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NEW QUESTION # 895
A company used an Amazon RDS for MySQL DB instance during application testing. Before terminating the DB instance at the end of the test cycle, a solutions architect created two backups. The solutions architect created the first backup by using the mysqldump utility to create a database dump. The solutions architect created the second backup by enabling the final DB snapshot option on RDS termination.
The company is now planning for a new test cycle and wants to create a new DB instance from the most recent backup. The company has chosen a MySQL-compatible edition of Amazon Aurora to host the DB instance.
Which solutions will create the new DB instance? (Select TWO.)
Answer: A,B
Explanation:
These answers are correct because they meet the requirements of creating a new DB instance from the most recent backup and using a MySQL-compatible edition of Amazon Aurora to host the DB instance. You can import the RDS snapshot directly into Aurora if the MySQL DB instance and the Aurora DB cluster are running the same version of MySQL. For example, you can restore a MySQL version 5.6 snapshot directly to Aurora MySQL version 5.6, but you can't restore a MySQL version 5.6 snapshot directly to Aurora MySQL version 5.7. This method is simple and requires the fewest number of steps. You can upload the database dump to Amazon S3 and then import the database dump into Aurora if the MySQL DB instance and the Aurora DB cluster are running different versions of MySQL. For example, you can import a MySQLversion
5.6 database dump into Aurora MySQL version 5.7, but you can't restore a MySQL version 5.6 snapshot directly to Aurora MySQL version 5.7. This method is more flexible and allows you to migrate across different versions of MySQL.
References:
https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraMySQL.Migrating.RDSMySQL.
Import.html
https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/AuroraMySQL.Migrating.RDSMySQL.
Dump.html
NEW QUESTION # 896
A company has two AWS accounts: Production and Development. The company needs to push code changes in the Development account to the Production account. In the alpha phase, only two senior developers on the development team need access to the Production account. In the beta phase, more developers will need access to perform testing.
Which solution will meet these requirements?
Answer: D
Explanation:
Understanding the Requirement: Developers in the Development account need to push code changes to the Production account, with phased access control for different stages of the project.
Analysis of Options:
Policy Documents in Each Account: This approach increases complexity and is harder to manage compared to role-based access.
IAM Role in Development Account: Roles in the Development account cannot directly control access to resources in the Production account.
IAM Role in Production Account: Creating a role in the Production account with a trust policy that allows the Development account to assume it provides controlled, secure access.
IAM Group in Production Account: This approach does not provide the required cross-account access control.
Best Solution:
IAM Role in the Production Account: This method allows precise control over who can access the Production account from the Development account, with the ability to manage permissions and access levels effectively.
References:
IAM Roles with Cross-Account Access
Creating a Role for Cross-Account Access
NEW QUESTION # 897
A company uses an AWS Transfer for SFTP public server endpoint and Amazon S3 storage to host large datasets for its customers. The company provides customers SSH private keys to authenticate and download their datasets. The Transfer for SFTP server is configured with structured logging that is saved to an S3 bucket. The company wants to charge customers based on their monthly data download usage. Which solution will meet these requirements?
Answer: D
Explanation:
Comprehensive and Detailed Step-by-Step
To accurately charge customers based on their monthly data download usage, the following solution is recommended:
Structured Logging Configuration:
Action:Ensure that the AWS Transfer for SFTP server is configured to log user activity, including details about file downloads, to Amazon S3 in a structured format.
Implementation:Utilize AWS Transfer Family's structured logging feature to capture detailed information about user sessions, including actions performed and data transferred.
docs.aws.amazon.com
Justification:Structured logs provide comprehensive data necessary for analyzing customer-specific download activities.
Data Analysis with Amazon Athena:
Action:Use Amazon Athena to run SQL queries on the structured log data stored in the S3 bucket to calculate the amount of data each customer has downloaded.
Implementation:
a.Define a Schema:Create a table in Athena that maps to the structure of your log files. This involves specifying the format of the logs and the location in S3.
b.Query Data:Write SQL queries to sum the total bytes downloaded by each customer over the billing period. This can be achieved by filtering logs based on user identifiers and summing the data transfer amounts.
Justification:Athena allows for efficient querying
NEW QUESTION # 898
A company runs a Node.js function on a server in its on-premises data center. The data center stores data in a PostgreSQL database. The company stores the credentials in a connection string in an environment variable on the server. The company wants to migrate its application to AWS and to replace the Node.js application server with AWS Lambda. The company also wants to migrate to Amazon RDS for PostgreSQL and to ensure that the database credentials are securely managed. Which solution will meet these requirements with the LEAST operational overhead?
Answer: B
Explanation:
AWS Secrets Manager is designed specifically to securely store and manage sensitive information such as database credentials. It integrates seamlessly with AWS services like Lambda and RDS, and it provides automatic credential rotation with minimal operational overhead. AWS Secrets Manager: By storing the database credentials in Secrets Manager, you ensure that the credentials are securely stored, encrypted, and managed. Secrets Manager provides a built-in mechanism to automatically rotate credentials at regular intervals (e.g., every
30 days), which helps in maintaining security best practices without requiring additional manual intervention. Lambda Integration: The Lambda function can be easily configured to retrieve the credentials from Secrets Manager using the AWS SDK, ensuring that the credentials are accessed securely at runtime.
Why Not Other Options?
Option A (Parameter Store with Rotation): While Parameter Store can store parameters securely, Secrets Manager is more tailored for secrets management and automatic rotation, offering more features and less operational overhead.
Option C (Encrypted Lambda environment variable): Storing credentials directly in Lambda environment variables, even when encrypted, requires custom code to manage rotation, which increases operational complexity.
Option D (KMS with automatic rotation): KMS is for managing encryption keys, not for storing and rotating secrets like database credentials. This option would require more custom implementation to manage credentials securely.
NEW QUESTION # 899
A manufacturing company has EC2 instances running in AWS. The EC2 instances are configured with Auto Scaling. There are a lot of requests being lost because of too much load on the servers. The Auto Scaling is launching new EC2 instances to take the load accordingly yet, there are still some requests that are being lost.
Which of the following is the MOST suitable solution that you should implement to avoid losing recently submitted requests?
Answer: D
Explanation:
Amazon Simple Queue Service (SQS) is a fully managed message queuing service that makes it easy to decouple and scale microservices, distributed systems, and serverless applications. Building applications from individual components that each perform a discrete function improves scalability and reliability, and is best practice design for modern applications. SQS makes it simple and cost-effective to decouple and coordinate the components of a cloud application. Using SQS, you can send, store, and receive messages between software components at any volume, without losing messages or requiring other services to be always available.
The number of messages in your Amazon SQS queue does not solely define the number of instances needed. In fact, the number of instances in the fleet can be driven by multiple factors, including how long it takes to process a message and the acceptable amount of latency (queue delay).
The solution is to use a backlog per instance metric with the target value being the acceptable backlog per instance to maintain. You can calculate these numbers as follows:
Backlog per instance: To determine your backlog per instance, start with the Amazon SQS metric ApproximateNumberOfMessages to determine the length of the SQS queue (number of messages available for retrieval from the queue). Divide that number by the fleet's running capacity, which for an Auto Scaling group is the number of instances in the InService state, to get the backlog per instance.
Acceptable backlog per instance: To determine your target value, first calculate what your application can accept in terms of latency. Then, take the acceptable latency value and divide it by the average time that an EC2 instance takes to process a message.
To illustrate with an example, let's say that the current ApproximateNumberOfMessages is 1500 and the fleet's running capacity is 10. If the average processing time is 0.1 seconds for each message and the longest acceptable latency is 10 seconds then the acceptable backlog per instance is 10 / 0.1, which equals 100. This means that 100 is the target value for your target tracking policy. Because the backlog per instance is currently at 150 (1500 / 10), your fleet scales out by five instances to maintain proportion to the target value.
Hence, the correct answer is: Use an Amazon SQS queue to decouple the application components and scale-out the EC2 instances based upon the ApproximateNumberOfMessages metric in Amazon CloudWatch.
Replacing the Auto Scaling group with a cluster placement group to achieve a low-latency network performance necessary for tightly-coupled node-to-node communication is incorrect because although it is true that a cluster placement group allows you to achieve a low-latency network performance, you still need to use Auto Scaling for your architecture to add more EC2 instances.
Using larger instances for your application with an attached Elastic Fabric Adapter (EFA) is incorrect because using a larger EC2 instance would not prevent data from being lost in case of a larger spike.
You can take advantage of the durability and elasticity of SQS to keep the messages available for consumption by your instances. Elastic Fabric Adapter (EFA) is simply a network interface for Amazon EC2 instances that enables customers to run applications requiring high levels of inter-node communications at scale on AWS.
Setting up Amazon Aurora Serverless for on-demand, auto-scaling configuration of your EC2 Instances and also enabling Amazon Aurora Parallel Query feature for faster analytical queries over your current data is incorrect because although the Amazon Aurora Parallel Query feature provides faster analytical queries over your current data, Amazon Aurora Serverless is an on-demand, auto-scaling configuration for your database, and NOT for your EC2 instances. This is actually an auto-scaling configuration for your Amazon Aurora database and not for your compute services. References:
https://aws.amazon.com/sqs/
https://docs.aws.amazon.com/AWSSimpleQueueService/latest/SQSDeveloperGuide/welcome.html
https://docs.aws.amazon.com/autoscaling/ec2/userguide/as-using-sqs-queue.html Check out this Amazon SQS Cheat Sheet:
https://tutorialsdojo.com/amazon-sqs/
NEW QUESTION # 900
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