10 Serverless Computing Interview Questions and Answers for go engineers

1. Can you explain the basics of serverless computing?

Serverless computing is a model of cloud computing where the cloud provider manages the infrastructure and automatically allocates computing resources as needed to execute code. In this model, the user only pays for the time their code is actually executed, unlike traditional server-based models where the user pays for a fixed amount of computing resources regardless of whether they are actually used.

The advantages of serverless computing include:

1. Scalability: the cloud provider automatically allocates resources to handle increased load without the need for the user to manually add or remove resources.
2. Cost savings: users only pay for the computing time actually used, which can result in significant cost savings compared to traditional server-based models.
3. Flexibility: users can focus on developing and deploying code without worrying about infrastructure management.

One example of serverless computing in action is AWS Lambda, which allows users to run code in response to events without having to provision or manage servers.

In a recent study, companies that switched to serverless computing reported a 30% reduction in infrastructure costs and a 40-50% reduction in development time compared to traditional server-based models. Additionally, serverless computing allows for faster scaling and increased agility, enabling companies to adapt quickly to changing market conditions.

2. What are some common use cases for serverless?

Serverless computing has become a popular approach to building scalable and cost-effective applications. Some common use cases for serverless include:

1. Event-driven processing - Serverless architectures are perfect for handling events such as real-time data streams, IoT devices, and notifications. One example is a financial institution that uses serverless to process incoming transactions in real-time, reducing the time it takes to detect and prevent fraud by 50%.

2. Low-latency web applications - By leveraging serverless computing, developers can build web applications that are highly responsive, even during high-traffic periods. One example is a social networking site that uses serverless to handle user requests, resulting in 30% faster page load times compared to traditional architectures.

3. Microservices - Serverless architectures are ideal for building microservices, which are small, independent components that work together to create an application. This approach allows for greater flexibility and scalability, and reduces the risk of system failures. One example is a healthcare provider that uses serverless to manage patient records, resulting in a 40% reduction in storage costs and a 25% improvement in data retrieval times.

4. Data processing - Serverless can also be used for data processing tasks such as ETL (Extract, Transform, Load) and batch processing. One example is an e-commerce company that uses serverless to process customer orders and generate reports, resulting in a 20% reduction in operational costs and a 30% improvement in data accuracy.

These are just a few examples of how serverless computing is being used today. As more organizations embrace this approach, we can expect to see even more innovative use cases emerge.

3. What serverless platforms have you worked with?

During my previous role as a Serverless Developer at XYZ Company, I had the opportunity to work with several serverless platforms including:

1. AWS Lambda: I developed a serverless application using AWS Lambda that processed over 500,000 requests per month with an average response time of 100 milliseconds.
2. Google Cloud Functions: I implemented a serverless function using Google Cloud Functions that reduced the application's deployment time by 50% and improved the scalability of the system.
3. Azure Functions: I utilized Azure Functions to build a serverless API that integrated with third-party payment gateways, which resulted in a 20% increase in revenue for the business.

Overall, my experience with these platforms has enabled me to address complex computing challenges in a cost-effective and scalable way while providing a seamless experience to end-users.

4. How do you ensure security and compliance in a serverless architecture?

Ensuring security and compliance in a serverless architecture is a complex task that requires a multi-layered approach. Here are some steps that I follow:

1. Proper Authentication: I make sure that authentication mechanisms are put in place to ensure secure access to the serverless architecture. For instance, applying a proper gatekeeper approach or tool such as AWS Cognito, OAuth2, or JWTs can secure the serverless environment. This ensures that only authorized users and applications can access resources.
2. Data Encryption and Protection: In order to protect data, it is important to encrypt it both in transit and at rest. Therefore, I utilize encryption mechanisms such as HTTPS, SSL, and Network ACLs where necessary to protect my serverless applications.
3. API Gateway Security: API Gateway grants authentication and authorization to API requests. Therefore, I build security policies to monitor and control access to APIs by ensuring that API Gateway meets the security compliances of standards such as HIPAA, ISO, and SOC.
4. Serverless Function-Level Security: In addition, I secure each Lambda function by leveraging AWS Config rules, which can be used for automating audits of your AWS resources to ensure compliance with security standards. I also ensure that each Lambda function is configured with the least privilege permission - this is necessary to reduce the attack vector.
5. Monitoring and Logging: Security events must be traced and tracked; therefore, I leverage CloudWatch, AWS CloudTrail and other monitoring tools to collect log data, monitor events, and analyze logs for suspicious activity. I also maintain well-structured logs that can be analyzed to generate reports, identifying any non-compliant activity.

Overall, my approach to ensuring Security and compliance on serverless architecture has yielded impressive results. For instance, I was able to reduce unauthorized access attempts by 75% and achieve 100% compliance with industry standards such as HIPAA and PCI-DSS.

5. What are the most significant differences between serverless and traditional computing architectures?

One of the most significant differences between serverless and traditional computing architectures is the way they handle scaling. In a traditional computing environment, scaling requires the addition of new servers to handle increased traffic or workload. This can be costly and time-consuming, as it requires setup time, configuration, and maintenance.

On the other hand, serverless computing scales automatically, without the need for additional infrastructure. This is because serverless architecture is event-driven and only uses computing resources when required. For example, if a company's website experiences a sudden spike in traffic, a traditional computing environment may struggle to handle the increased demand, resulting in slow load times and even downtime. In contrast, with serverless computing, scaling occurs automatically and instantaneously, ensuring that website visitors experience fast load times and no downtime.

Another significant difference is cost. In traditional computing environments, the cost of additional infrastructure can be significant, as it requires new servers, storage, and networking equipment. With serverless computing, costs are based on usage, which means companies only pay for what they use. This can result in significant cost savings, especially for smaller companies or startups.

Finally, serverless computing offers greater flexibility when it comes to development. In a traditional computing environment, developers need to worry about configuring servers, load balancing, and other infrastructure-related tasks. In contrast, serverless computing allows developers to focus on writing code and developing applications, rather than worrying about the underlying infrastructure. This can result in faster development cycles and more innovative applications.

6. How do you manage and monitor serverless applications?

Managing and monitoring serverless applications is crucial to ensure that everything is running smoothly and in case of issues, they are addressed as fast as possible. As a serverless computing expert, I have experience using multiple tools to help me in this task.

1. CloudWatch: This is an Amazon Web Services (AWS) native tool that provides a range of monitoring and management capabilities for serverless applications. I have used it extensively to detect and resolve issues, analyze logs, and set up alarms for critical events. With my experience in CloudWatch, I was able to reduce the downtime for one of my clients by 40%.
2. Datadog: This is a popular cloud monitoring platform that I have also used to manage and monitor serverless applications. Datadog allows me to track resource usage, identify anomalies, and analyze metrics across multiple cloud providers. Thanks to Datadog, I reduced costs by 20% for one of my clients by identifying and fixing resource wastage.
3. AppDynamics: This APM (Application Performance Monitoring) tool provides deep insights into serverless architectures and helps me understand how different functions interact with each other. I have used AppDynamics to diagnose and fix issues in real-time, reducing downtime and improving performance metrics by up to 30% for one of my clients.
4. New Relic: This cloud-based monitoring tool is another tool I have used. It provides real-time insights into serverless applications, highlighting errors, and bottlenecks. Starting from scratch, I was able to set up New Relic for a client, and reduce the resolution time for certain errors from hours to minutes.

In conclusion, I have experience with multiple tools to manage and monitor serverless applications. By leveraging these tools, I was able to detect and resolve issues quickly, reduce downtime, and optimize costs for my clients.

7. What are some possible performance pitfalls to be aware of in serverless architectures?

When it comes to serverless architectures, there are some potential performance pitfalls that developers should keep in mind:

1. Cold starts: When a function is invoked for the first time, there may be a delay as the cloud provider sets up the necessary resources to run it. This delay can impact performance, particularly for functions that are infrequently invoked. For example, in a recent project, we observed that the first invocation of a function took over 500ms, while subsequent invocations took around 100ms.
2. Memory allocation: Serverless functions are often charged based on the amount of memory they use. However, increasing the memory allocated to a function can lead to faster execution times, as more CPU resources are made available. In one test we conducted, we found that doubling the amount of memory allocated to a function resulted in a 50% reduction in execution time.
3. Size of deployment package: Since serverless functions are typically deployed as small packages of code, excessive package size can cause performance issues. This is particularly true for large applications, where dependencies may be duplicated across multiple functions. In one instance, we found that shrinking a package size from over 200MB to less than 50MB resulted in a 90% improvement in deployment time.
4. Concurrency limits: Many cloud providers impose concurrency limits on serverless functions, which can lead to performance issues during periods of high usage. For example, in a recent project, we hit a concurrency limit of 1000 for a particular function. During periods of heavy usage, some requests were dropped, resulting in a decrease in overall performance.

By keeping these pitfalls in mind and optimizing serverless functions accordingly, developers can ensure that their applications perform optimally and meet the needs of users.

8. How have you implemented serverless functions with different event triggers?

How have you implemented serverless functions with different event triggers?

In my previous project, I was responsible for developing and deploying serverless functions for an e-commerce website. The website had different triggers such as user registration, order placement, and inventory management.

To implement serverless functions with different event triggers, I followed the following steps:

1. Identified the event triggers and the corresponding actions I needed to perform.
2. Created separate Lambda functions for each event trigger.
3. Configured the Lambda functions to listen to the specific event triggers.
4. Added the necessary code to handle the trigger and perform the action.

For example, when a new user registered on the website, the Lambda function would trigger and perform the following actions:

• Create a new user profile in the database.
• Send a welcome email to the new user.
• Assign default roles and permissions to the new user.

I also implemented serverless functions with different event triggers for order placement and inventory management. The results were impressive as the website's response time improved significantly, and the serverless architecture was more cost-efficient.

With these experiences, I am confident that I can implement serverless functions with different event triggers effectively and efficiently in any project.

9. How do you approach testing and debugging in a serverless environment?

As an experienced serverless developer, I understand that testing and debugging are critical aspects of the development process. In a serverless environment, where multiple functions interact with each other, proper testing and debugging can make the difference between success and failure.

1. Unit Testing: I begin by writing unit tests as I develop each function. These tests validate the function's input and output. This helps identify flaws in the function's logic or code before they cause more extensive problems.
2. Integration Testing: Once I've written and tested individual functions, I move on to integration testing. This involves testing each function's interaction with other functions in a controlled environment. I create mock data points and simulate system events to test how each function behaves in its production environment.
3. End-to-End Testing: After integration testing, I conduct end-to-end testing to assess how the entire system operates. This testing ensures that all functions work together to deliver the intended results. It also aids in identifying bottlenecks and opportunities for optimization.

When it comes to debugging, my approach is to use the proper tools to identify and fix issues promptly. For example, I leverage logging and monitoring tools to track system behavior and analyze issues. These tools enable me to detect issues early on, allowing me to take a proactive approach to fixing them before they become more significant problems.

During a recent project, I followed this approach to testing and debugging to ensure that the system was running correctly. As a result, we achieved a 99% uptime and zero production bugs were reported in Q3 2023.

10. Can you give me an example of how you would architect a serverless application for scalability and high availability?

When architecting a serverless application for scalability and high availability, I would first consider breaking down the application into small microservices that can be independently deployed and scaled.

1. One such microservice would be responsible for handling incoming requests and routing them to the appropriate service.
2. Another microservice could handle data storage and retrieval.
3. Yet another one could handle authentication and user management.

The decoupling of these services allows for greater flexibility and easier management of the application as a whole. Ideally, each microservice would also be designed to automatically scale based on demand, be it through a serverless function or a container.

To ensure high availability, I would also design the application to be distributed across multiple geographic regions, with load balancers routing traffic to the nearest available region. This approach ensures that users experience minimal disruption in case of any failures in one region.

As a result of such architecture, one of my previous projects -- a e-commerce solution -- was able to quickly scale to handle seasonal spikes in traffic without any downtime or performance issues. The application was able to support multiple geographical regions, providing quicker access and less waiting time for users. Users were able to access the site without any server errors, enhancing their experience and resulting in an increase in sales conversion rate by 15%.

Conclusion

Congratulations on finishing our list of 10 Serverless Computing interview questions and answers in 2023! Now that you're armed with knowledge to ace your interview, the next steps are to write an outstanding cover letter and prepare an impressive CV. Don't forget to checkout our guide on writing a stellar cover letter and our guide on writing a resume for go engineers (which you can find at https://www.remoterocketship.com/advice/guide/go-engineer/resume). If you're searching for a new job, Remote Rocketship is the perfect place for you! We specialize in connecting remote go engineers with top companies. Check out our remote go engineer job board to find your next exciting opportunity.