Amazon Web Services Updated SAA-C03 Exam Questions and Answers by amari

To meet the requirement of deleting objects older than 3 years while retaining certain data, this solution leverages serverless technologies to minimize operational overhead.

S3 Inventory: S3 Inventory provides a flat file that lists all the objects in an S3 bucket and their metadata, which can be configured to include data such as the last modified date. This inventory can be generated daily or weekly.

AWS Lambda Function: A Lambda function can be created to process the S3 Inventory report, filtering out the objects that need to be retained and identifying those that should be deleted.

S3 Batch Operations: S3 Batch Operations can execute tasks such as object deletion at scale. By invoking the Lambda function through S3 Batch Operations, you can automate the process of deleting the identified objects, ensuring that the solution is serverless and requires minimal operational management.

Why Not Other Options?:

Option A (AWS CLI script on EC2): Running a script on an EC2 instance adds unnecessary operational overhead and is not serverless.

Option B (AWS Batch): AWS Batch is designed for running large-scale batch computing workloads, which is overkill for this scenario.

Option C (AWS Glue + script): AWS Glue is more suited for ETL tasks, and this approach would add unnecessary complexity compared to the serverless Lambda solution.

AWS References:

Amazon S3 Inventory - Information on how to set up and use S3 Inventory.

S3 Batch Operations - Documentation on how to perform bulk operations on S3 objects using S3 Batch Operations.

This solution addresses the scalability needs of the workload while preventing failed processing attempts due to resource constraints.

Amazon S3 event notifications can be used to trigger a message to an SQS queue whenever a new video is uploaded.

The existing Auto Scaling group of EC2 instances can poll the SQS queue, ensuring that the EC2 instances only process videos when there is a job in the queue.

SQS decouples the video upload and processing steps, allowing the system to handle sudden spikes in video uploads without overloading EC2 instances.

The use of Auto Scaling ensures that the EC2 instances can scale in or out based on the demand, maintaining cost efficiency while avoiding processing failures due to insufficient resources.

AWS References:

S3 Event Notifications details how to configure notifications for S3 events.

Amazon SQS is a fully managed message queuing service that decouples components of the system.

Auto Scaling EC2 explains how to manage automatic scaling of EC2 instances based on demand.

Why the other options are incorrect:

A. AWS Lambda functions: While Lambda can handle some workloads, video processing is often resource-intensive and long-running, making EC2 a more suitable solution.

B. Using SNS with Lambda: Similar to A, Lambda is not ideal for large-scale video processing due to its time and memory limitations.

D. AWS Step Functions: While a valid orchestration solution, this introduces more complexity and overhead compared to the simpler SQS-based solution.

Memory Optimized Instances: These instances are designed to deliver fast performance for workloads that process large data sets in memory. They are ideal for high-performance databases like SAP and applications with high memory utilization.

High Memory Utilization: Both the SAP application and the SQL Server database have high memory demands as per the on-premises performance data. Memory optimized instances provide the necessary memory capacity and performance.

Instance Types:

For the SAP application, using a memory optimized instance ensures the application has sufficient memory to handle the high workload efficiently.

For the SQL Server database, memory optimized instances ensure optimal database performance with high memory throughput.

Operational Efficiency: Using the same instance family for both the application and the database simplifies management and ensures both components meet performance requirements.

References:

Amazon EC2 Instance Types

SAP on AWS

This solution is the most cost-effective and scalable for analyzing large amounts of web traffic logs.

Amazon S3: Storing the logs in Amazon S3 is highly scalable, durable, and cost-effective. S3 is designed to handle large-scale data storage, which is ideal for storing tens of gigabytes of log data generated daily by multiple websites.

Amazon Athena: Athena is a serverless, interactive query service that allows you to analyze data in S3 using standard SQL. It works directly with the data stored in S3, so there’s no need to load the data into a database, which saves on costs and reduces complexity. Athena charges based on the amount of data scanned by queries, making it a cost-effective solution for on-demand analysis that only occurs once a week.

Why Not Other Options?:

Option B (Amazon RDS): Storing logs in a relational database like Amazon RDS would be more expensive due to the storage and I/O costs associated with RDS. Additionally, it would require more management overhead.

Option C (Amazon OpenSearch Service): OpenSearch is a good option for full-text search and analytics on log data, but it might be more costly and complex to manage compared to the simplicity and cost-effectiveness of Athena for periodic SQL-based queries.

Option D (Amazon EMR): While EMR can handle large-scale data processing, it involves more operational overhead and might be overkill for the type of ad-hoc, SQL-based analysis required here. Additionally, EMR costs can be higher due to the need to maintain a cluster.

AWS References:

Amazon S3 - Information on how to store and manage data in Amazon S3.

Amazon Athena - Documentation on using Amazon Athena for querying data stored in S3 using SQL.