Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

 This solution meets the requirement of gaining access to SageMaker Studio to use AWS Glue interactive sessions. AWS Glue interactive sessions are a way to use AWS Glue DataBrew and AWS Glue Data Catalog from within SageMaker Studio. To use AWS Glue interactive sessions, the data engineer’s IAM user needs to have permissions to assume the AWS Glue service role and the SageMaker execution role. By adding a policy to the data engineer’s IAM user that includes the sts:AssumeRole action for the AWS Glue and SageMaker service principals in the trust policy, the data engineer can grant these permissions and avoid the access denied error. The other options are not sufficient or necessary to resolve the error. References:

Get started with data integration from Amazon S3 to Amazon Redshift using AWS Glue interactive sessions

Troubleshoot Errors - Amazon SageMaker

AccessDeniedException on sagemaker:CreateDomain in AWS SageMaker Studio, despite having SageMakerFullAccess

Option A is correct because Amazon Kinesis Data Streams is designed to collect and process large streams of data records in real time and is optimized for rapid, continuous data intake and aggregation. AWS states that Kinesis Data Streams is a fully managed, serverless data streaming service for ingesting streaming data at any scale and highlights event sources such as clickstream and application activity data. AWS also notes that Kinesis can pair with AWS Lambda for low-latency event processing. This directly matches the requirement to ingest millions of customer activity events with minimum latency in a scalable and durable way.

Option B is batch processing every 10 minutes, so it does not satisfy the real-time requirement. Option C depends on files landing in S3 first, which is also not the lowest-latency ingest path. Option D can support streaming, but a managed Kafka cluster with self-managed consumers introduces more operational overhead and is generally less cost-effective here than a native Kinesis + Lambda pipeline for an AWS-centric event-ingestion workload. Therefore, Kinesis Data Streams + Lambda + Redshift is the best answer.

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