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Lambda function limitations

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AWS Lambda comes with certain limitations for function execution, such as memory, execution duration (by default up to 15 minutes), function environment variables (limited storage), disk space (including libraries and dependencies), and concurrent invocation limits (by default 1000 per region). The…

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AWS Certified Solutions Architect - Lambda function limitations Example Questions

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Question 1

An e-commerce application uses AWS Lambda to process large data files uploaded to Amazon S3. Some files cause a single Lambda invocation to exceed the 15-minute maximum execution time. The company must keep a fully serverless design and continue using Lambda for processing. What should the developer do to reliably complete the processing?

Correct Answer: Split data files and process them using multiple Lambda functions concurrently

AWS Lambda has a hard maximum timeout of 15 minutes per invocation. To stay within a serverless architecture and continue using Lambda, the correct approach is to break the workload into smaller tasks that each complete within the 15-minute limit and run them in parallel. Splitting the large data files and processing chunks concurrently with multiple Lambda functions enables horizontal scaling and ensures each invocation stays under the timeout. Increasing the timeout beyond 15 minutes is impossible, switching to EC2 violates the requirement to remain serverless, and simply increasing memory allocation is not a reliable or guaranteed fix for workloads that inherently exceed the time limit.

Question 2

A Lambda function triggered by S3 PutObject events generates image thumbnails. During peak periods, users report slower thumbnail creation. CloudWatch shows p95 duration rising from 800 ms to 2.4 s, zero throttles, concurrent executions peaking at 150 (well below the account limit), and cold starts under 2%. The function performs CPU-bound image resizing with no external calls. Without changing the code, which configuration change will most effectively reduce per-invocation processing time?

Correct Answer: Increase the memory allocation of the Lambda function

AWS Lambda allocates CPU shares and network throughput in proportion to the configured memory. For CPU-bound workloads like image resizing, increasing the memory setting directly increases the available CPU, which reduces per-invocation duration without any code changes. In the stated scenario, there are no throttles, concurrency is below limits, and cold starts are minimal, so the bottleneck is compute, not scaling or startup. Increasing package size does not improve runtime performance. Decreasing concurrency would reduce throughput and does not speed up individual executions. Decreasing memory would reduce CPU shares and make the function slower.

Question 3

An application using Lambda functions to process API requests is running into issues handling regional disaster scenarios. What should be done to improve the robustness of the system?

Correct Answer: Deploy the Lambda functions in multiple AWS Regions

The correct solution to handle regional disaster scenarios is to deploy the Lambda functions in multiple AWS Regions. This allows for higher reliability and redundancy in case of a region-wide outage. Increasing the timeout, memory allocation, concurrency limit, or package size would not address the issue, and changing the invocation type would not affect regional disaster scenarios.

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