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| Section | Weight | Objectives |
|---|---|---|
| Design Resilient Architectures | 26% | - Highly available architecture
|
| Design High-Performing Architectures | 24% | - Compute and storage optimization
|
| Design Secure Architectures | 30% | - Secure workloads and applications
|
| Design Cost-Optimized Architectures | 20% | - Cost-effective resource selection
|
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NEW QUESTION # 516
A company uses Amazon EC2 instances to host its internal systems. As part of a deployment operation, an administrator tries to use the AWS CLI to terminate an EC2 instance.
However, the administrator receives a 403 (Access Denied) error message. The administrator is using an IAM role that has the following IAM policy attached:
What is the cause of the unsuccessful request?
Answer: D
Explanation:
The IAM policy includes two statements:
Allow ec2:TerminateInstances on all resources
Deny ec2:TerminateInstances if the request does NOT come from:
192.0.2.0/24
203.0.113.0/24
AWS IAM policy evaluation rules state:
Explicit Deny always overrides Allow.
A request must meet all applicable conditions in the policy to be granted.
aws: SourceIp condition applies when IAM actions are invoked via the public AWS APIs.
In this scenario, the administrator is not originating the request from one of the allowed CIDR blocks, so the Deny condition is triggered, overriding the Allow statement.
Therefore, the operation is blocked with:
403 AccessDenied: explicit deny
This matches the behavior described in the AWS IAM Policy Evaluation Logic used in the Security Pillar of the Well-Architected Framework:
Explicit Deny > Allow > Default Deny
IP-based Conditions restrict API calls originating outside approved network ranges Options A/B/C do not accurately reflect this explicit Deny condition behavior.
NEW QUESTION # 517
[Design High-Performing Architectures]
A company hosts more than 300 global websites and applications. The company requires a platform to analyze more than 30 TB of clickstream data each day.
What should a solutions architect do to transmit and process the clickstream data?
Answer: A
Explanation:
https://aws.amazon.com/es/blogs/big-data/real-time-analytics-with-amazon-redshift-streaming-ingestion/
NEW QUESTION # 518
A company stores data for multiple business units in a single Amazon S3 bucket that is in the company's payer AWS account. To maintain data isolation, the business units store data in separate prefixes in the S3 bucket by using an S3 bucket policy. The company plans to add a large number of dynamic prefixes. The company does not want to rely on a single S3 bucket policy to manage data access at scale. The company wants to develop a secure access management solution in addition to the bucket policy to enforce prefix-level data isolation. Which solution will meet these requirements?
Answer: A
Explanation:
Why Option C is Correct:
S3 Access Points: Provide scalable management of access to large datasets with specific permissions for individual prefixes.
Dynamic Prefixes: Access points simplify managing access to a growing number of prefixes without relying solely on a single bucket policy.
Fine-Grained Control: Resource-based permissions on access points enforce prefix-level isolation effectively.
Why Other Options Are Not Ideal:
Option A: Using deny/allow bucket policies introduces complexity and is less scalable for dynamic prefixes.
Option B: Encryption ensures data security but does not address access management.
Option D: Pre-signed URLs are temporary and not suitable for managing access at scale.
NEW QUESTION # 519
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?
Answer: D
Explanation:
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.
Explanation:
Reference:
https://docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/Aurora.Overview.Endpoints.html Amazon Aurora Overview:
https://youtu.be/iwS1h7rLNBQ
Check out this Amazon Aurora Cheat Sheet:
https://tutorialsdojo.com/amazon-aurora/
NEW QUESTION # 520
A company has developed an API using an Amazon API Gateway REST API and AWS Lambda functions. The API serves static and dynamic content to users worldwide. The company wants to decrease the latency of transferring content for API requests.
Options:
Answer: B
Explanation:
A . Edge-optimized API + Caching:Reduces latency by using Amazon CloudFront for edge locations and enables caching for dynamic content. Compression reduces data transfer latency.
B . Regional API + Caching:Increases latency for global users due to the lack of edge locations.
C . Edge-optimized API + Reserved Concurrency:Reserved concurrency ensures Lambda availability but does not address latency for dynamic content.
D . Regional API + Reserved Concurrency:Lacks edge optimization, increasing latency for global users.
NEW QUESTION # 521
......
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