AWS Certified Associate SAA-C03 Exam Questions and Answers PDF

The solution that will reduce the launch time of the application during the next testing phase is to launch the EC2 On-Demand Instances with hibernation turned on and configure EC2 Auto Scaling warm pools. This solution allows the application to resume from a hibernated state instead of starting from scratch, which can save time and resources. Hibernation preserves the memory (RAM) state of the EC2 instances to the root EBS volume and then stops the instances. When the instances are resumed, they restore their memory state from the EBS volume and become productive quickly. EC2 Auto Scaling warm pools can be used to maintain a pool of pre-initialized instances that are ready to scale out when needed. Warm pools can also support hibernated instances, which can further reduce the launch time and cost of scaling out.

The other solutions are not as effective as the first one because they either do not reduce the launch time, do not guarantee availability, or do not use On-Demand Instances as required. Launching two or more EC2 On-Demand Instances with auto scaling features does not reduce the launch time of the application, as each instance still has to go through the initialization process. Launching EC2 Spot Instances does not guarantee availability, as Spot Instances can be interrupted by AWS at any time when there is a higher demand for capacity. Launching EC2 On-Demand Instances with Capacity Reservations does not reduce the launch time of the application, as it only ensures that there is enough capacity available for the instances, but does not pre-initialize them.

References:

Hibernating your instance - Amazon Elastic Compute Cloud

Warm pools for Amazon EC2 Auto Scaling - Amazon EC2 Auto Scaling

Amazon Kinesis Data Streams is a scalable and reliable service that can ingest, buffer, and process streaming data in real-time. It can handle large traffic spikes and preserve the order of the incoming data records. AWS Lambda is a serverless compute service that can process the data streams from Kinesis Data Streams without requiring any infrastructure management. It can also scale automatically to match the throughput of the data stream. Amazon DynamoDB is a fully managed, highly available, and fast NoSQL database that can store the processed updates from Lambda. It can also handle high write throughput and provide consistent performance. By using these services, the solutions architect can design a solution that meets the requirements of the company with the least operational overhead.

This solution meets the following requirements:

It is the least effort, as it does not require any additional AWS services, custom scripts, or data processing steps. Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. You can use Athena to query CloudTrail logs directly from the S3 bucket where they are stored, without any data loading or transformation. You can also use the AWS Management Console, the AWS CLI, or the Athena API to run and manage your queries.

It is effective, as it allows you to filter, aggregate, and join CloudTrail log data using SQL syntax. You can use various SQL functions and operators to specify the criteria for identifying Access Denied and Unauthorized errors, such as the error code, the user identity, the event source, the event name, the event time, and the resource ARN. You can also use subqueries, views, and common table expressions to simplify and optimize your queries.

It is flexible, as it allows you to customize and save your queries for future use. You can also export the query results to other formats, such as CSV or JSON, or integrate them with other AWS services, such as Amazon QuickSight, for further analysis and visualization.

References:

Querying AWS CloudTrail Logs - Amazon Athena

Analyzing Data in S3 using Amazon Athena | AWS Big Data Blog

Troubleshoot IAM permisson access denied or unauthorized errors | AWS re:Post

The company wants to deploy its containerized application workloads to a VPC across three Availability Zones, with high availability and minimal changes to the application. The solution that will meet these requirements with the least operational overhead is:

Use Amazon Elastic Container Service (Amazon ECS). Amazon ECS is a fully managed container orchestration service that allows you to run and scale containerized applications on AWS. Amazon ECS eliminates the need for you to install, operate, and scale your own cluster management infrastructure. Amazon ECS also integrates with other AWS services, such as VPC, ELB, CloudFormation, CloudWatch, IAM, and more.

Configure Amazon ECS Service Auto Scaling to use target tracking scaling. Amazon ECS Service Auto Scaling allows you to automatically adjust the number of tasks in your service based on the demand or custom metrics. Target tracking scaling is a policy type that adjusts the number of tasks in your service to keep a specified metric at a target value. For example, you can use target tracking scaling to maintain a target CPU utilization or request count per task for your service.

Set the minimum capacity to 3. This ensures that your service always has at least three tasks running across three Availability Zones, providing high availability and fault tolerance for your application.

Set the task placement strategy type to spread with an Availability Zone attribute. This ensures that your tasks are evenly distributed across the Availability Zones in your cluster, maximizing the availability of your service.

This solution will provide high availability across Availability Zones, require minimal changes to the application, and reduce the operational overhead of managing your own cluster infrastructure.

References:

Amazon Elastic Container Service

Amazon ECS Service Auto Scaling

Target Tracking Scaling Policies for Amazon ECS Services

Amazon ECS Task Placement Strategies