https://aws.amazon.com/blogs/database/cross-region-disaster-recovery-of-amazon-rds-for-sqlserver/

https://aws.amazon.com/premiumsupport/knowledge-center/rds-db-storage-size/

Use AWS Database Migration Service (AWS DMS) for minimal downtime.

:

:

Correct Answer B

Explanation:

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://docs.aws.amazon.com/amazondynamodb/latest/developerguide/bp-partition-keydesign.html#bp-partition-key-throughput-bursting

"DynamoDB provides some flexibility in your per-partition throughput provisioning by providing

burst capacity. Whenever you're not fully using a partition's throughput, DynamoDB reserves a

portion of that unused capacity for later bursts of throughput to handle usage spikes. DynamoDB

currently retains up to 5 minutes (300 seconds) of unused read and write capacity. During an

occasional burst of read or write activity, these extra capacity units can be consumed quickly—even

faster than the per-second provisioned throughput capacity that you've defined for your table.

DynamoDB can also consume burst capacity for background maintenance and other tasks without

prior notice. Note that these burst capacity details might change in the future."

https://aws.amazon.com/premiumsupport/knowledge-center/dynamodb-cross-account-migration/

https://aws.amazon.com/premiumsupport/knowledge-center/data-pipeline-account-accessdynamodb-s3/

"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"

Correct Answer :- B and D

Explanation:

Explanation 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.

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."