazon documents:To allow an existing Redshift cluster to access data from other Redshift clusters, Amazon RDS for PostgreSQL databases, and AWS Glue Data Catalog tables, the database specialist should use the following features123:Create external tables in the existing Redshift database to connect to the AWS Glue Data Catalog tables. This feature allows you to query data stored in Amazon S3 using the AWS Glue Data Catalog as the metadata store. You can create external tables in your Redshift database that reference the data catalog tables and use SQL to query the data in S3. This feature is operationally efficient because it does not require moving or copying the data from S3 to Redshift1.Use federated queries to access data in Amazon RDS. This feature allows you to query and join data from one or more Amazon RDS for PostgreSQL and Amazon Aurora PostgreSQL databases with data already in your Amazon Redshift cluster. You can use SQL to query the RDS databases directly from your Redshift cluster without having to load or unload any data. This feature is operationally efficient because it reduces data movement and storage costs, and simplifies data access and analysis2.Use data sharing to access data from the other Redshift clusters. This feature allows you to securely share live data across different Redshift clusters without the complexity and delays associated with data copies and data movement. You can share data within or across AWS accounts using a consumer-producer model. The producer cluster grants privileges on one or more schemas, called datashares, to the consumer clusters. The consumer clusters can then query the shared data in the producer cluster as if it were local tables. This feature is operationally efficient because it enables real-time and transactionally consistent data access, and eliminates data duplication and stale data issues3.Therefore, option B, D, and E are the correct steps to meet the requirements with the most operational efficiency. Option A is not efficient because it involves taking and restoring snapshots, which can be time-consuming and costly. Option C is not efficient because it involves unloading and loading data between S3 and Redshift, which can also incur additional time and cost. Option F is not necessary because it involves transferring the AWS Glue Data Catalog tables into S3, which can be avoided by using external tables to connect to the data catalog tables directly.

Explanation 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.

Explanation from Amazon documents:Amazon Aurora PostgreSQL is a fully managed relational database service that is compatible with PostgreSQL. Aurora PostgreSQL offers up to three times better performance than standard PostgreSQL, as well as high availability, scalability, security, and durability. Aurora PostgreSQL also supports Babelfish, which is a new feature that enables Aurora to understand queries from applications written for Microsoft SQL Server. Babelfish allows you to migrate your SQL Server databases to Aurora PostgreSQL with minimal or no code changes, and run complex T-SQL queries and stored procedures on Aurora PostgreSQL.Migrating the database to Amazon Aurora PostgreSQL and turning on Babelfish will meet the requirements of minimizing database server maintenance and operating costs, and minimizing the need to rewrite code as part of the migration effort. This solution will allow the company to benefit from the performance, reliability, and cost-efficiency of Aurora PostgreSQL, while preserving the compatibility and functionality of SQL Server. The company will also avoid the hassle and expense of managing and licensing SQL Server on premises or on AWS.Therefore, option A is the correct solution to meet the requirements. Option B is not suitable because Amazon S3 is an object storage service that is not designed for OLTP workloads. Amazon Redshift Spectrum is a feature that allows you to query data in S3 using Amazon Redshift, but it is not compatible with SQL Server or T-SQL. Option C is not optimal because Amazon RDS for SQL Server is a managed relational database service that supports SQL Server, but it does not offer the same performance, scalability, or cost savings as Aurora PostgreSQL. Kerberos authentication is a security feature that does not affect the migration effort or the operating costs. Option D is not suitable because Amazon EMR is a big data processing service that runs Apache Hadoop and Spark clusters, not relational databases. EMR does not support SQL Server or T-SQL, and it is not optimized for OLTP workloads.

Explanation from Amazon documents:A CloudFormation stack policy is a JSON document that defines the update actions that can be performed on designated resources in a CloudFormation stack. A stack policy can be used to prevent accidental updates or deletions of stack resources, such as a production database.The Update:Replace action is an update action that replaces an existing resource with a new one during a stack update. This action can cause data loss or downtime for the resource. To prevent this action from affecting the production database resources, the database engineer should use a Deny statement for the Update:Replace action on the production database resources in the stack policy. This statement will override any Allow statements for the same action and resource, and protect the production database resources from being replaced during a stack update.Therefore, option D is the correct stack policy action to meet the requirements. Option A is incorrect because the Update:Modify action is not a valid update action for a stack policy. The valid update actions are Update:Replace, Update:Skip, and Delete. Option B is incorrect because it does not specify a valid update action for the Deny statement. Option C is incorrect because the Update:Delete action is not a valid update action for a stack policy. The valid update actions are Update:Replace, Update:Skip, and Delete.

Explanation from Amazon documents:Amazon RDS for Microsoft SQL Server supports two types of database snapshots: automated and manual. Automated snapshots are taken daily and are retained for a period of time that you specify, from 1 to 35 days. Manual snapshots are taken by you and are retained until you delete them.To save a particular automated database snapshot for longer than the maximum number of days, the database administrator can create a manual copy of the snapshot. This can be done using the AWS Management Console, the AWS CLI, or the RDS API. The manual copy of the snapshot will be retained until it is deleted, regardless of the retention period of the automated snapshot. This solution is the most operationally efficient way to meet the requirements, because it does not require any additional steps or resources.Therefore, option A is the correct solution to meet the requirements. Option B is not operationally efficient because it requires exporting the contents of the snapshot to an Amazon S3 bucket, which can be time-consuming and costly. Option C is not possible because the maximum retention period for automated snapshots is 35 days, not 45 days. Option D is not operationally efficient because it requires creating a native SQL Server backup and saving it to an Amazon S3 bucket, which can also be time-consuming and costly.

Explanation 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.

Explanation from Amazon documents:Amazon Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora MySQL that automatically starts up, shuts down, and scales capacity up or down based on your application's needs. Aurora Serverless is ideal for applications with unpredictable or intermittent traffic patterns that experience sudden spikes or drops in demand. Aurora Serverless also provides high availability and durability by replicating your data across multiple Availability Zones and continuously backing up your data to Amazon S3.Migrating the Aurora MySQL database to Amazon Aurora Serverless by restoring a snapshot will meet the requirements of implementing a scalable solution that is more resilient to database failures as quickly as possible. This solution will allow the company to benefit from the auto-scaling and high availability features of Aurora Serverless, which will handle the unpredictable surges in traffic and prevent connection issues. This solution will also be cost-effective, as the company will only pay for the database capacity that they use. The migration process will be simple and fast, as the company can use the AWS Management Console, the AWS CLI, or the RDS API to restore a snapshot of their existing Aurora MySQL database to an Aurora Serverless DB cluster, and then change the endpoint in the Lambda functions to use the new database.Therefore, option A is the correct solution to meet the requirements. Option B is not cost-effective because migrating the Aurora MySQL database to Amazon DynamoDB tables by using AWS DMS will incur additional time and cost, and may require significant code changes to adapt to a different data model and query language. Option C is not scalable because creating an EventBridge rule that invokes a Lambda function to end any connections in the sleep state will not address the root cause of the connection issues, which is the lack of database capacity to handle the traffic spikes. Option D is not scalable because increasing the instance class for the Aurora database with more memory and setting a larger value for the max_connections parameter will not provide auto-scaling or high availability, and may still result in connection issues if the traffic exceeds the provisioned capacity.

Explanation from Amazon documents:Amazon Aurora PostgreSQL supports cluster cache management, which is a feature that helps reduce the impact of failover on query performance by preserving the cache of the primary DB instance on one or more Aurora Replicas. Cluster cache management allows you to assign a promotion priority tier to each DB instance in your Aurora DB cluster. The promotion priority tier determines the order in which Aurora Replicas are considered for promotion to the primary instance after a failover. The lower the numerical value of the tier, the higher the priority.By enabling cluster cache management for the Aurora DB cluster and setting the promotion priority for the writer DB instance and replica to tier-0, the database specialist can minimize the performance degradation after failover. This solution will ensure that the primary DB instance and one Aurora Replica have the same cache contents and are in the same promotion priority tier. In the event of a failover, Aurora will promote the tier-0 replica to the primary role, and the cache will be preserved. This will reduce the number of cache misses and improve query performance after failover.Therefore, option A is the correct solution to minimize the performance degradation after failover. Option B is incorrect because setting the promotion priority for the writer DB instance and replica to tier-1 will not preserve the cache after failover. Aurora will first try to promote a tier-0 replica, which may have a different cache than the primary instance. Option C is incorrect because enabling Query Plan Management and performing a manual plan capture will not affect the cache behavior after failover. Query Plan Management is a feature that helps you control query execution plans and improve query performance by creating and enforcing custom execution plans. Option D is incorrect because enabling Query Plan Management and forcing the query optimizer to use the desired plan will not affect the cache behavior after failover. Forcing the query optimizer to use a desired plan may improve query performance by avoiding suboptimal plans, but it will not prevent cache misses after failover.