Amazon Web Services pass4future aws Certified Professional Sap-c02 New Questions by Blanka q253 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:	645 Q&A's	Shared By:	blanka

Question 160

A company needs to run large batch-processing jobs on data that is stored in an Amazon S3 bucket. The jobs perform simul-ations. The results of the jobs are not time sensitive, and the process can withstand interruptions.

Each job must process 15-20 GB of data when the data is stored in the S3 bucket. The company will store the output from the jobs in a different Amazon S3 bucket for further analysis.

Which solution will meet these requirements MOST cost-effectively?

Options:

Create a serverless data pipeline. Use AWS Step Functions for orchestration. Use AWS Lambda functions with provisioned capacity to process the data.

Create an AWS Batch compute environment that includes Amazon EC2 Spot Instances. Specify the SPOT_CAPACITY_OPTIMIZED allocation strategy.

Create an AWS Batch compute environment that includes Amazon EC2 On-Demand Instances and Spot Instances. Specify the SPOT_CAPACITY_OPTIMIZED allocation strategy for the Spot Instances.

Use Amazon EKS to run the processing jobs. Use managed node groups that contain a combination of Amazon EC2 On-Demand Instances and Spot Instances.

Discussion

Answer:

Explanation:

The workload consists of large batch-processing simulation jobs that read 15–20 GB per job from Amazon S3, write results back to S3, are not time sensitive, and can tolerate interruptions. These characteristics are ideal for using EC2 Spot Instances, which provide steep discounts compared to On-Demand pricing in exchange for potential interruptions.

AWS Batch is designed specifically for batch and simulation workloads. It can dynamically provision compute resources, manage job queues, retry failed jobs, and integrate tightly with EC2 Spot Instances. Using an AWS Batch compute environment that is configured to use EC2 Spot Instances with the SPOT_CAPACITY_OPTIMIZED allocation strategy allows AWS Batch to choose the lowest interruption-risk Spot capacity pools automatically while still giving the largest cost savings.

Option B leverages these capabilities. It creates an AWS Batch compute environment using only Spot Instances and uses the SPOT_CAPACITY_OPTIMIZED allocation strategy to select capacity across instance types with lower interruption rates. Since the simulations are not time sensitive and can withstand interruptions, the risk of Spot interruptions is acceptable, and AWS Batch will re-run interrupted jobs as needed. This combination provides the most cost-effective solution while minimizing operational overhead for job scheduling and capacity management.

Option A uses Lambda with Step Functions. Lambda has limits on memory, runtime, and payload size, making it a poor fit for processing 15–20 GB of data per job. In addition, the concurrency and invocation cost for large-scale, long-running simulation workloads would be higher and less cost-effective than EC2 Spot Instances.

Option C mixes On-Demand and Spot Instances. While this can provide reliability for more time-critical workloads, it is less cost-effective than a pure Spot configuration when the workload explicitly can tolerate interruptions and is not time sensitive.

Option D uses Amazon EKS with a mix of On-Demand and Spot Instances. While this can be made to work, it requires managing Kubernetes clusters, node groups, and job scheduling, which introduces more operational complexity than using AWS Batch, and the inclusion of On-Demand capacity reduces cost-effectiveness compared to pure Spot-based batch processing.

Therefore, using AWS Batch with EC2 Spot Instances and the SPOT_CAPACITY_OPTIMIZED allocation strategy (option B) is the most cost-effective solution for this tolerant, batch-processing workload.

[References:AWS documentation on AWS Batch for running batch and simulation jobs.AWS guidance on using EC2 Spot Instances and the SPOT_CAPACITY_OPTIMIZED allocation strategy for cost-effective, interruption-tolerant workloads., , ]

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Question 161

A company is providing weather data over a REST-based API to several customers. The API is hosted by Amazon API Gateway and is integrated with different AWS Lambda functions for each API operation. The company uses Amazon Route 53 for DNS and has created a resource record of weather.example.com. The company stores data for the API in Amazon DynamoDB tables. The company needs a solution that will give the API the ability to fail over to a different AWS Region.

Which solution will meet these requirements?

Options:

Deploy a new set of Lambda functions in a new Region. Update the API Gateway API to use an edge-optimized API endpoint with Lambda functions from both Regions as targets. Convert the DynamoDB tables to global tables.

Deploy a new API Gateway API and Lambda functions in another Region. Change the Route 53 DNS record to a multivalue answer. Add both API Gateway APIs to the answer. Enable target health monitoring. Convert the DynamoDB tables to global tables.

Deploy a new API Gateway API and Lambda functions in another Region. Change the Route 53 DNS record to a failover record. Enable target health monitoring. Convert the DynamoDB tables to global tables.

Deploy a new API Gateway API in a new Region. Change the Lambda functions to global functions. Change the Route 53 DNS record to a multivalue answer. Add both API Gateway APIs to the answer. Enable target health monitoring. Convert the DynamoDB tables to global tables.

Discussion

Question 162

A company hosts a game player-matching service on a public-facing, physical, on-premises instance that all users are able to access over the instance uses UDP. The company wants to migrate the service to AWS and provide a high level of security. A solutions architect needs to de matching service using AWS.

Which combination of steps should the solutions architect take to meet these requirements? (Select THREE.)

Options:

Use a Network Load Balancer (NLB) in front of the player-matching instance. Use a friendly DNS entry in Amazon Route 53-point address.

Use an Application Load Balancer (ALB) in front of the player-matching instance. Use a friendly DNS entry in Amazon Route 53 p facing fully qualified domain name (FQDN).

Define an AWS WAF rule to explicitly drop non-UDP traffic, and associate the rule with the load balancer.

Configure a network ACL rule to block all non-UDP traffic. Associate the network ACL with the subnets that hold the load balance

Discussion

Question 163

A company needs a hybrid DNS architecture. The architecture must include the company’s on-premises network and a VPC. An AWS Site-to-Site VPN connection connects the VPC to the on-premises network. The company already hosts the onprem.mydc.com domain name on premises.

The company wants to host the myvpc.example.com domain name in the company’s AWS account and resolve it to the VPC. The company also needs the on-premises devices to resolve DNS queries to the myvpc.example.com domain.

Which combination of steps will meet these requirements? Select THREE.

Options:

Create an Amazon Route 53 private hosted zone for the myvpc.example.com domain. Associate the domain with the VPC.

Create an Amazon Route 53 public hosted zone for the myvpc.example.com domain. Associate the domain with the VPC.

Use Amazon Route 53 Resolver to create an inbound endpoint in the AWS Region of the VPC.

Use Amazon Route 53 Resolver to create an outbound endpoint in the AWS Region of the VPC.

Use Amazon Route 53 Resolver to create a forwarding rule for the Route 53 private hosted zone domain and IP addresses of the outbound endpoint.

Configure the on-premises DNS resolvers with a conditional forwarding rule for DNS queries for the Amazon Route 53 private hosted zone domain and IP addresses of the inbound endpoint.