DBS-C01 Exam Dumps - AWS Certified Database - Specialty

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

AWS CloudFormation is a service that helps you model and set up your AWS resources so that you can spend less time managing those resources and more time focusing on your applications that run in AWS. You create a template that describes all the AWS resources that you want (like Amazon RDS instances), and CloudFormation takes care of provisioning and configuring those resources for you1.

Using AWS CloudFormation to create RDS resources and share the template with each account is a suitable solution for the company’s requirements, as it can:

• Automate the deployment of RDS instances with specific compliance parameters, such as security groups, encryption, backup settings, etc.
• Reduce the operational overhead and human errors of manually creating RDS instances in each account.
• Enable the company to create the databases within one day, as CloudFormation can provision resources in parallel and in a consistent manner.

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.CreateReplicationInstance.html

https://aws.amazon.com/about-aws/whats-new/2018/11/amazon-aurora-simplifies-workload-management-with-custom-endpoints/

You can now create custom endpoints for Amazon Aurora databases. This allows you to distribute and load balance workloads across different sets of database instances in your Aurora cluster. For example, you may provision a set of Aurora Replicas to use an instance type with higher memory capacity in order to run an analytics workload. A custom endpoint can then help you route the analytics workload to these appropriately-configured instances, while keeping other instances in your cluster isolated from this workload. As you add or remove instances from the custom endpoint to match your workload, the endpoint helps spread the load around.

The best combination of actions to meet the company’s requirements are:

• A. Establish an AWS Direct Connect hosted connection between the company’s data center and AWS. This will provide a secure and high-bandwidth connection for the Always On data replication and minimize the network latency and data loss.
• D. Deploy a new SQL Server Always On availability group DB cluster on Amazon EC2. Configure Always On distributed availability groups between the on-premises DB cluster and the AWS DB cluster. Fail over to the AWS DB cluster when it is time to migrate. This will allow the company to use the same SQL Server version and edition as on-premises, and leverage the distributed availability group feature to span two separate availability groups across different locations. The failover process will be fast and seamless, with minimal downtime and data loss.
• F. Configure the third-party backup product to perform backups of the DB cluster on Amazon EC2. This will enable the company to continue using their existing backup solution, which requires system-level access to the databases. Amazon RDS for SQL Server does not support system-level access, so it is not a suitable option for this requirement.

https://aws.amazon.com/about-aws/whats-new/2022/08/amazon-rds-mysql-supports-ssl-tls-connections/

• Option A is correct because it sets the audit_logs cluster parameter to enabled. This enables auditing on the DocumentDB cluster, which records events that were performed in the cluster, such as successful and failed authentication attempts, dropping a collection in a database, or creating an index1. By enabling auditing, the database specialist can review the logs to see who and when deleted the primary DocumentDB database, and what other actions were taken on the cluster.
• Option B is correct because it enables DocumentDB log export to Amazon CloudWatch Logs. This allows the DocumentDB cluster to export its auditing records (JSON documents) to Amazon CloudWatch Logs, where they can be analyzed, monitored, and archived1. By enabling log export, the database specialist can access the logs even if the primary DocumentDB database is deleted, as they are stored in a separate service.

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.a ws.amazon.com/AmazonRDS/latest/UserGuide/USER_CreateSnapshot.html

Aurora Global Databases provides a writer and a reader endpoints in the primary region but only a reader endpoints in other region. Although strongly consistent, it does not fulfill the requirements that "there are plenty of read / write activities" in all regions.

On-demand mode is a good option if any of the following are true: You create new tables with unknown workloads. You have unpredictable application traffic. You prefer the ease of paying for only what you use. https://docs.aws.amazon.com/amazondynamodb/ latest/developerguide/HowItWorks.ReadWriteCapacityMode.html#HowItWorks.ProvisionedThroughput.Manual

Amazon DynamoDB auto scaling uses the AWS Application Auto Scaling service to dynamically adjust provisioned throughput capacity on your behalf https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/AutoScaling.html

A - https://aws.amazon.com/about-aws/whats-new/2018/09/amazon-rds-now-provides-database-deletion-protection/

D - https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_WorkingWithAutomatedBackups.html

F - https://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-attribute-deletionpolicy.html

RDS event subscriptions do not cover "data is inserted into a table" - see https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/USER_Events.Messages.html We can use stored procedure to invoke Lambda function - https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraMySQL.Integrating.Lambda.html

The combination of changes that the database administrator must make to the database deployment to automate the collection of performance data are:

• Update the CloudFormation template to enable Amazon CloudWatch monitoring on the Aurora PostgreSQL DB instances. This will allow the database administrator to monitor the CPU utilization, memory usage, disk I/O, network throughput, and other metrics of the DB instances. CloudWatch monitoring is enabled by default for Aurora DB instances, but it can also be specified in the CloudFormation template using the MonitoringInterval property of the AWS::RDS::DBInstance resource1.
• Update the CloudFormation template to turn on Performance Insights for Aurora PostgreSQL. This will allow the database administrator to analyze the database load and identify the most resource-intensive SQL queries, hosts, or users. Performance Insights is a feature of Amazon RDS that provides an easy-to-use dashboard to visualize and troubleshoot database performance. Performance Insights can be enabled in the CloudFormation template using the PerformanceInsightsEnabled property of the AWS::RDS::DBInstance resource2.

The other options are not relevant or sufficient for the requirement of automating the collection of performance data for the Aurora PostgreSQL databases. For example:

• Turning on Amazon DevOps Guru for the Aurora database resources in the CloudFormation template is not necessary, as DevOps Guru is a service that uses machine learning to identify operational issues and risks across applications and infrastructure. DevOps Guru does not provide detailed performance data or analysis for Aurora PostgreSQL databases3.
• Turning on AWS CloudTrail in each AWS account is not sufficient, as CloudTrail is a service that records API calls and events for AWS services. CloudTrail does not provide performance data or analysis for Aurora PostgreSQL databases, although it can help with auditing and compliance4.
• Turning on and configuring AWS Config for all Aurora PostgreSQL databases is not relevant, as AWS Config is a service that tracks and evaluates the configuration changes of AWS resources. AWS Config does not provide performance data or analysis for Aurora PostgreSQL databases, although it can help with governance and security5.

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_PerfInsights.Enabling.html

https://aws.amazon.com/rds/performance-insights/

https://aws.amazon.com/blogs/database/tuning-amazon-rds-for-mysql-with-performance-insights/

Correct Answer: A

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.

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

When you perform a restore operation to a point in time or from a DB Snapshot, a new DB Instance is created with a new endpoint (the old DB Instance can be deleted if so desired). This is done to enable you to create multiple DB Instances from a specific DB Snapshot or point in time."

Correct Answer: A, C, E

Explanation from Amazon documents:

To enable CDC mode for RDS for PostgreSQL database, the database architect needs to configure the following parameters12:

• Set wal_level = logical. This parameter determines how much information is written to the write-ahead log (WAL). For CDC mode, the wal_level must be set to logical, which enables logical decoding of the WAL and allows AWS DMS to read changes from the source database1.
• Set max_replication_slots to 1 or more, depending on the number of DMS tasks. This parameter specifies the maximum number of replication slots that the source database can support. A replication slot is a data structure that records the state of a replication stream. AWS DMS uses replication slots to set up logical replication and track changes in the source database. The max_replication_slots parameter must be equal to or greater than the number of DMS tasks that use CDC mode for the source database1.
• Set wal_sender_timeout to 20,000 milliseconds. This parameter specifies the amount of time that a WAL sender process waits for feedback from a WAL receiver process before terminating the connection. A WAL sender process is a background process that streams WAL data from the source database to AWS DMS. A WAL receiver process is a background process that receives WAL data from a WAL sender process and writes it to a local file. The wal_sender_timeout parameter must be set to a value greater than 10,000 milliseconds (10 seconds) to prevent connection timeouts during CDC mode2.

Therefore, option A, C, and E are the correct parameters to support CDC mode for RDS for PostgreSQL database. Option B is incorrect because wal_level = replica is not sufficient for logical decoding and CDC mode. Option D is incorrect because max_replication_slots must be a positive integer, not zero. Option F is incorrect because wal_sender_timeout = 5,000 milliseconds is too low and may cause connection timeouts during CDC mode.

"AWS Lambda function to capture changes" capture changes to what? ElastiCache? The main use of ElastiCache is to cache frequently read data. Also "the company expects a tenfold increase in the user base" and "correspond to simultaneous requests from users"

https://aws.amazon.com/premiumsupport/knowledge-center/back-up-dynamodb-s3/

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."

https://aws.amazon.com/blogs/database/configuring-amazon-elasticache-for-redis-for-higher-availability/

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

Correct Answer: A

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.

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.

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_WorkingWithAutomatedB ackups.html

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/CreateBackup.html#:~:text=If%20you%20don%27t%20want%20to%20create%20schedulin g%20scripts%20and%20cleanup%20jobs%2C%20you%20can%20use%20AWS%20Backup%20to%20create%20backup%20plans%20with%20schedules%20and%20retention%20policies%20for%20your%20DynamoDB%20tables.%20AWS%20Backup%20runs%20the%20backups%20and%20deletes%20them%20when%20they%20expire.%20For%20more%20information%2C%20see%20the%20AWS%20Backup%20Developer%20Guide.

https://docs.aws.amazon.com/amazondynamodb/latest/dev eloperguide/backuprestore_HowItWorks.html

Amazon DevOps Guru is a service that helps you identify and troubleshoot performance issues and operational risks in your AWS applications. DevOps Guru uses machine learning to analyze data from various sources, such as Amazon CloudWatch metrics, AWS CloudTrail events, and Amazon RDS performance events, to detect anomalous behavior and generate insights. Insights provide a summary of the issue, the affected resources, the severity, the start and end time, and recommendations for remediation. DevOps Guru can also send notifications to Amazon Simple Notification Service (SNS) topics or AWS Chatbot channels when insights are created or updated.

Using DevOps Guru insights is a suitable solution for the social media company because it can help them identify the source of the high CPU utilization and high database latency in their Amazon RDS for MySQL DB instance with minimal administrative oversight. DevOps Guru can automatically monitor their application and generate insights when it detects any operational issues or risks. The company can then use the recommendations provided by DevOps Guru to resolve the issue and improve their application performance.

Correct Answer: C. Create a new DAX cluster using a larger node type. Change the DAX endpoint in the application to point to the new cluster.

Explanation from Amazon documents:

The cache miss rate is the percentage of read requests that are not satisfied by the DAX cache and have to be forwarded to DynamoDB1. A high cache miss rate indicates that the DAX cluster does not have enough memory to store all the frequently accessed items. Increasing the number of nodes in the existing DAX cluster (option A) or creating a new DAX cluster with more nodes (option B) will not increase the total memory available for caching, because DAX uses a partitioned cache model, where each node is responsible for caching a subset of the data2. Modifying the node type in the existing DAX cluster (option D) will cause downtime and data loss, because DAX does not support online resizing of clusters3. Therefore, the best option is to create a new DAX cluster using a larger node type (option C), which will provide more memory per node and allow more items to be cached. The application will need to change the DAX endpoint to point to the new cluster, which can be done with minimal disruption by using DNS aliasing or load balancing3.

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.

https://aws.amazon.com/blogs/mt/integrating-aws-cloudformation-with-aws-systems-manager-parameter-store/

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.

Correct Answer: B

Explanation from Amazon documents:

Amazon ElastiCache for Redis is a fully managed in-memory data store that supports Redis, an open source, key-value database. Amazon ElastiCache for Redis provides several features to enhance the performance, availability, scalability, and security of your Redis data, such as cluster mode, global datastore, replication groups, snapshots, and encryption.

A global datastore is a feature that allows you to create a cross-Region read replica of your ElastiCache for Redis cluster. A global datastore consists of a primary cluster that is replicated across up to two other Regions as secondary clusters. A global datastore provides low-latency reads and high availability for your Redis data across Regions. A global datastore also supports encryption of cross-Region data transfers using AWS Key Management Service (AWS KMS).

To create a global datastore in ElastiCache for Redis, you need to do the following:

• Create a primary cluster in one Region. You can use an existing cluster or create a new one. The cluster must have cluster mode enabled and use Redis engine version 5.0.6 or later.
• Create a global datastore and add the primary cluster to it. You can use the AWS Management Console, the AWS CLI, or the ElastiCache API to create a global datastore.
• Create one or two secondary clusters in other Regions and add them to the global datastore. The secondary clusters must have the same specifications as the primary cluster, such as node type, number of shards, and number of replicas per shard.
• Enable encryption in transit and at rest for the primary and secondary clusters. Specify a customer master key (CMK) from AWS KMS for each cluster.

By creating a global datastore in ElastiCache for Redis and creating replica clusters in two other Regions, the database administrator can develop a caching solution that will be available across Regions and include low-latency replication and failover capabilities for DR. This solution will also meet the security requirement of encrypting cross-Region data transfers using AWS KMS. This solution will also require the least amount of operational effort, as it does not involve any data migration or manual intervention.

Therefore, option B is the correct solution to meet the requirements. Option A is not optimal because enabling cluster mode in ElastiCache for Redis and creating multiple clusters across Regions will not provide cross-Region replication or failover capabilities. Using AWS DMS to replicate the cache data by using AWS DMS will incur additional time and cost, and may not support encryption of cross-Region data transfers. Option C is not optimal because disabling cluster mode in ElastiCache for Redis and creating multiple replication groups across Regions will not provide cross-Region replication or failover capabilities. Using AWS DMS to replicate the cache data will incur additional time and cost, and may not support encryption of cross-Region data transfers. Option D is not optimal because creating a snapshot of ElastiCache for Redis in the primary Region and copying it to the failover Region will not provide low-latency replication or high availability for the Redis data across Regions. Using the snapshot to restore the cluster from the failover Region when DR is required will involve manual intervention and downtime.

https://docs .aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/aurora-global-database-disaster-recovery.html

https://aws.amazon.c om/blogs/database/how-to-choose-the-best-disaster-recovery-option-for-your-amazon-aurora-mysql-cluster/

https://aws.amazon.com/about -aws/whats-new/2019/11/aurora-supports-in-place-conversion-to-global-database/

Correct Answer: A

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.

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

With Amazon RDS, you can create a MariaDB, MySQL, Oracle, or PostgreSQL read replica in a different AWS Region from the source DB instance. Creating a cross-Region read replica isn't supported for SQL Server on Amazon RDS.

https://docs.aws.amazon.com/qldb/latest/developerguide/vpc-endpoints.html

"However, because you can encrypt a copy of an unencrypted DB snapshot, you can effectively add encryption to an unencrypted DB instance. That is, you can create a snapshot of your DB instance, and then create an encrypted copy of that snapshot. You can then restore a DB instance from the encrypted snapshot, and thus you have an encrypted copy of your original DB instance. For more information, see Copying a Snapshot." https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Overview.Encryption.html

The number of orders and exact schedule for the sale will vary each day. During the sale, approximately 10,000 concurrent users will look at the deals before buying items. Outside of the sale, the traffic volume is very low ==> Setting provisioning DynamoDB fix read 5000/write 10000 with will waste the resource when the traffic is low. It is not cost-effective.

You can restore a DB instance to a specific point in time (PITR), creating a new DB instance. To support PITR, your DB instances must have backup retention set to a nonzero value. https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/custom-backup-sqlserver.html

https://aws.amazon.com/blogs/database/setting-up-a-binlog-server-for-amazon-rds-mysql-and-mariadb-using-mariadb-maxscale/

"After you run the command, it’s okay to enable backup retention on the RDS instance by using the AWS CLI or the console. Enabling backup retention also enables binary logging."

https://aws.amazon.com/blogs/storage/point-in-time-recovery-and-continuous-backup-for-amazon-rds-with-aws-backup/

PostgreSQL: sslrootcert=rds-cert.pem sslmode=[verify-ca | verify-full]

https://aws.amazon.com/blogs/database/best-practices-for-migrating-rds-for-mysql-databases-to-amazon-aurora/

https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraPostgreSQL.Migrating.html#AuroraPostgreSQL.Migrating.RDSPostgreSQL.Replica

https://docs.aws.amazon.com/AmazonCloudWatch/latest/events/Create-CloudWatch-Events-Scheduled-Rule.html

https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/schedule-jobs-for-amazon-rds-and-aurora-postgresql-using-lambda-and-secrets-manager.html

a job for data extraction or a job for data purging can easily be scheduled using cron. For these jobs, database credentials are typically either hard-coded or stored in a properties file. However, when you migrate to Amazon Relational Database Service (Amazon RDS) or Amazon Aurora PostgreSQL, you lose the ability to log in to the host instance to schedule cron jobs. This pattern describes how to use AWS Lambda and AWS Secrets Manager to schedule jobs for Amazon RDS and Aurora PostgreSQL databases after migration.

https://docs.aws.amazon.com/AmazonCloudWatch/latest/events/RunLambdaSchedule.html

https://aws.amazon.com/dynamodb/dax/#:~:text=Amazon%20DynamoDB%20Accelerator%20(DAX)%20is,millions%20of%20requests%20per%20second. "Amazon DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10x performance improvement – from milliseconds to microseconds – even at millions of requests per second. "

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/SQLServer.Concepts.General.SSL.Using.html

https://aws.amazon.com/premiumsupport/knowledge-center/rds-cannot-connect/

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

"Amazon Aurora, a fully-managed relational database service in AWS, is now offering a backtrack feature. With Amazon Aurora with MySQL compatibility, users can backtrack, or "rewind", a database cluster to a specific point in time, without restoring data from a backup. The backtrack process allows a point in time to be specified with one second resolution, and the rewind process typically takes minutes. This new feature facilitates developers in undoing mistakes like deleting data inappropriately or dropping the wrong table."

https://aws.amazon.com/premiumsupport/knowledge-center/rds-required-maintenance/

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/Scan.html#Scan.ParallelScan

AWS CloudWatch Logs are stored indefinitely and CloudWatch Log Insights is used to analyze the logs and query upon them.

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/AnalyzingLogData.html

https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/WhatIsCloudWatchLogs.html

"Log retention – By default, logs are kept indefinitely and never expire. You can adjust the retention policy for each log group, keeping the indefinite retention, or choosing a retention period between 10 years and one day."

https://docs.aws.amazon.com/redshift/latest/mgmt/db-auditing.html

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

• Option C is correct because it solves the throttling issue without requiring changes to the app. On-demand capacity mode is a flexible billing option for DynamoDB that automatically accommodates your workload as it ramps up or down. With on-demand mode, you pay per request for the data reads and writes your application performs on your tables, and you don’t need to specify how much read and write throughput you expect. On-demand mode can handle sudden increases and decreases in activity without throttling, as long as the request rate does not exceed the default table quotas1. To use on-demand mode, you only need to update the table settings in the AWS Management Console or by using the AWS SDK2. You don’t need to modify any application logic, as on-demand mode is compatible with existing DynamoDB API calls.

https://aws.amazon.com/premiumsupport/knowledge-center/rds-snapshots-share-account/

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_WorkingWithParamGroups.html#USER_WorkingWithParamGroups.Modifying

Known patterns, minimum maintenance, miliseconds latency

https://docs.aws.amazon.com/prescriptive-guidance/latest/migration-sql-server/ec2-fci.html

An FCI is generally preferable over an Always on availability group when: You’re using SQL Server Standard edition instead of Enterprise edition.

https://aws.amazon.com/elasticache/redis-vs-memcached/

https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/elasticache-use-cases.html#elasticache-for-redis-use-cases-gaming

https://aws.amazon.com/blogs/database/migrate-tde-enabled-sql-server-databases-to-amazon-rds-for-sql-server/

https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/encryption.html

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

https://aws.amazon.com/premiumsupport/knowledge-center/dynamodb-table-throttled/

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected data sets1. Graph databases are designed to store and query data that has complex relationships and interconnections, such as social networks, recommendation engines, fraud detection, and knowledge graphs2. Amazon Neptune supports two popular graph models: Property Graph and Resource Description Framework (RDF), and their respective query languages: Apache TinkerPop Gremlin and SPARQL2.

Amazon Neptune also supports IAM policies to control access to the database resources and operations. You can use IAM database authentication to authenticate users and applications that connect to a Neptune DB cluster. IAM database authentication works with MySQL and PostgreSQL database clients. You can also use IAM roles to manage access to Neptune from other AWS services, such as Amazon EC2, AWS Lambda, and Amazon SageMaker2.

Therefore, Amazon Neptune DB cluster is a suitable solution for the marketing company’s requirements, as it can provide a graph database storage solution that is optimized for highly connected data and can limit connections and programmatic access by using IAM policies.

https://aws.amazon.com/blogs/database/using-iam-multifactor-authentication-with-amazon-rds/

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/security_iam_id-based-policy-htmlhttps://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/DataDurability.html

https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/vpc-endpoints-dynamodb.html

Correct Answer: A

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.

https://docs.aws.amazon.com/amazondynamodb/latest/d eveloperguide/vpc-endpoints-dynamodb.html

https://aws.amazon.com/premiumsupport/knowledge-center/rds-read-replica/

https://aws.amazon.com/about-aws/whats-new/2020/02/amazon-rds-data-api-now-supports-aws-privatelink/

https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraMySQL.Managing.FaultInjectionQueries.html

"You can test the fault tolerance of your Amazon Aurora DB cluster by using fault injection queries. Fault injection queries are issued as SQL commands to an Amazon Aurora instance and they enable you to schedule a simulated occurrence of one of the following events: A crash of a writer or reader DB instance A failure of an Aurora Replica A disk failure Disk congestion When a fault injection query specifies a crash, it forces a crash of the Aurora DB instance. The other fault injection queries result in simulations of failure events, but don't cause the event to occur. When you submit a fault injection query, you also specify an amount of time for the failure event simulation to occur for."

Correct Answer: B, D, E

Explanation from Amazon documents:

To share an encrypted Amazon RDS snapshot with another account, you need to do the following123:

• Create a manual snapshot of the production database. You can’t share an automated snapshot directly, but you can copy it to a manual snapshot and then share it1.
• Use a custom KMS encryption key for the manual snapshot. You can’t share a snapshot that is encrypted using the default KMS key of the source account1.
• Share the snapshot with the development account by specifying the account ID of the target account1.
• Allow the development account to access the custom KMS key of the source account by adding the target account ID to the key policy of the source account2.
• Copy the shared snapshot to the development account by using a KMS key of the target account2.

Therefore, option B, D, and E are the correct steps to meet the requirements. Option A is incorrect because you can’t share an automated snapshot. Option C is incorrect because you can’t share a snapshot that is encrypted using the default KMS key. Option F is unnecessary because the production account does not need to access the development account KMS key.

Correct Answer: B

Explanation from Amazon documents:

Amazon Quantum Ledger Database (Amazon QLDB) is a fully managed ledger database that provides a transparent, immutable, and cryptographically verifiable transaction log of all your application changes. Amazon QLDB tracks each and every application data change and maintains a complete and verifiable history of changes over time1. This makes it ideal for storing historical records that need to be tamper-proof and auditable. Amazon QLDB also supports PartiQL, a SQL-compatible query language that lets you query data using familiar SQL operators2.

Amazon OpenSearch Service (successor to Amazon Elasticsearch Service) is a fully managed service that makes it easy to deploy, operate, and scale OpenSearch, an open source search and analytics engine. Amazon OpenSearch Service lets you store, search, and analyze large volumes of data quickly and at low cost3. You can use Amazon OpenSearch Service to index and query your employee record history data stored in Amazon QLDB using the QLDB Streams feature. This way, you can leverage the powerful search and analytics capabilities of OpenSearch Service to run fast and flexible queries on your historical data.

Therefore, option B is the best solution that meets the requirements of preventing modification of the historical records and maximizing the speed of the queries. Option A is not suitable because DynamoDB is a key-value and document database that does not provide a ledger-like transaction log. Option C is not suitable because Aurora PostgreSQL is a relational database that does not guarantee immutability of the historical records. Option D is not suitable because Redshift is a data warehouse that is optimized for analytical queries on large datasets, not for storing and querying individual records.

https://aws.amazon.com/about-aws/whats-new/2019/05/amazon-aurora-with-postgresql-compatibility-supports-database-activity-streams/

"Database Activity Streams for Amazon Aurora with PostgreSQL compatibility provides a near real-time data stream of the database activity in your relational database to help you monitor activity. When integrated with third party database activity monitoring tools, Database Activity Streams can monitor and audit database activity to provide safeguards for your database and help meet compliance and regulatory requirements."

https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Overview.LoggingAndMonitoring.html

https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraMySQL.Auditing.html

https://docs.aws.amazon.com/AmazonElastiCache/latest/red-ug/AutoFailover.html

You can enable Multi-AZ only on Redis (cluster mode disabled) clusters that have at least one available read replica. Clusters without read replicas do not provide high availability or fault tolerance.

https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Overview.Encryption.html#Overview.Encryption.Limitations

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

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