MCQs on Optimizing Lambda Performance | AWS Lambda Quiz
AWS Lambda offers a scalable solution for running serverless applications, but optimizing performance is crucial for cost efficiency and better execution. This set of AWS Lambda MCQ questions and answers covers critical topics like Lambda execution time optimization, reducing cold starts, optimizing memory and CPU, and best practices for Lambda code. These questions will help you understand how to enhance Lambda function performance and prepare for exams or real-world implementation.
Lambda Execution Time Optimization (10 MCQs)
- What is a common way to optimize AWS Lambda execution time?
a) Use larger instance types
b) Reduce the size of the function code
c) Use asynchronous invocations
d) Minimize the function’s payload size - Which AWS feature helps monitor Lambda execution time for performance analysis?
a) AWS CloudWatch
b) Amazon S3
c) AWS CloudTrail
d) AWS Inspector - What is the best approach to reducing the execution time of Lambda functions?
a) Reduce the execution frequency
b) Optimize code and reduce dependencies
c) Increase the memory allocation
d) Use longer timeout settings - How does AWS Lambda automatically handle scaling to meet execution time needs?
a) By distributing functions across multiple availability zones
b) By invoking the function in parallel when needed
c) By adjusting memory automatically
d) By executing functions synchronously - Which of these is a key factor in improving Lambda performance?
a) Reducing the amount of data processed in each execution
b) Increasing the timeout limit
c) Using synchronous execution
d) Using smaller Lambda roles - What is a recommended practice for reducing Lambda execution time when interacting with a database?
a) Use connection pooling
b) Increase the database connection timeout
c) Use batch processing
d) Avoid caching data - What is the effect of increasing the memory allocation for a Lambda function?
a) It increases execution time
b) It can improve execution time by providing more CPU power
c) It reduces cost
d) It causes longer cold starts - Which of the following will most likely reduce the time it takes for a Lambda function to execute?
a) Increasing the timeout value
b) Reducing the size of the function’s deployment package
c) Reducing the memory allocation
d) Enabling retries - To optimize execution time for Lambda functions triggered by S3 events, what should be done?
a) Reduce the size of the payload processed by the Lambda
b) Use synchronous invocation
c) Use DynamoDB as the event source
d) Increase the execution timeout - In what scenario would you consider using Lambda in conjunction with Amazon API Gateway to optimize execution time?
a) When the Lambda function is called synchronously by external users
b) When you want to use event-based triggers
c) When the Lambda function processes large datasets
d) When you want to store API request logs
Reducing Cold Starts in Lambda (10 MCQs)
- What is the primary cause of cold starts in AWS Lambda?
a) Memory allocation issues
b) Large function code packages
c) The initial invocation of a function after it has been idle
d) Slow network latency - Which method is effective in reducing the cold start time for Lambda?
a) Increasing the function memory
b) Keep-alive configurations in VPC
c) Warm-up strategies
d) Increase timeout limits - What is a “warm-up” strategy for reducing Lambda cold starts?
a) Use smaller function packages
b) Pre-warm Lambda functions by invoking them at regular intervals
c) Use an event source mapping
d) Reduce IAM role complexity - What type of Lambda function is more likely to experience cold starts?
a) Functions with smaller payloads
b) Functions with complex dependencies
c) Functions that are invoked frequently
d) Functions that run synchronously - How does the use of provisioned concurrency impact Lambda cold starts?
a) It eliminates cold starts by keeping a set number of instances warm
b) It increases cold start time
c) It makes functions run slower
d) It allows automatic scaling - Which of the following is an effective strategy to reduce cold starts when using AWS Lambda with API Gateway?
a) Use VPC peering
b) Configure Lambda to run asynchronously
c) Enable provisioned concurrency
d) Use DynamoDB as an event source - What is the role of VPC configuration in reducing cold starts?
a) It has no effect on cold start times
b) It can slow down cold starts due to networking delays
c) It eliminates cold starts entirely
d) It reduces function size - How can optimizing your Lambda deployment package reduce cold starts?
a) By decreasing the function code size and dependencies
b) By using larger deployment packages
c) By enabling retries
d) By using the Lambda proxy integration - Which AWS service can help mitigate the impact of cold starts in Lambda?
a) AWS CloudWatch
b) AWS Step Functions
c) AWS S3
d) AWS Glue - What happens when AWS Lambda is invoked after a period of inactivity, leading to a cold start?
a) The function is executed faster
b) The function code is loaded from scratch
c) The function’s memory is reduced
d) The function executes synchronously
Optimizing Memory and CPU Allocation (5 MCQs)
- How does increasing the memory allocation for a Lambda function affect its CPU?
a) It reduces CPU power
b) It increases the available CPU power
c) It has no impact on CPU
d) It causes CPU throttling - What is the relationship between memory allocation and execution time in AWS Lambda?
a) More memory always reduces execution time
b) Higher memory allocation results in higher execution time
c) Memory allocation has no impact on execution time
d) Increasing memory can sometimes reduce execution time by providing more CPU - How can you optimize Lambda performance by adjusting memory and CPU allocation?
a) Increase memory until performance is optimal, as it also increases CPU power
b) Decrease memory allocation for better performance
c) Use the default memory setting for all functions
d) Allocate minimal memory to reduce cost - What should you do if a Lambda function frequently runs out of memory?
a) Decrease the timeout setting
b) Increase the memory allocation
c) Use smaller deployment packages
d) Reduce the function’s execution time - What is the impact of selecting a lower memory allocation for a Lambda function?
a) It reduces execution time
b) It may increase the cold start time
c) It improves performance
d) It results in better code performance
Best Practices for Efficient Lambda Code (5 MCQs)
- Which of the following is considered a best practice for writing efficient Lambda code?
a) Use as many dependencies as possible
b) Avoid blocking calls and synchronous operations
c) Always increase the memory allocation
d) Use synchronous execution for all functions - To reduce Lambda execution time, what should be done when dealing with external API calls?
a) Use a single API call per invocation
b) Use parallel execution to call APIs
c) Increase the timeout limit
d) Use synchronous invocation for API calls - How can you improve Lambda function performance when dealing with large data sets?
a) Use batch processing and chunking
b) Compress all data before sending it to Lambda
c) Increase the Lambda timeout limit
d) Use synchronous execution - Which of the following is a recommended practice for writing efficient Lambda code?
a) Write large functions to process multiple tasks
b) Use only one function for all Lambda triggers
c) Break large functions into smaller, more focused tasks
d) Avoid using logging in Lambda functions - What is the best way to manage dependencies in AWS Lambda?
a) Include all dependencies directly within the function code
b) Use layers to manage dependencies separately
c) Avoid using external libraries
d) Store dependencies in S3 and load them during function execution
Answers
| Qno | Answer |
|---|---|
| 1 | b) Reduce the size of the function code |
| 2 | a) AWS CloudWatch |
| 3 | b) Optimize code and reduce dependencies |
| 4 | b) By invoking the function in parallel when needed |
| 5 | a) Reducing the amount of data processed in each execution |
| 6 | a) Use connection pooling |
| 7 | b) It can improve execution time by providing more CPU power |
| 8 | b) Reducing the size of the function’s deployment package |
| 9 | a) Reduce the size of the payload processed by the Lambda |
| 10 | a) When the Lambda function is called synchronously by external users |
| 11 | c) The initial invocation of a function after it has been idle |
| 12 | c) Warm-up strategies |
| 13 | b) Pre-warm Lambda functions by invoking them at regular intervals |
| 14 | b) Functions with complex dependencies |
| 15 | a) It eliminates cold starts by keeping a set number of instances warm |
| 16 | c) Enable provisioned concurrency |
| 17 | b) It can slow down cold starts due to networking delays |
| 18 | a) By decreasing the function code size and dependencies |
| 19 | b) AWS Step Functions |
| 20 | b) The function code is loaded from scratch |
| 21 | b) It increases the available CPU power |
| 22 | d) Increasing memory can sometimes reduce execution time by providing more CPU |
| 23 | a) Increase memory until performance is optimal, as it also increases CPU power |
| 24 | b) Increase the memory allocation |
| 25 | b) It may increase the cold start time |
| 26 | b) Avoid blocking calls and synchronous operations |
| 27 | b) Use parallel execution to call APIs |
| 28 | a) Use batch processing and chunking |
| 29 | c) Break large functions into smaller, more focused tasks |
| 30 | b) Use layers to manage dependencies separately |