Data-Engineer-Associate Exam Dumps - AWS Certified Data Engineer - Associate (DEA-C01)

AWS Lambda is a serverless compute service that lets you run code without provisioning or managing servers. You can use Lambda to create functions that perform custom logic and integrate with other AWS services, such as API Gateway. Lambda automatically scales your application by running code in response to each trigger. You pay only for the compute time you consume1.

Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. You can use ECS to deploy, manage, and scale Docker containers using either Amazon EC2 instances or AWS Fargate, a serverless compute engine for containers2.

Amazon EKS is a fully managed Kubernetes service that allows you to run Kubernetes clusters on AWS without needing to install, operate, or maintain your own Kubernetes control plane. You can use EKS to deploy, manage, and scale containerized applications using Kubernetes on AWS3.

The solution that meets the requirements with the least operational overhead is to create an AWS Lambda Python function with provisioned concurrency. This solution has the following advantages:

It does not require you to provision, manage, or scale any servers or clusters, as Lambda handles all the infrastructure for you. This reduces the operational complexity and cost of running your code.

It allows you to write your Python script as a Lambda function and integrate it with API Gateway using a simple configuration. API Gateway can invoke your Lambda function synchronously or asynchronously, and return the results to the frontend website.

It ensures that your Lambda function is ready to respond to API requests without any cold start delays, by using provisioned concurrency. Provisioned concurrency is a feature that keeps your function initialized and hyper-ready to respond in double-digit milliseconds. You can specify the number of concurrent executions that you want to provision for your function.

Option A is incorrect because it requires you to deploy a custom Python script on an Amazon ECS cluster. This solution has the following disadvantages:

It requires you to provision, manage, and scale your own ECS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.

It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.

It requires you to configure your ECS cluster to integrate with API Gateway, either using an Application Load Balancer or a Network Load Balancer. This adds another layer of complexity to your architecture.

Option C is incorrect because it requires you to deploy a custom Python script that can integrate with API Gateway on Amazon EKS. This solution has the following disadvantages:

It requires you to provision, manage, and scale your own EKS cluster, either using EC2 instances or Fargate. This increases the operational complexity and cost of running your code.

It requires you to package your Python script as a Docker container image and store it in a container registry, such as Amazon ECR or Docker Hub. This adds an extra step to your deployment process.

It requires you to configure your EKS cluster to integrate with API Gateway, either using an Application Load Balancer, a Network Load Balancer, or a service of type LoadBalancer. This adds another layer of complexity to your architecture.

Option D is incorrect because it requires you to create an AWS Lambda function and ensure that the function is warm by scheduling an Amazon EventBridge rule to invoke the Lambda function every 5 minutes by using mock events. This solution has the following disadvantages:

It does not guarantee that your Lambda function will always be warm, as Lambda may scale down your function if it does not receive any requests for a long period of time. This may cause cold start delays when your function is invoked by API Gateway.

It incurs unnecessary costs, as you pay for the compute time of your Lambda function every time it is invoked by the EventBridge rule, even if it does not perform any useful work1.

1: AWS Lambda - Features

2: Amazon Elastic Container Service - Features

3: Amazon Elastic Kubernetes Service - Features

[4]: Building API Gateway REST API with Lambda integration - Amazon API Gateway

[5]: Improving latency with Provisioned Concurrency - AWS Lambda

[6]: Integrating Amazon ECS with Amazon API Gateway - Amazon Elastic Container Service

[7]: Integrating Amazon EKS with Amazon API Gateway - Amazon Elastic Kubernetes Service

[8]: Managing concurrency for a Lambda function - AWS Lambda

In AWS Glue’s SQL implementation (Spark SQL-compatible), the MERGE INTO statement supports conditional actions.

The clause WHEN MATCHED THEN DELETE deletes matching records from the target table (accounts) where the join condition is true.

“A MERGE INTO statement can perform updates, inserts, or deletes based on the match condition between source and target tables.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

In this scenario, the company is using Amazon Elastic Kubernetes Service (EKS) and wants secure access to DynamoDB without embedding credentials inside the application containers. The best practice is to use IAM roles for service accounts (IRSA), which allows assigning IAM roles to Kubernetes service accounts. This lets the EKS pods assume specific IAM roles securely, without the need to store credentials in containers.

IAM Roles for Service Accounts (IRSA):

With IRSA, each pod in the EKS cluster can assume an IAM role that grants access to DynamoDB without needing to manage long-term credentials. The IAM role can be attached to the service account associated with the pod.

This ensures least privilege access, improving security by preventing credentials from being embedded in the containers.