Serverless Computing Concepts Questions and Answers for Viva

Answer-1: Serverless computing is a cloud computing execution model where the cloud provider dynamically manages the allocation and provisioning of servers, allowing developers to focus on code without managing infrastructure.

Answer-2: In traditional cloud computing, users manage servers or VMs, whereas in serverless computing, the cloud provider handles server management automatically.

Answer-3: FaaS is a serverless computing model where developers deploy individual functions that are executed on-demand in response to events.

Answer-4: Benefits include automatic scaling, no server management, pay-per-use pricing, faster development, and improved resource utilization.

Answer-5: AWS Lambda, Azure Functions, Google Cloud Functions, IBM Cloud Functions.

Answer-6: Billing is usually based on the number of function executions and the duration and resources consumed during those executions.

Answer-7: Functions are triggered by events such as HTTP requests, database changes, file uploads, scheduled timers, or messaging queues.

Answer-8: Cold start is the latency that occurs when a serverless function is invoked after being idle, requiring the platform to provision resources before execution.

Answer-9: By using techniques like keeping functions warm with scheduled invocations or optimizing function initialization code.

Answer-10: Stateless functions do not maintain any state between executions; all necessary state information must be passed in or stored externally.

Answer-11: Because serverless environments are ephemeral and functions may run on different servers, making state persistence unreliable.

Answer-12: State should be stored in external services such as databases, caches, or object storage.

Answer-13: Use cases include web APIs, data processing, real-time file processing, chatbots, IoT backends, and scheduled tasks.

Answer-14: API Gateway acts as a front door to expose serverless functions as HTTP endpoints, handling authentication, routing, and throttling.

Answer-15: Serverless platforms automatically scale functions horizontally based on incoming event volume.

Answer-16: A backend built using serverless services like FaaS, managed databases, and storage without managing servers.

Answer-17: Developers focus on writing code without worrying about provisioning or maintaining infrastructure, speeding up development cycles.

Answer-18: Limitations include cold start delays, execution time limits, limited control over infrastructure, and complexity in debugging.

Answer-19: No, most platforms impose execution time limits (e.g., AWS Lambda max 15 minutes). Long-running tasks require different approaches.

Answer-20: Most platforms support popular languages like JavaScript (Node.js), Python, Java, C#, Go, and others.

Answer-21: An architecture where functions respond to events generated by other services or users, enabling loosely coupled and scalable systems.

Answer-22: Serverless reduces attack surface by abstracting servers, but requires securing event sources, APIs, and access to external resources.

Answer-23: Cloud provider tools like AWS CloudWatch, Azure Monitor, Google Stackdriver, and third-party tools such as Datadog or New Relic.

Answer-24: Serverless abstracts infrastructure and auto-scales functions on demand, while containers require you to manage deployment and scaling.

Answer-25: By using cloud logs, local emulators, remote debugging tools, and adding detailed logging in functions.

Answer-26: Serverless applications often rely on proprietary platform features, making migration to other platforms challenging.

Answer-27: A set of tools to develop, deploy, and manage serverless applications, like the Serverless Framework or AWS SAM.

Answer-28: Yes, serverless can be used for real-time data processing with appropriate event triggers and low latency functions.

Answer-29: Managed database services that auto-scale and require no server management, such as AWS DynamoDB or Google Firestore.

Answer-30: By allowing each microservice component to be implemented as independent functions that scale automatically.

Answer-31: Factors include runtime language, function size, network latency, and cloud provider optimizations.

Answer-32: By using open standards, abstraction layers, and minimizing reliance on proprietary services.

Answer-33: Currently, AWS Lambda functions can run for up to 15 minutes per invocation.

Answer-34: Platforms allocate separate instances for concurrent function invocations, scaling horizontally to meet demand.

Answer-35: Concerns include event data injection, inadequate access controls, insecure dependencies, and insufficient monitoring.

Answer-36: Yes, services like AWS Fargate and Azure Container Instances offer serverless containers.

Answer-37: Serverless focuses on running your custom functions, while BaaS provides ready-made backend services like authentication and databases.

Answer-38: Invoking multiple serverless functions in a sequence to accomplish complex workflows.

Answer-39: Using external services like AWS Step Functions or Durable Functions to coordinate state.

Answer-40: Network latency affects response times, especially for functions calling external services.

Answer-41: By charging only for actual compute time and scaling down automatically, eliminating idle resources.

Answer-42: Functions that can safely be called multiple times without changing the result beyond the initial application.

Answer-43: Because functions may be retried or invoked multiple times, idempotency prevents unintended side effects.

Answer-44: Configuration values passed to functions at runtime, enabling flexible deployment and secrets management.

Answer-45: By integrating with cloud provider logging services and using structured logs within functions.

Answer-46: Coordinating multiple serverless functions to execute complex business processes.

Answer-47: Running periodic jobs such as backups, cleanups, or batch data processing without managing servers.

Answer-48: It encourages automation, continuous integration, and delivery by simplifying deployment and scaling.

Answer-49: Yes, serverless can be integrated with on-premises and multi-cloud architectures via APIs and connectors.

Answer-50: Trends include better cold start optimizations, serverless containers, improved stateful workflows, and expanded language support.