The engineering team at an online fashion retailer uses AWS Cloud to manage its technology infrastructure. The Amazon EC2 server fleet is behind an Application Load Balancer and the fleet strength is managed by an Auto Scaling group. Based on the historical data, the team is anticipating a huge traffic spike during the upcoming Thanksgiving sale.
As an AWS solutions architect, what feature of the Auto Scaling group would you leverage so that the potential surge in traffic can be preemptively addressed?
Auto Scaling group target tracking scaling policy
Auto Scaling group scheduled action
Auto Scaling group step scaling policy
Auto Scaling group lifecycle hook
The engineering team can create a scheduled action for the Auto Scaling group to pre-emptively provision additional instances for the sale duration. This makes sure that adequate instances are ready before the sale goes live. The scheduled action tells Amazon EC2 Auto Scaling to perform a scaling action at specified times. To create a scheduled scaling action, you specify the start time when the scaling action should take effect, and the new minimum, maximum, and desired sizes for the scaling action. At the specified time, Amazon EC2 Auto Scaling updates the group with the values for minimum, maximum, and desired size that are specified by the scaling action.
A financial services company runs its flagship web application on AWS. The application serves thousands of users during peak hours. The company needs a scalable near-real-time solution to share hundreds of thousands of financial transactions with multiple internal applications. The solution should also remove sensitive details from the transactions before storing the cleansed transactions in a document database for low-latency retrieval.
As an AWS Certified Solutions Architect Associate, which of the following would you recommend?
Persist the raw transactions into Amazon DynamoDB. Configure a rule in Amazon DynamoDB to update the transaction by removing sensitive data whenever any new raw transaction is written. Leverage Amazon DynamoDB Streams to share the transactions data with the internal applications
Batch process the raw transactions data into Amazon S3 flat files. Use S3 events to trigger an AWS Lambda function to remove sensitive data from the raw transactions in the flat file and then store the cleansed transactions in Amazon DynamoDB. Leverage DynamoDB Streams to share the transactions data with the internal applications
Feed the streaming transactions into Amazon Kinesis Data Streams. Leverage AWS Lambda integration to remove sensitive data from every transaction and then store the cleansed transactions in Amazon DynamoDB. The internal applications can consume the raw transactions off the Amazon Kinesis Data Stream
Feed the streaming transactions into Amazon Kinesis Data Firehose. Leverage AWS Lambda integration to remove sensitive data from every transaction and then store the cleansed transactions in Amazon DynamoDB. The internal applications can consume the raw transactions off the Amazon Kinesis Data Firehose
Cuando hay varios accesos a los datos data stream. Si solo llega a uno servicio data firehose.
A company wants to ensure high availability for its Amazon RDS database. The development team wants to opt for Multi-AZ deployment and they would like to understand what happens when the primary instance of the Multi-AZ configuration goes down.
As a Solutions Architect, which of the following will you identify as the outcome of the scenario?