Serverless Deployment Models and Cloud Functions Basics Quiz Quiz

Explore foundational concepts of serverless deployment models, focusing on cloud functions and event-driven compute in the major public clouds. Assess your understanding of serverless computing triggers, scaling, billing, and supported use cases in a cloud-agnostic context.

1. Event-Driven Function Triggers

   Which type of event commonly triggers a serverless cloud function, such as when a new file is uploaded to storage?

   1. A file system change
   2. A scheduled timer
   3. A software crash
   4. A failed disk

   Explanation: A file system change, like uploading a new file, often triggers a serverless function in the cloud. Scheduled timers are another valid trigger type but are not specifically related to file uploads. A failed disk or software crash typically triggers alerting or recovery mechanisms, not event-driven functions.

2. Auto-Scaling in Serverless

   What is a key benefit of serverless deployment models when handling unpredictable workloads?

   1. Fixed resource allocation
   2. Auto-scaling based on events
   3. Manual scaling needs
   4. Always-on virtual machines

   Explanation: Serverless functions automatically scale up or down in response to incoming events, providing efficient resource use. Manual scaling requires user intervention and is contrary to the serverless approach. Always-on virtual machines and fixed resource allocation do not offer the flexibility or cost savings typical of serverless.

3. Billing Model for Cloud Functions

   How are users commonly billed for cloud functions in a serverless model?

   1. By the function's runtime resource usage
   2. By the number of deployed virtual machines
   3. By monthly flat fee
   4. By storage used only

   Explanation: Serverless platforms generally charge based on runtime and the amount of resources consumed by function executions. Billing by deployed virtual machines or by storage only does not reflect actual function usage. Monthly flat fees are uncommon for serverless compute, where usage varies.

4. Statelessness of Functions

   Why are serverless cloud functions typically considered stateless?

   1. They never process data
   2. Function instances do not preserve state between invocations
   3. They always store data locally
   4. They require persistent connections

   Explanation: Each serverless function invocation is isolated and does not retain data from previous runs, making them stateless by design. They do process data for each run but do not keep it afterward. Storing data locally or needing persistent connections does not fit the serverless execution style.

5. Serverless Use Case Example

   Which scenario is best suited for serverless functions in cloud environments?

   1. Hosting a long-running web server
   2. Real-time image resizing after file upload
   3. Continuous batch data processing
   4. Running a full-featured database

   Explanation: Handling tasks like resizing images immediately after upload fits serverless models well, using event-driven and short-lived execution. Continuous batch processing and long-running web servers are better with dedicated or containerized servers. Serverless is not intended for hosting databases, which require persistent storage and processes.

6. Cold Start in Serverless Computing

   What does a 'cold start' refer to in serverless cloud functions?

   1. Initial setup latency before execution
   2. Data entering a cold storage tier
   3. A function running before it's uploaded
   4. A server losing power unexpectedly

   Explanation: Cold start means the brief delay when a serverless function starts for the first time, as underlying resources are set up. It's not about running functions before upload or data moving to cold storage. An unexpected server power loss is unrelated to how serverless functions launch.

7. State Management in Serverless

   How should persistent state be managed when using serverless deployment models?

   1. Keep state in local function variables
   2. Store state in external storage services
   3. Ignore persistent state needs
   4. Store state in volatile RAM only

   Explanation: Since serverless functions are stateless, persistent data must be stored externally, such as in managed databases or file storage. Variables inside a function or in RAM are temporary and lost after execution. Ignoring persistent needs leads to data loss.

8. Resource Limits for Functions

   Which statement best describes resource limits of serverless cloud functions?

   1. They have strict limits on runtime duration
   2. Resource use is always unlimited
   3. There are never any memory limits
   4. They run indefinitely if needed

   Explanation: Serverless functions are subject to maximum runtime and memory limits to ensure fair usage and platform efficiency. Unlimited runtime or memory would disrupt shared infrastructure. Functions are designed for short tasks rather than indefinite running.

9. Supported Programming Languages

   Which languages are typically supported by cloud serverless function services?

   1. Spreadsheet formulas
   2. Only assembly and COBOL
   3. Common languages like Python, JavaScript, and Java
   4. Proprietary machine code only

   Explanation: Cloud providers support popular high-level languages such as Python, JavaScript, and Java for serverless functions. Assembly, COBOL, or proprietary code are rarely supported. Spreadsheet formulas are not used for programming cloud functions.

10. Function Deployment Process

    What is an essential step when deploying a serverless function to a cloud platform?

    1. Sending source code to the platform
    2. Editing BIOS firmware
    3. Configuring a physical server
    4. Burning code onto optical media

    Explanation: Deploying a serverless function requires uploading your source code or packaged application to the cloud provider's service. There is no need to configure hardware or physical servers, and tasks like burning to optical media or editing firmware are unrelated to cloud deployments.