DBS-C01 Exam Dumps - AWS Certified Database - Specialty

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.