Serverless Computing

5 minutes 5 Questions

Serverless computing is a cloud-based execution model that dynamically allocates resources as needed, allowing developers to build and run applications without managing servers. Serverless does not mean that there are no servers involved, but rather that the responsibility of managing servers is abstracted away from the developer. The cloud provider automatically manages the infrastructure, scaling, and patching, while the developer focuses on writing application logic. Users are charged based on actual usage, instead of pre-allocated resources. Applications built with serverless computing are typically event-driven, responding to triggers like user requests, file uploads, or database operations. Examples of serverless computing platforms include AWS Lambda, Microsoft Azure Functions, and Google Cloud Functions.

Guide to Serverless Computing

What is Serverless Computing?
Serverless Computing is a computing framework where developers don’t have to worry about the machines on which their code is running. It enables developers to focus on writing their applications and the cloud provider manages the rest.

Why is Serverless Computing Important?
Serverless Computing simplifies development process by removing the need to manage infrastructcure. It increases productivity, and allows faster time to market for applications. It also optimizes costs as you only pay for the compute power used.

How it Works
In Serverless Computing, the cloud provider dynamically manages the allocation of machine resources. The applications are broken down to functions, which run on demand, enabling efficient use of resources.

Exam Tips: Answering Questions on Serverless Computing
-Understand the basic concept of serverless computing and its benefits.
-Know the difference between serverless computing and traditional cloud computing.
-Remember that with serverless computing, you only pay for the compute power used, not for the infrastructure.
-Note that being 'serverless' doesn't mean there are no servers. It means the developers don't need to worry about the servers as they are managed by the cloud provider.
-Be aware of use cases where serverless computing can be beneficial.
-Understand the role of FaaS (Functions-as-a-Service) in serverless computing.
Note: It's crucial to offer precise and accurate information when answering questions about Serverless Computing in exams.

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Question 1

When designing a serverless architecture for your application, which of the following databases should you use for high scalability and low latency?

DynamoDB Aurora RDS Redshift

Correct Answer: DynamoDB

DynamoDB is the correct choice when designing a serverless architecture for high scalability and low latency. This is because DynamoDB is a fully managed NoSQL database service provided by Amazon for applications that require consistency and low latency even at scale. It integrates well with serverless architectures where developers do not have to manage server infrastructure. On the contrary, RDS is a managed relational database service suitable for structured data and applications that require complex joins and transactions. Aurora is a part of the RDS service and is compatible with MySQL and PostgreSQL but does not have the scalability and low latency advantage of DynamoDB at high throughput. Lastly, Redshift is a data warehouse best designed for large scale data analytics and not suitable for low latency serverless applications.

Question 2

A company needs real-time data processing and analysis from their IoT devices. Which serverless computing service would be the most suitable for this scenario?

AWS IoT Core AWS EMR RDS AWS Kinesis Data Firehose

Correct Answer: AWS Kinesis Data Firehose

AWS Kinesis Data Firehose is the most suitable for real-time data processing and analysis. It is designed for the real-time processing of streaming data - it can capture, transform, and load streaming data into AWS destinations such as Amazon Simple Storage Service (Amazon S3), Amazon Redshift, Amazon Elasticsearch Service (Amazon ES), and Splunk. This characteristic makes it the best fit for handling data from IoT devices. On the other hand, AWS EMR is for large-scale data processing, not real-time data, RDS is a relational database service which is not typically used for real-time analysis, and AWS IoT Core is more about device communication and security rather than real-time data processing and analysis.

Question 3

A company wants to create a serverless application for processing customer orders. The orders come in different sizes and may take varying amounts of time to process. What is the best strategy to manage serverless functions for this use case?

Use a serverless function with a fixed timeout regardless of the order size. Implement a queue-based load leveling pattern to process the orders efficiently and according to their priority. Set up a single serverless function to handle all order processing. Create separate serverless functions for each order size.

Correct Answer: Implement a queue-based load leveling pattern to process the orders efficiently and according to their priority.

By implementing a queue-based load-leveling pattern, the company can manage the processing of orders efficiently, taking into account their size and priority. This allows the processing of orders to be more flexible and better utilize system resources. The other options may not effectively manage the varying processing times and priorities of the orders.

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