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NEW QUESTION 47
A company is running an online transaction processing (OLTP) workload on AWS. This workload uses an unencrypted Amazon RDS DB instance in a Multi-AZ deployment. Daily database snapshots are taken from this instance.
What should a solutions architect do to ensure the database and snapshots are always encrypted moving forward?
- A. Copy the snapshots to an Amazon S3 bucket that is encrypted using server-side encryption with AWS Key Management Service (AWS KMS) managed keys (SSE-KMS)
- B. Create a new encrypted Amazon Elastic Block Store (Amazon EBS) volume and copy the snapshots to it Enable encryption on the DB instance
- C. Copy the snapshots and enable encryption using AWS Key Management Service (AWS KMS) Restore encrypted snapshot to an existing DB instance
- D. Encrypt a copy of the latest DB snapshot. Replace existing DB instance by restoring the encrypted snapshot
https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/USER_RestoreFromSnapshot.html#USER_RestoreFromSnapshot.CON Under “Encrypt unencrypted resources” – https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/EBSEncryption.html
NEW QUESTION 48
An online shopping platform is hosted on an Auto Scaling group of Spot EC2 instances and uses Amazon Aurora PostgreSQL as its database. There is a requirement to optimize your database workloads in your cluster where you have to direct the write operations of the production traffic to your high-capacity instances and point the reporting queries sent by your internal staff to the low-capacity instances.
Which is the most suitable configuration for your application as well as your Aurora database cluster to achieve this requirement?
- A. Do nothing since by default, Aurora will automatically direct the production traffic to your high- capacity instances and the reporting queries to your low-capacity instances.
- B. Configure your application to use the reader endpoint for both production traffic and reporting queries, which will enable your Aurora database to automatically perform load-balancing among all the Aurora Replicas.
- C. Create a custom endpoint in Aurora based on the specified criteria for the production traffic and another custom endpoint to handle the reporting queries.
- D. In your application, use the instance endpoint of your Aurora database to handle the incoming production traffic and use the cluster endpoint to handle reporting queries.
Amazon Aurora typically involves a cluster of DB instances instead of a single instance. Each connection is handled by a specific DB instance. When you connect to an Aurora cluster, the host name and port that you specify point to an intermediate handler called an endpoint. Aurora uses the endpoint mechanism to abstract these connections. Thus, you don’t have to hardcode all the hostnames or write your own logic for load-balancing and rerouting connections when some DB instances aren’t available.
For certain Aurora tasks, different instances or groups of instances perform different roles. For example, the primary instance handles all data definition language (DDL) and data manipulation language (DML) statements. Up to 15 Aurora Replicas handle read-only query traffic.
Using endpoints, you can map each connection to the appropriate instance or group of instances based on your use case. For example, to perform DDL statements you can connect to whichever instance is the primary instance. To perform queries, you can connect to the reader endpoint, with Aurora automatically performing load-balancing among all the Aurora Replicas. For clusters with DB instances of different capacities or configurations, you can connect to custom endpoints associated with different subsets of DB instances. For diagnosis or tuning, you can connect to a specific instance endpoint to examine details about a specific DB instance.
The custom endpoint provides load-balanced database connections based on criteria other than the read-only or read-write capability of the DB instances. For example, you might define a custom endpoint to connect to instances that use a particular AWS instance class or a particular DB parameter group.
Then you might tell particular groups of users about this custom endpoint. For example, you might direct internal users to low-capacity instances for report generation or ad hoc (one-time) querying, and direct production traffic to high-capacity instances. Hence, creating a custom endpoint in Aurora based on the specified criteria for the production traffic and another custom endpoint to handle the reporting queries is the correct answer.
Configuring your application to use the reader endpoint for both production traffic and reporting queries, which will enable your Aurora database to automatically perform load-balancing among all the Aurora Replicas is incorrect. Although it is true that a reader endpoint enables your Aurora database to automatically perform load-balancing among all the Aurora Replicas, it is quite limited to doing read operations only. You still need to use a custom endpoint to load-balance the database connections based on the specified criteria.
The option that says: In your application, use the instance endpoint of your Aurora database to handle the incoming production traffic and use the cluster endpoint to handle reporting queries is incorrect because a cluster endpoint (also known as a writer endpoint) for an Aurora DB cluster simply connects to the current primary DB instance for that DB cluster. This endpoint can perform write operations in the database such as DDL statements, which is perfect for handling production traffic but not suitable for handling queries for reporting since there will be no write database operations that will be sent.
Moreover, the endpoint does not point to lower-capacity or high-capacity instances as per the requirement. A better solution for this is to use a custom endpoint.
The option that says: Do nothing since by default, Aurora will automatically direct the production traffic to your high-capacity instances and the reporting queries to your low-capacity instances is incorrect because Aurora does not do this by default. You have to create custom endpoints in order to accomplish this requirement.
https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Overview.Endpoints.html Amazon Aurora Overview:
Check out this Amazon Aurora Cheat Sheet:
NEW QUESTION 49
A solutions architect must design a solution that uses Amazon CloudFront with an Amazon S3 origin to store a static website. The company’s security policy requires that all website traffic be inspected by AWS WAR How should the solutions architect comply with these requirements?
- A. Configure a security group that allows Amazon CloudFront IP addresses to access Amazon S3 only. Associate AWS WAF to CloudFront.
- B. Configure Amazon CloudFront and Amazon S3 to use an origin access identity (OAI) to restrict access to the S3 bucket. Enable AWS WAF on the distribution.
- C. Configure an S3 bucket policy lo accept requests coming from the AWS WAF Amazon Resource Name (ARN) only.
- D. Configure Amazon CloudFront to forward all incoming requests to AWS WAF before requesting content from the S3 origin.
NEW QUESTION 50
A company is implementing a new business application. The application runs on two Amazon EC2 instances and uses an Amazon S3 bucket for document storage. A solutions architect needs to ensure that the EC2 instances can access the S3 bucket.
What should the solutions architect do to meet this requirement?
- A. Create an 1AM policy that grants access to the S3 bucket. Attach the policy to the EC2 instances.
- B. Create an 1AM group that grants access to the S3 bucket. Attach the group to the EC2 instances.
- C. Create an 1AM role that grants access to the S3 bucket. Attach the role to the EC2 instances.
- D. Create an 1AM user that grants access to the S3 bucket. Attach the user account to the EC2 instances.
NEW QUESTION 51
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