Which service combination processes hundreds of simultaneous user requests efficiently?

CLF-C02 By islever Editorial Last updated

Identify AWS compute services. Identify services from other in-scope AWS service categories.

Short answer

A Use Amazon SQS to buffer and decouple hundreds of concurrent user requests and AWS Lambda to process them with automatic serverless scaling.

Question

A company needs to simultaneously process hundreds of requests from different users.
Which combination of AWS services should the company use to build an operationally efficient solution?
  1. Amazon Simple Queue Service (Amazon SQS) and AWS Lambda Correct Answer
  2. AWS Data Pipeline and Amazon EC2
  3. Amazon Kinesis and Amazon Athena
  4. AWS Amplify and AWS AppSync

Key Takeaways

Tests whether you can match a workload pattern (bursty, high-volume, decoupled request processing) to serverless services — the trap is picking data-orchestration or analytics tools that do not perform request processing at all.

The question asks which AWS service pairing delivers an operationally efficient way to process hundreds of concurrent user requests. Amazon SQS plus AWS Lambda is correct because the queue decouples and buffers incoming requests while Lambda scales processing elastically without server management.

Common mistake: Option B (AWS Data Pipeline with Amazon EC2) attracts learners who equate 'processing data' with 'processing requests'; Data Pipeline orchestrates batch data movement between storage and compute, not high-volume concurrent request handling, and EC2 requires capacity management that undercuts operational efficiency.

Expert Analysis

Why the Answer Is Correct

Amazon SQS is a fully managed queue that absorbs hundreds of simultaneous user requests, buffering them so producers and consumers stay decoupled and no downstream component is overwhelmed. AWS Lambda then polls the queue and scales concurrency automatically, so the processing tier grows and shrinks with the actual workload with no servers to provision. Together they deliver the operational efficiency the question asks for: no infrastructure management, automatic scaling, and built-in durability and retry behavior. That directly matches "simultaneously process hundreds of requests from different users."

Why the Other Options Are Wrong

AWS Data Pipeline (option B) is an orchestration service for scheduled, batch-oriented data movement between AWS compute/storage services and on-premises sources — as AnnieApples put it, it "moves data between AWS compute / storage services and on prem data," which is unrelated to fan-out request handling, and EC2 instances would reintroduce the capacity planning the scenario is trying to avoid. Amazon Kinesis with Athena (option C) is a streaming-ingest plus interactive query pattern suited to analytics on streaming data, not per-request transactional processing. AWS Amplify with AWS AppSync (option D) targets building web and mobile front ends with managed GraphQL APIs; AppSync resolvers could invoke compute, but the pairing provides no queue-based buffering or elastic batch consumers for hundreds of concurrent requests, and Amplify is a front-end hosting and tooling service.

Community Comment Notes

Community sentiment overwhelmingly favors option A, with multiple learners independently walking through the decoupling and auto-scaling rationale — AnnieApples dismisses Data Pipeline as irrelevant, and ArkieKT explains that SQS "acts as a buffer, storing incoming requests and decoupling the request generation from the actual processing." Proctored_Expert and man5484 add scalability and reliability points, while cqrm3n stresses asynchronous communication and automatic scaling. Several voters, including Uchendum, simply state "SQS helps with the question," and one commenter (Jaccky) questions why the item is considered controversial at all — a hint that the intended pairing is straightforward once Data Pipeline is ruled out.

Note: the source key for this item is unmarked, so this analysis is based on the question wording and the AWS service definitions above.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

When a stem stresses high concurrency plus operational efficiency, look for fully managed, auto-scaling services and eliminate any option containing a batch-orchestration or analytics service (Data Pipeline, Athena). Ask yourself whether each service in the pair actually performs request processing — one irrelevant component invalidates the whole combination.

Frequently Asked Questions

Why is AWS Data Pipeline not the right choice for processing hundreds of requests?

Data Pipeline orchestrates scheduled batch data movement between storage and compute services, not high-volume concurrent request handling, and pairing it with EC2 adds server management overhead.

Does Lambda scale automatically when messages arrive in an SQS queue?

Yes. Lambda polls the queue and scales concurrency with the backlog, so hundreds of queued requests are processed in parallel without provisioning capacity.

More CLF-C02 FAQ →

Discussion 88 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 30 of 88

  1. Jaccky 👍 51
    Why there're so much controversial questions
  2. AnnieApples 👍 26 Answered A
    Data pipeline is not relevant for this question - it moves data between AWS compute / storage services and on prem data.
  3. ArkieKT 👍 6 Answered A
    The most suitable answer here is A. Amazon Simple Queue Service (Amazon SQS) and AWS Lambda. Here's why this combination is a strong fit for the scenario: Why SQS and Lambda work well together: Decoupling: SQS acts as a buffer, storing incoming requests and decoupling the request generation from the actual processing. This prevents the backend from being overwhelmed. Scalability: Lambda functions can automatically scale up or down based on the volume of messages in the SQS queue. This ensures efficient resource utilization to match demand. Asynchronous Processing: This architecture allows for asynchronous handling of requests. Users don't have to wait for immediate responses, improving responsiveness. Fault Tolerance: If a Lambda function fails, SQS retains the message for retry, adding resilience.
  4. kenamatias 👍 6 Answered A
    Para quem ainda está na dúvida: O AWS Data Pipeline não é a melhor escolha para processar centenas de solicitações simultaneamente. O AWS Data Pipeline é usado principalmente para orquestrar e automatizar fluxos de trabalho de processamento de dados que envolvem transferências de dados entre diferentes serviços e transformações de dados. A combinação mais adequada para processar centenas de solicitações simultâneas seria a opção A. Amazon Simple Queue Service (Amazon SQS) e AWS Lambda. O Amazon SQS enfileira e gerencia as solicitações e o AWS Lambda executa o código de processamento de maneira escalável e assíncrona em resposta a cada solicitação da fila. Isso proporciona uma solução eficiente e escalável para lidar com solicitações simultâneas de usuários.
  5. Proctored_Expert 👍 5 Answered A
    The combination of AWS services that the company should use to build an operationally efficient solution for simultaneously processing hundreds of requests from different users is Amazon Simple Queue Service (Amazon SQS) and AWS Lambda. Amazon SQS is a fully managed message queuing service that enables applications to send and receive messages in a distributed and decoupled manner. Amazon SQS can be used to store and process requests from users, and to ensure that they are processed in a timely and reliable manner. AWS Lambda is a serverless compute service that runs code in response to events and automatically manages the underlying compute resources. AWS Lambda can be used to process requests stored in an Amazon SQS queue, allowing the company to scale the processing of requests without the need to provision and manage servers.
Show 25 more comments
  1. cqrm3n 👍 4
    Amazon SQS is a fully managed message queuing service that decouples the sending and receiving of messages, allowing for asynchronous communication between components. It can handle a large number of requests and offers features like message durability and automatic scaling. AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers. It can be triggered by events, such as messages in an Amazon SQS queue, and execute functions in response. Lambda scales automatically based on the incoming request rate, ensuring efficient processing of multiple requests. By combining Amazon SQS and AWS Lambda, the company can achieve asynchronous message processing, reliable message delivery, and automatic scaling to handle the high volume of requests from different users efficiently.
  2. man5484 👍 4 Answered A
    The combination of SQS and Lambda offers several benefits: Scalability: SQS allows for handling a large number of requests, while Lambda automatically scales the processing capacity based on the incoming workload. Reliability: SQS ensures reliable message delivery, and Lambda functions are designed to be stateless and fault-tolerant. Decoupling: The decoupling of services through SQS enables loose coupling, allowing components to evolve independently and scale separately.
  3. Uchendum 👍 4
    SQS helps with the question. I vote A
  4. Marcelo_Costa 👍 3 Answered A
    The most suitable combination of AWS services for processing hundreds of requests from different users in an operationally efficient manner would be: A. Amazon Simple Queue Service (Amazon SQS) and AWS Lambda Amazon SQS is a fully managed message queuing service that allows decoupling of the components of an application by enabling asynchronous communication between them. This service helps in reducing the coupling between the components and ensures that the application is highly scalable, reliable, and fault-tolerant. AWS Lambda is a serverless compute service that enables developers to run code without provisioning or managing servers. This service allows developers to focus on writing code that responds to events and requests, rather than managing infrastructure. By using Amazon SQS to decouple the components of the application and AWS Lambda to execute the code in response to events and requests, the company can build an operationally efficient solution that can handle hundreds of requests from different users simultaneously.
  5. GCipry3 👍 3 Answered A
    A. Amazon Simple Queue Service (Amazon SQS) and AWS Lambda. Amazon Simple Queue Service (SQS) is a fully managed message queuing service that enables the decoupling of components of a cloud application. It can handle any volume of messages, as well as any type of message, without requiring any immediate processing by the recipient. AWS Lambda is a compute service that runs code in response to events and automatically manages the compute resources required by that code. By using SQS and Lambda together, the company can build a solution that scales automatically to handle large volumes of requests without overloading any one instance or component, providing an operationally efficient solution.
  6. LabStation 👍 3
    Here is the explanation: Amazon SQS: Amazon SQS is a fully managed message queue service that can store and transmit messages between different system components. It is designed to handle high scalability and allows multiple requests to be queued and processed asynchronously, which is useful when there are a large amount of requests to be processed. AWS Lambda: AWS Lambda is a serverless compute service that lets you run code in response to events. Can be triggered automatically by Amazon SQS when new messages arrive in the queue. Each request can be processed individually as a separate Lambda function, which allows for concurrent processing and on-demand scaling.
  7. har_new 👍 3 Answered A
    A. Amazon Simple Queue Service (Amazon SQS) and AWS Lambda. Here's why: By combining Amazon SQS and AWS Lambda, the company can achieve efficient and scalable processing of hundreds of requests from different users. SQS acts as a buffer for incoming requests, ensuring that they are processed reliably, while Lambda functions can be invoked in response to messages in the queue, processing them in a scalable and cost-effective manner. This architecture allows for decoupling between the request producers and the request consumers, providing flexibility, reliability, and scalability.
  8. KokoScripts 👍 3
    Amazon SQS is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. It allows for managing and queuing hundreds or thousands of requests, making it ideal for handling multiple simultaneous user requests. AWS Lambda is a serverless compute service that automatically scales with the number of requests, so it can process each queued message individually, efficiently handling concurrent requests without managing infrastructure.
  9. LabStation 👍 3 Answered A
    A. Amazon Simple Queue Service (Amazon SQS) and AWS Lambda The most suitable combination of AWS services to handle the simultaneous processing of hundreds of requests from different users in an operationally efficient manner would be option A, which involves the use of Amazon Simple Queue Service (Amazon SQS) and AWS Lambda.
  10. kumaran1000001 👍 3
    B - Data Pipeline is for moving data around and EC2 is for running apps on virtual servers. C - Kinesis is for processing streaming data and Athena for interactive SQL queries D - doesn't apply here. So the answer should be A.
  11. N3ar 👍 3
    Has to be something like SQS or ELB or similar. So, I would go for A. Why did someone pick B? I don't understand this.
  12. GonzaloD 👍 3 Answered A
    Think Pipeline is more data transfer oriented and SQS handles messages so looks like better option
  13. LloydA 👍 3 Answered A
    Some clarity here please Admin
  14. Esward 👍 3
    A would be the correct answer
  15. buccanaquarter 👍 2
    C. Amazon Kinesis and Amazon Athena For simultaneously processing hundreds of requests from different users in a scalable and operationally efficient manner, Amazon Kinesis and Amazon Athena would be a suitable combination of AWS services. • Amazon Kinesis is designed for real-time data streaming and can handle large volumes of data with low latency. You can use it to ingest, buffer, and process streaming data in real-time. This is particularly useful when dealing with a high volume of incoming requests. Amazon Athena is a serverless, interactive query service for analyzing data in Amazon S3 using standard SQL. It allows you to run ad-hoc queries on your data stored in S3, which is valuable for processing and analyzing the data collected by Kinesis.
  16. Manny_75 👍 2 Answered B
    Option B is correct. SQS is from Producer to End User / Customers. See https://aws.amazon.com/sqs/ diagram shows clearly this concept. Therefore, I selected Data pipeline, a web service that you can use to automate the movement and transformation of data. With AWS Data Pipeline, you can define data-driven workflows, so that tasks can be dependent on the successful completion of previous tasks. You define the parameters of your data transformations and AWS Data Pipeline enforces the logic that you've set up. Please let me know your thoughts. AWS Data Pipeline also helps create EC2 instances flexibly.
  17. Dylan100 👍 2 Answered A
    Amazon Kinesis It is the AWS fully managed solution for collecting, processing, and analyzing streaming data in the cloud. streaming - Multiple consumers can react to events (messages) - Events live in the stream for long periods of time, so complex operations can be applied.<mark> Real-time </mark> Amazon Athena - is a serverless interactive query service. - It can take a bunch of CSV or JSON files in an S3 Bucket and load them into temporary SQL tables so you can run SQL queries. When you want to query CSV or JSON files
  18. chelosip 👍 2
    Answer is B AWS Data Pipeline, you can regularly access your data where it’s stored, transform and process it at scale, and efficiently transfer the results to AWS services such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR. AWS Data Pipeline helps you easily create complex data processing workloads that are fault tolerant, repeatable, and highly available ref: https://aws.amazon.com/datapipeline/
  19. Satta10 👍 2 Answered B
    For me B is the right answer. "AWS Data Pipeline helps you easily create complex data processing workloads that are fault tolerant, repeatable, and highly available. You don’t have to worry about ensuring resource availability, managing inter-task dependencies, retrying transient failures or timeouts in individual tasks, or creating a failure notification system."
  20. blopa 👍 2
    1 The combination of AWS services that would be the most suitable for the company to simultaneously process hundreds of requests from different users in an operationally efficient manner would be A. Amazon Simple Queue Service (Amazon SQS) and AWS Lambda.
  21. backslash_cc 👍 2 Answered A
    "Operationnaly efficient" are the key words for me. It means sustaining heavy load (SQS), and complying to AWS pillars (use serverless). Therefore, I can not see any other answer than A.
  22. AN0NG33K 👍 2
    It should be A, see question 8 similar scenario. https://d1.awsstatic.com/training-and-certification/docs-sa-pro/AWS-Certified-Solutions-Architect-Professional_Sample-Questions.pdf
  23. Guru4Cloud 👍 2 Answered A
    To efficiently handle concurrent requests from different users, the company can use a combination of Amazon Simple Queue Service (Amazon SQS) and AWS Lambda, which is option A.
  24. davemark 👍 2
    Amazon Simple Queue Service (Amazon SQS) and AWS Lambda M ExxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxE
  25. avinash4344 👍 2 Answered A
    Amazon SQS – Standard Queue • Scales from 1 message per second to 10,000s per second • No limit to how many messages can be in the queue

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