The execution model of AWS Lambda ensures __________ and __________ for functions.
- Fixed resource allocation, high latency
- Manual intervention, resource constraints
- Predictable execution time, low throughput
- Scalability, fault tolerance
AWS Lambda's execution model ensures scalability by automatically scaling resources based on demand and fault tolerance by handling failures transparently.
AWS Lambda optimizes __________ to reduce latency and improve performance.
- Code complexity
- Data storage costs
- Invocation overhead
- Networking bandwidth
AWS Lambda optimizes invocation overhead to minimize the time it takes for functions to start executing in response to events, reducing overall latency.
Scenario: Your team is experiencing increased cold start times in AWS Lambda functions. What strategies would you recommend to mitigate this issue?
- Adjusting VPC settings
- Increasing function memory
- Pre-warming Lambda functions
- Reducing function timeout
Pre-warming Lambda functions helps keep them warm, reducing cold start times when real events trigger them, thus mitigating the issue effectively.
Scenario: You need to ensure optimal resource allocation for a highly concurrent workload in AWS Lambda. What approach would you take to achieve this?
- Fine-tune memory allocation
- Increase function timeout
- Limit concurrency settings
- Reduce function memory
Limiting concurrency settings helps ensure optimal resource allocation by controlling the number of concurrent executions, thus effectively handling highly concurrent workloads in AWS Lambda.
What are runtimes in the context of AWS Lambda?
- Authentication mechanisms
- Data storage options
- Execution environments for code
- Networking protocols
Runtimes in AWS Lambda refer to the execution environments where your code runs. These environments include preconfigured software and settings necessary to execute functions.
What is the importance of considering language runtime compatibility when developing Lambda functions?
- It ensures compatibility with third-party libraries
- It improves function security
- It reduces function cost
- It simplifies function deployment
Considering language runtime compatibility is crucial as it ensures that Lambda functions can utilize third-party libraries and dependencies supported by the chosen runtime.
Custom runtimes in AWS Lambda allow developers to use ________ languages not officially supported by AWS.
- Custom
- Non-standard
- Third-party
- Unconventional
Custom runtimes in AWS Lambda enable the use of non-standard languages, extending the platform's flexibility beyond officially supported ones.
Upgrading to a newer version of a runtime in AWS Lambda may introduce ________ and compatibility issues.
- Breakages
- Bugs
- Errors
- Incompatibilities
Upgrading to a newer runtime version in AWS Lambda may introduce breakages and compatibility issues, necessitating thorough testing before deployment.
Scenario: You need to develop a machine learning model using AWS Lambda. Which runtime option would you choose and why?
- Go runtime
- Java runtime
- Node.js runtime
- Python with TensorFlow runtime
Python with TensorFlow runtime is a suitable choice for developing machine learning models on AWS Lambda, as it provides the necessary libraries and frameworks for training and inference tasks.
Which of the following programming languages is NOT supported as a runtime for AWS Lambda?
- COBOL
- Java
- Python
- Ruby
COBOL is not supported as a runtime for AWS Lambda. AWS Lambda primarily supports modern programming languages like Python, Node.js, Java, and others.