Exam AWS Certified Developer - Associate DVA-C02 topic 1 question 74 discussion

You need to set the reserved concurrency to 1 otherwise multiple functions could run at the same time causing the math to be off. Also there was a similar question in another practice exam set that stated the same thing

By using ElastiCache, the Lambda function can store the values of the previous messages it has received, which can be used to calculate an accurate rolling average.

Reserved Concurrency of 1 ensures that only one instance of the Lambda function runs at a time, which is crucial for calculating an accurate rolling average. If multiple instances of the Lambda function run simultaneously (without concurrency control), each instance might have incomplete or inconsistent data, leading to incorrect results for the rolling average. Storing the rolling average in Amazon ElastiCache provides a persistent, in-memory storage that the function can access quickly during each invocation. ElastiCache can store the current state of the rolling average and any relevant historical data, ensuring that each invocation of the Lambda function has access to the most recent values for calculation

Correct answer should be D. Storing values in the function's layers and using them during initialization is straightforward. This Approach avoids external services like Elasticache.

elasticache for keeping the values from previous invocation

A y C limitan mucho la capacidad de Lamda. Voy con B

A is the correct answer.

Selected Answer: B As per Gemini below is why A is incorrect A. Reserved concurrency: Setting reserved concurrency to 1 ensures only one instance of the function executes at a time. While this might prevent race conditions, it doesn't address the core issue of calculating the rolling average across multiple Lambda invocations.

While limiting concurrency (A or C) can help manage the function's execution rate and scale and prevent throttling, it is not directly related to ensuring the accuracy of calculating rolling average. Instead, focusing on proper state management and data consistency mechanisms (using ElastiCache) is key to achieving accurate results in this scenario.

Option A ("Set the function's reserved concurrency to 1. Calculate the rolling average in the function. Store the calculated rolling average in Amazon ElastiCache.") is the most suitable solution. It ensures that only one instance of the Lambda function processes messages at any given time, maintaining the sequence of message processing which is crucial for an accurate rolling average calculation. Additionally, using Amazon ElastiCache to store and retrieve the rolling average across invocations addresses the statelessness of AWS Lambda, enabling stateful processing.

What if one of the instances freezes and holds one of the vzlues for some time, not updating cache, while the others continue calculatinv the avg giving wrong output ?

By storing individual message values in ElastiCache (a fast, in-memory data store), the Lambda function can retrieve these values upon initialization to calculate an accurate rolling average. This approach effectively maintains state across Lambda invocations.

Using ElastiCache allows you to maintain a shared state across all instances of your Lambda function

This approach controls concurrency by ensuring only one instance runs at a time. Provisioned concurrency also has the added benefit of reducing cold start latency. Storing the rolling average in ElastiCache is a good practice for maintaining state. However, like option A, it may limit the function's throughput.

Both options A and B provide valid approaches to address potential issues, but they have different trade-offs. Option A focuses on limiting concurrency, while Option B suggests using a caching solution to store and retrieve intermediate values. If avoiding concurrency problems is a top priority and the function's execution time is not a concern, Option A could be a suitable choice. However, if you are looking for a solution that allows for better scalability and doesn't impose strict concurrency limitations, Option B with Amazon ElastiCache provides a more scalable and distributed approach.

A is correct

Another tricky question. I go with A mainly because ElastiCache is mainly used along with databases. See this link https://docs.aws.amazon.com/lambda/latest/dg/lambda-concurrency.html#reserved-and-provisioned Pulled from AWS Website --> What is Amazon ElastiCache? Amazon ElastiCache allows you to seamlessly set up, run, and scale an in-memory cache in the cloud. ElastiCache is compatible with both Redis and Memcached. Boost your application performance and achieve microsecond latency by caching alongside your existing databases. ElastiCache is a popular choice for real-time use cases like caching, session stores, gaming, geo-spatial services, real-time analytics, and queuing.