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| Section | Weight | Objectives |
|---|---|---|
| Cost Control | 18.5% | - Reserved capacity planning
- Resource efficiency
|
| Design for Organizational Complexity | 12.5% | - Networks
- Cross-account strategies and AWS organizations
|
| Design for New Solutions | 31% | - Cost optimization at design phase
|
| Continuous Improvement for Existing Solutions | 23% | - Reviewing existing architectures
|
| Migration Planning | 15% | - Validation and testing post-migration - Migration strategies
|
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NEW QUESTION # 486
A company wants to migrate its website to AWS. The website uses containers that are deployed in an on- premises, self-managed Kubernetes cluster. All data for the website is stored in an on-premises PostgreSQL database.
The company has decided to migrate the on-premises Kubernetes cluster to an Amazon EKS cluster. The EKS cluster will use EKS managed node groups with a static number of nodes. The company will also migrate the on-premises database to an Amazon RDS for PostgreSQL database.
A solutions architect needs to estimate the total cost of ownership (TCO) for this workload before the migration.
Which solution will provide the required TCO information?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The requirement is to estimate total cost of ownership before migration. This includes comparing current on- premises costs with projected AWS costs for compute, storage, databases, and related services, and producing a consolidated business-focused TCO view.
AWS Migration Evaluator is the AWS service specifically designed to help customers build data-driven business cases for migrating workloads to AWS. Migration Evaluator collects inventory and utilization data from on-premises environments by using a collector. It then allows architects to model different migration scenarios, map on-premises resources to AWS services such as Amazon EKS managed node groups and Amazon RDS for PostgreSQL, and generate reports that estimate costs, savings, and TCO impacts. The Quick Insights report provides a summarized, executive-level view of the estimated AWS costs, on-premises costs, and projected savings, which directly satisfies the requirement.
Option B is not sufficient because AWS DMS assessment reports focus on database migration feasibility and compatibility, not full workload TCO. The AWS Pricing Calculator can estimate AWS service costs, but it does not automatically incorporate on-premises cost data or provide a consolidated TCO comparison without significant manual effort.
Option C is incorrect because AWS Application Migration Service focuses on rehosting servers and testing migrations. It does not produce comprehensive TCO reports comparing on-premises and AWS environments for container platforms like EKS and managed databases.
Option D is incorrect because the AWS Cloud Economics Center and Cloud Value Framework provide conceptual guidance and methodology, not a workload-specific TCO report. AWS Cost and Usage Reports are used to analyze existing AWS usage, not to estimate costs for workloads that have not yet been migrated.
Therefore, using Migration Evaluator to collect on-premises data, model the target EKS and RDS architecture, and export a Quick Insights TCO report is the correct solution.
References:AWS documentation on Migration Evaluator for building migration business cases, estimating total cost of ownership, and generating Quick Insights reports.AWS guidance on using Migration Evaluator to model container and database migrations and compare on-premises costs with projected AWS costs.
NEW QUESTION # 487
A company is running a containerized workload on AWS. The workload consists of several data-processing services that run on a group of Amazon EC2 instances.
The company uploads new data to an Amazon S3 bucket every night. A cron job on each EC2 instance starts the data processing every night. The amount of uploaded data varies. The data-processing tasks can take hours to finish running. After the data is processed, the services remain idle until the next processing window occurs the next night. The company needs a solution to modernize the architecture and reduce the operational overhead.
Which solution will meet these requirements?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The workload is containerized, runs for hours, and is event-driven by nightly data arrival in Amazon S3. The current architecture uses EC2 instances and cron jobs, which results in operational overhead (managing instances, patching, scaling, scheduling) and idle compute between processing windows.
A key constraint is that the processing tasks can take hours. AWS Lambda has maximum execution duration limits that make it unsuitable for multi-hour batch processing. Even though Lambda can run container images, it still must complete within Lambda's runtime limit. Packaging container images as Lambda layers is also not an appropriate pattern for long-running container workloads and adds complexity.
A modern, low-ops approach for long-running, containerized batch jobs is to run containers on AWS Fargate.
Fargate removes the need to manage EC2 instances and allows tasks to run for extended periods as needed, scaling based on demand. Because the workload is composed of several data-processing services that likely need orchestration (for example, fan-out, sequencing, retries, parallelism), AWS Step Functions is well suited to coordinate the workflow and invoke the appropriate ECS tasks.
For triggering based on new S3 data, Amazon EventBridge provides a managed, scalable event bus for AWS service events, including S3 object events, and can route events to targets such as Step Functions state machines. Using EventBridge reduces the need for direct point-to-point notification wiring and provides centralized event routing, filtering, and monitoring.
Option C combines all the right elements: it runs the containers as ECS tasks on Fargate to eliminate EC2 management and idle capacity, uses Step Functions to orchestrate tasks that can run for hours, and uses EventBridge to trigger the state machine when new data is uploaded to S3. This replaces the per-instance cron scheduling with an event-driven serverless orchestration model and significantly reduces operational overhead.
Option B is close but is less appropriate as written because S3 Event Notifications are typically configured to send to Amazon SQS, Amazon SNS, or AWS Lambda. Triggering Step Functions directly is more naturally handled through EventBridge rules. EventBridge is also the recommended event routing layer for integrating service events into workflows.
Option A is not suitable because Lambda is not designed for multi-hour processing jobs due to runtime limits.
Option D is incorrect because Lambda layers are for sharing libraries and runtime dependencies, not for packaging multi-hour container workloads. It also still depends on Lambda runtime limits and does not match the operational model for long-running batch processing.
Therefore, option C is the best modernization approach with the least operational overhead.
References:AWS documentation on AWS Fargate for running container workloads without managing EC2 instances and supporting long-running tasks.AWS documentation on AWS Step Functions for orchestrating long-running workflows, retries, parallelism, and service integrations including Amazon ECS.AWS documentation on Amazon EventBridge for routing Amazon S3 object events to targets such as Step Functions state machines for event-driven architectures.
NEW QUESTION # 488
A company is building a solution in the AWS Cloud. Thousands or devices will connect to the solution and send data. Each device needs to be able to send and receive data in real time over the MQTT protocol. Each device must authenticate by using a unique X.509 certificate.
Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
Explanation
This solution requires minimal operational overhead, as it only requires setting up AWS IoT Core and creating a thing for each device. (Reference: AWS Certified Solutions Architect - Professional Official Amazon Text Book, Page 537) AWS IoT Core is a fully managed service that enables secure, bi-directional communication between internet-connected devices and the AWS Cloud. It supports the MQTT protocol and includes built-in device authentication and access control. By using AWS IoT Core, the company can easily provision and manage the
X.509 certificates for each device, and connect the devices to the service with minimal operational overhead.
NEW QUESTION # 489
A company is building an application that will run on an AWS Lambda function. Hundreds of customers will use the application. The company wants to give each customer a quota of requests for a specific time period.
The quotas must match customer usage patterns. Some customers must receive a higher quota for a shorter time period.
Which solution will meet these requirements?
Answer: A
Explanation:
Explanation
The correct answer is A.
A: This solution meets the requirements because it allows the company to create different usage plans for each customer, with different request quotas and time periods. The usage plans can be associated with API keys, which can be distributed to the users of each customer. The API Gateway REST API can invoke the Lambda function using a proxy integration, which passes the request data to the function as input and returns the function output as the response. This solution is scalable, secure, and cost-effective12 B: This solution is incorrect because API Gateway HTTP APIs do not support usage plans or API keys. These features are only available for REST APIs3 C: This solution is incorrect because it does not provide a way to enforce request quotas for each customer.
Lambda function aliases can be used to create different versions of the function, but they do not have any quota mechanism. Moreover, this solution exposes the Lambda function URLs directly to the customers, which is not secure or recommended4 D: This solution is incorrect because it does not provide a way to differentiate between customers or users.
AWS WAF rate-based rules can be used to limit requests based on IP addresses, but they do not support any other criteria such as user agents or headers. Moreover, this solution adds unnecessary complexity and cost by using an ALB and a VPC56 References:
1: Creating and using usage plans with API keys - Amazon API Gateway 2: Set up a proxy integration with a Lambda proxy integration - Amazon API Gateway 3: Choose between HTTP APIs and REST APIs - Amazon API Gateway 4: Using AWS Lambda aliases - AWS Lambda 5: Rate-based rule statement - AWS WAF, AWS Firewall Manager, and AWS Shield Advanced 6: Lambda functions as targets for Application Load Balancers
- Elastic Load Balancing
NEW QUESTION # 490
A company that provisions job boards for a seasonal workforce is seeing an increase in traffic and usage. The backend services run on a pair of Amazon EC2 instances behind an Application Load Balancer with Amazon DynamoDB as the datastore. Application read and write traffic is slow during peak seasons.
Which option provides a scalable application architecture to handle peak seasons with the LEAST development effort?
Answer: D
Explanation:
Option C is correct because using Auto Scaling groups for the backend services allows the company to scale up or down the number of EC2 instances based on the demand and traffic. This way, the backend services can handle more requests during peak seasons without compromising performance or availability. Using DynamoDB auto scaling allows the company to adjust the provisioned read and write capacity of the table or index automatically based on the actual traffic patterns. This way, the table or index can handle sudden increases or decreases in workload without throttling or overprovisioning1.
Option A is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, increasing the read and write capacity of DynamoDB manually may not be efficient or cost-effective, as it does not account for the variability of the workload. The company may end up paying for unused capacity or experiencing throttling if the workload exceeds the provisioned capacity1.
Option B is incorrect because migrating the backend services to AWS Lambda may require significant development effort to rewrite the code and test the functionality. Moreover, configuring DynamoDB to use global tables may not be necessary or beneficial for the company, as global tables are mainly used for replicating data across multiple AWS Regions for fast local access and disaster recovery. Global tables do not automatically scale the provisioned capacity of each replica table; they still require manual or auto scaling settings2.
Option D is incorrect because using Amazon Simple Queue Service (Amazon SQS) and an AWS Lambda function to write to DynamoDB may introduce additional complexity and latency to the application architecture. Amazon SQS is a message queue service that decouples and coordinates the components of a distributed system. AWS Lambda is a serverless compute service that runs code in response to events. Using these services may require significant development effort to integrate them with the backend services and DynamoDB, Moreover, they may not improve the read performance of DynamoDB, which may also be affected by high traffic3.
Reference:
Auto Scaling groups
DynamoDB auto scaling
AWS Lambda
DynamoDB global tables
AWS Lambda vs EC2: Comparison of AWS Compute Resources - Simform
Managing throughput capacity automatically with DynamoDB auto scaling - Amazon DynamoDB AWS Aurora Global Database vs. DynamoDB Global Tables Amazon Simple Queue Service (SQS)
NEW QUESTION # 491
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