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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design Cost-Optimized Architectures | 20% | - Design cost-effective compute and network solutions
|
| Topic 2: Design High-Performing Architectures | 24% | - Design high-performance compute solutions
|
| Topic 3: Design Secure Architectures | 30% | - Design secure workloads and applications
|
| Topic 4: Design Resilient Architectures | 26% | - Design decoupled and loosely coupled systems
|
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NEW QUESTION # 436
A tech company is currently using Auto Scaling for their web application. A new AMI now needs to be used for launching a fleet of EC2 instances.
Which of the following changes needs to be done?
Answer: D
Explanation:
A launch configuration is a template that an Auto Scaling group uses to launch EC2 instances. When you create a launch configuration, you specify information for the instances such as the ID of the Amazon Machine Image (AMI), the instance type, a key pair, one or more security groups, and a block device mapping. If you've launched an EC2 instance before, you specified the same information in order to launch the instance.
You can specify your launch configuration with multiple Auto Scaling groups. However, you can only specify one launch configuration for an Auto Scaling group at a time, and you can't modify a launch configuration after you've created it. Therefore, if you want to change the launch configuration for an Auto Scaling group, you must create a launch configuration and then update your Auto Scaling group with the new launch configuration.
For this scenario, you have to create a new launch configuration. Remember that you can't modify a launch configuration after you've created it.
Hence, the correct answer is: Create a new launch configuration.
The option that says: Do nothing. You can start directly launching EC2 instances in the Auto Scaling group with the same launch configuration is incorrect because what you are trying to achieve is change the AMI being used by your fleet of EC2 instances. Therefore, you need to change the launch configuration to update what your instances are using.
The option that says: create a new target group and create a new target group and launch configuration are both incorrect because you only want to change the AMI being used by your instances, and not the instances themselves. Target groups are primarily used in ELBs and not in Auto Scaling. The scenario didn't mention that the architecture has a load balancer. Therefore, you should be updating your launch configuration, not the target group.
References:
http://docs.aws.amazon.com/autoscaling/latest/userguide/LaunchConfiguration.html
https://docs.aws.amazon.com/autoscaling/ec2/userguide/AutoScalingGroup.html Check out this AWS Auto Scaling Cheat Sheet: https://tutorialsdojo.com/aws-auto-scaling/
NEW QUESTION # 437
A company runs an application on Amazon EC2 instances. The application needs to access an Amazon RDS database. The company wants to grant the EC2 instances access permissions to the RDS database while following the principle of least privilege. Which solution will meet these requirements?
Answer: B
Explanation:
The best practice for granting AWS resource access to EC2 instances is to use IAM roles, not users or long-lived access keys. You create an IAM role with a policy that grants the minimum permissions required, then attach that role to an instance profile associated with the EC2 instance. The instance then automatically receives temporary credentials for AWS service access.
NEW QUESTION # 438
A company has stored millions of objects across multiple prefixes in an Amazon S3 bucket by using the Amazon S3 Glacier Deep Archive storage class. The company needs to delete all data older than 3 years except for a subset of data that must be retained. The company has identified the data that must be retained and wants to implement a serverless solution. Which solution will meet these requirements?
Answer: B
Explanation:
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.
NEW QUESTION # 439
[Design High-Performing Architectures]
A company recently migrated a monolithic application to an Amazon EC2 instance and Amazon RDS. The application has tightly coupled modules. The existing design of the application gives the application the ability to run on only a single EC2 instance.
The company has noticed high CPU utilization on the EC2 instance during peak usage times. The high CPU utilization corresponds to degraded performance on Amazon RDS for read requests. The company wants to reduce the high CPU utilization and improve read request performance.
Which solution will meet these requirements?
Answer: B
Explanation:
To address the high CPU utilization on the EC2 instance and the degraded performance of Amazon RDS for read requests, the solution involves two key actions: resizing the EC2 instance and leveraging Amazon RDS read replicas.
Resizing the EC2 Instance: The first step is to resize the EC2 instance to a type with more CPU capacity to handle the higher computational demands during peak usage times. This helps to alleviate the immediate pressure on the CPU.
Auto Scaling Group with a Size of 1: Although the application can only run on a single EC2 instance due to its monolithic nature, creating an Auto Scaling group with a minimum and maximum size of 1 ensures that the instance is automatically restarted or replaced in case of failure, maintaining high availability.
RDS Read Replica: Configuring an RDS read replica allows the application to offload read requests to a separate instance, thus reducing the load on the primary RDS instance. This improves the performance of read operations, which were previously bottlenecked due to the high CPU usage on the EC2 instance.
Why Not Other Options?:
Option B: Redirecting all traffic to the RDS read replica is not recommended because replicas are meant for read traffic only, not for write operations. This could lead to data consistency issues.
Option C: Increasing the RDS instance type capacity helps, but it doesn't address the high CPU usage on the EC2 instance, nor does it provide a solution for scaling reads.
Option D: While resizing both the EC2 and RDS instances increases their capacities, it doesn't address the specific need to offload read traffic from the primary RDS instance.
AWS Reference:
Amazon RDS Read Replicas- Explains how to create and use read replicas to offload read traffic from the primary database instance.
Resizing Your EC2 Instance- Guidance on resizing EC2 instances to meet workload demands.
NEW QUESTION # 440
A company wants to analyze and troubleshoot Access Denied errors and Unauthonzed errors that are related to 1AM permissions The company has AWS CloudTrail turned on Which solution will meet these requirements with the LEAST effort?
Answer: D
Explanation:
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
NEW QUESTION # 441
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