Amazon Web Services passleader pdf Sap-c02 Study Guide by Alicia q42 vce pdf

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Exam Name:	AWS Certified Solutions Architect - Professional
Exam Code:	SAP-C02 Dumps
Vendor:	Amazon Web Services	Certification:	AWS Certified Professional
Questions:	614 Q&A's	Shared By:	alicia

Question 92

A company runs a latency-sensitive application that consumes messages from an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. The MSK cluster runs across three Availability Zones.

The current MSK cluster uses Standard brokers with two standard large instances in each Availability Zone. The company wants to minimize latency between Apache Kafka clients that are deployed in the same Availability Zones as the brokers. The company wants to increase available bandwidth and to increase the scaling speed of the cluster. Clients currently use default settings. Some downtime is acceptable while the company implements a solution.

Which solution will meet these requirements?

Options:

Configure a predictive scaling policy and set the MSK cluster as the target. Set the target value to 80 and set the scheduling buffer size to 0. Configure a placement group for the Kafka clients and associate the MSK hosts with the placement group.

Configure Cruise Control on the MSK cluster and enable bandwidth control bandwidth and rebalancing. Deploy an Amazon MSK Connect proxy layer that uses latency-based routing. Reconfigure the Kafka clients to use the proxy endpoint.

Replace the Standard brokers with Express brokers that use express large instances. Set the client.rack property for the Kafka clients to az_id.

Resize the brokers to standard xlarge instances. Create MSK PrivateLink endpoints in each Availability Zone. Reconfigure each Kafka client to use the endpoint that is in the same Availability Zone as the client.

Discussion

Answer:

Explanation:

The company wants three things: minimize client-to-broker latency within the same Availability Zone, increase available bandwidth, and increase the scaling speed of the MSK cluster. The current brokers are Standard brokers (two per AZ). Clients use default settings, which means they are not explicitly configured for rack awareness or AZ affinity.

A common way to reduce latency in multi-AZ Kafka deployments is to enable rack awareness on clients and brokers so clients prefer brokers in the same “rack,” which can map to an Availability Zone. In Kafka, the client.rack setting allows the client to include rack information so the broker can return metadata that helps the client select replicas that are closest, reducing cross-AZ traffic and improving latency.

To increase bandwidth and improve scaling speed, the most direct approach in the choices is to move from Standard brokers to Express brokers. Express brokers are designed to provide higher throughput and faster scaling characteristics compared to standard broker types. Since the question explicitly calls out increasing available bandwidth and scaling speed, the broker type change is the key lever, and it can be combined with client.rack configuration to minimize cross-AZ latency.

Option C matches these requirements: it replaces Standard brokers with Express brokers (to improve throughput/bandwidth and scaling speed) and sets client.rack to the Availability Zone identifier (az_id) to improve locality and reduce latency between clients and brokers in the same AZ.

Option A is not appropriate because MSK does not use EC2 Auto Scaling predictive scaling in that manner, and Kafka clients/brokers are not “associated” with an EC2 placement group as a primary latency solution in MSK. Placement groups are for EC2 instance placement; MSK broker placement is managed by the service.

Option B introduces a proxy layer and MSK Connect in a way that increases complexity and does not directly guarantee lower latency or higher bandwidth. MSK Connect is for Kafka Connect workloads, not as a general-purpose low-latency routing proxy for Kafka clients. Cruise Control is used for partition rebalancing and cluster optimization, but it does not replace the benefits of higher-throughput broker types and client rack awareness for AZ locality.

Option D increases broker size and introduces PrivateLink endpoints. PrivateLink is about private connectivity from VPCs to services and does not inherently ensure AZ-local broker selection or reduce latency between clients and brokers in the same AZ. Also, resizing to xlarge increases capacity but does not address scaling speed and locality as directly as express brokers plus rack configuration.

Therefore, option C best meets all requirements.

[References:AWS documentation on Amazon MSK broker types, including performance and scaling characteristics of Standard and Express brokers.Apache Kafka concepts and AWS guidance on rack awareness and using client.rack to reduce cross-AZ traffic and latency in multi-AZ Kafka deployments., , ]

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Question 93

A company is running a web application in the AWS Cloud. The application consists of dynamic content that is created on a set of Amazon EC2 instances. The

EC2 instances run in an Auto Scaling group that is configured as a target group for an Application Load Balancer (ALB).

The company is using an Amazon CloudFront distribution to distribute the application globally. The CloudFront distribution uses the ALB as an origin. The company uses Amazon Route 53 for DNS and has created an A record of www.example.com for the CloudFront distribution.

A solutions architect must configure the application so that itis highly available and fault tolerant.

Which solution meets these requirements?

Options:

Provision a full, secondary application deployment in a different AWS Region. Update the Route 53 A record to be a failover record. Add both of the CloudFront distributions as values. Create Route 53 health checks.

Provision an ALB, an Auto Scaling group, and EC2 instances in a different AWS Region. Update the CloudFront distribution, and create a second origin for the new ALB. Create an origin group for the two origins. Configure one origin as primary and one origin as secondary.

Provision an Auto Scaling group and EC2 instances in a different AWS Region. Create a second target for the new Auto Scaling group in the ALB. Set up the failover routing algorithm on the ALB.

Provision a full, secondary application deployment in a different AWS Region. Create a second CloudFront distribution, and add the new application setup as an origin. Create an AWS Global Accelerator accelerator. Add both of the CloudFront distributions as endpoints.

Discussion

Question 94

A company recently deployed an application on AWS. The application uses Amazon DynamoDB.The company measured the application load and configured the RCUs and WCUs on the DynamoDB table to match the expected peak load. The peak load occurs once a week for a 4-hour period and is double the average load. The application load is close to the average load tor the rest of the week. The access pattern includes many more writes to the table than reads of the table.

A solutions architect needs to implement a solution to minimize the cost of the table.

Which solution will meet these requirements?

Options:

Use AWS Application Auto Scaling to increase capacity during the peak period. Purchase reserved RCUs and WCUs to match the average load.

Configure on-demand capacity mode for the table.

Configure DynamoDB Accelerator (DAX) in front of the table. Reduce the provisioned read capacity to match the new peak load on the table.

Configure DynamoDB Accelerator (DAX) in front of the table. Configure on-demand capacity mode for the table.

Discussion

Question 95

A company owns a chain of travel agencies and is running an application in the AWS Cloud. Company employees use the application to search for information about travel destinations. Destination content is updated four times each year.

Two fixed Amazon EC2 instances serve the application. The company uses an Amazon Route 53 public hosted zone with a multivalue record of travel.example.com that returns the Elastic IP addresses for the EC2 instances. The application uses Amazon DynamoDB as its primary data store. The company uses a self-hosted Redis instance as a caching solution.

During content updates, the load on the EC2 instances and the caching solution increases drastically. This increased load has led to downtime on several occasions. A solutions architect must update the application so that the application is highly available and can handle the load that is generated by the content updates.

Which solution will meet these requirements?

Options:

Set up DynamoDB Accelerator (DAX) as in-memory cache. Update the application to use DAX. Create an Auto Scaling group for the EC2 instances. Create an Application Load Balancer (ALB). Set the Auto Scaling group as a target for the ALB. Update the Route 53 record to use a simple routing policy that targets the ALB's DNS alias. Configure scheduled scaling for the EC2 instances before the content updates.

Set up Amazon ElastiCache for Redis. Update the application to use ElastiCache. Create an Auto Scaling group for the EC2 instances. Create an Amazon CloudFront distribution, and set the Auto Scaling group as an origin for the distribution. Update the Route 53 record to use a simple routing policy that targets the CloudFront distribution's DNS alias. Manually scale up EC2 instances before the content updates.

Set up Amazon ElastiCache for Memcached. Update the application to use ElastiCache Create an Auto Scaling group for the EC2 instances. Create an Application Load Balancer (ALB). Set the Auto Scaling group as a target for the ALB. Update the Route 53 record to use a simple routing policy that targets the ALB's DNS alias. Configure scheduled scaling for the application before the content updates.

Set up DynamoDB Accelerator (DAX) as in-memory cache. Update the application to use DAX. Create an Auto Scaling group for the EC2 instances. Create an Amazon CloudFront distribution, and set the Auto Scaling group as an origin for the distribution. Update the Route 53 record to use a simple routing policy that targets the CloudFront distribution's DNS alias. Manually scale up EC2 instances before the content updates.