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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Design High-Performing Architectures | 24% | - Compute and storage optimization
|
| Topic 2: Design Resilient Architectures | 26% | - Fault tolerance and recovery
|
| Topic 3: Design Secure Architectures | 30% | - Secure workloads and applications
|
| Topic 4: Design Cost-Optimized Architectures | 20% | - Cost-effective resource selection
|
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質問 # 484
A company has an automobile sales website that stores its listings in a database on Amazon RDS When an automobile is sold the listing needs to be removed from the website and the data must be sent to multiple target systems.
Which design should a solutions architect recommend?
正解：B
解説：
https://docs.aws.amazon.com/lambda/latest/dg/services-rds.htmlhttps://docs.aws.amazon.com/lambda/latest/dg
/with-sns.html
質問 # 485
A financial service company has a two-tier consumer banking application. The frontend serves static web content. The backend consists of APIs. The company needs to migrate the frontendcomponent to AWS. The backend of the application will remain on premises. The company must protect the application from common web vulnerabilities and attacks. Which solution will meet these requirements with the LEAST operational overhead?
正解：D
解説：
Comprehensive Deploying the frontend as a CloudFront distribution with multiple origins provides an efficient and scalable solution. Using WAF rules with CloudFront protects against web vulnerabilities, while the multi-origin configuration allows traffic routing to the on-premises backend APIs. This approach minimizes operational overhead compared to managing EC2 instances.
質問 # 486
A company has an Amazon S3 data lake The company needs a solution that transforms the data from the data lake and loads the data into a data warehouse every day The data warehouse must have massively parallel processing (MPP) capabilities.
Data analysts then need to create and train machine learning (ML) models by using SQL commands on the data The solution must use serverless AWS services wherever possible Which solution will meet these requirements?
正解：C
解説：
AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy to prepare and load your data for analytics. AWS Glue can automatically discover your data in Amazon S3 and catalog it, so you can query and search the data using SQL. AWS Glue can also run serverless ETL jobs using Apache Spark and Python to transform and load your data into various destinations, such as Amazon Redshift, Amazon Athena, or Amazon Aurora. AWS Glue is a serverless service, so you only pay for the resources consumed by the jobs, and you don't need to provision or manage any infrastructure.
Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables you to use standard SQL and your existing business intelligence (BI) tools to analyze your data. Amazon Redshift also supports massively parallel processing (MPP), which means it can distribute and execute queries across multiple nodes in parallel, delivering fast performance and scalability. Amazon Redshift Serverless is a new option that automatically scales query compute capacity based on the queries being run, so you don't need to manage clusters or capacity. You only pay for the query processing time and the storage consumed by your data.
Amazon Redshift ML is a feature that enables you to create, train, and deploy machine learning (ML) models using familiar SQL commands. Amazon Redshift ML can automatically discover the best model and hyperparameters for your data, and store the model in Amazon SageMaker, a fully managed service that provides a comprehensive set of tools for building, training, and deploying ML models. You can then use SQL functions to apply the model to your data in Amazon Redshift and generate predictions.
The combination of AWS Glue, Amazon Redshift Serverless, and Amazon Redshift ML meets the requirements of the question, as it provides a serverless, scalable, and SQL-based solution to transform, load, and analyze the data from the Amazon S3 data lake, and to create and train ML models on the data.
Option A is not correct, because Amazon EMR is not a serverless service. Amazon EMR is a managed service that simplifies running Apache Spark, Apache Hadoop, and other big data frameworks on AWS. Amazon EMR requires you to launch and configure clusters of EC2 instances to run your ETL jobs, which adds complexity and cost compared to AWS Glue.
Option B is not correct, because Amazon Aurora Serverless is not a data warehouse service, and it does not support MPP. Amazon Aurora Serverless is an on-demand, auto-scaling configuration for Amazon Aurora, a relational database service that is compatible with MySQL and PostgreSQL. Amazon Aurora Serverless can automatically adjust the database capacity based on the traffic, but it does not distribute the data and queries across multiple nodes like Amazon Redshift does. Amazon Aurora Serverless is more suitable for transactional workloads than analytical workloads.
Option D is not correct, because Amazon Athena is not a data warehouse service, and it does not support MPP.
Amazon Athena is an interactive query service that enables you to analyze data in Amazon S3 using standard SQL. Amazon Athena is serverless, so you only pay for the queries you run, and you don't need to load the data into a database. However, Amazon Athena does not store the data in a columnar format, compress the data, or optimize the query execution plan like Amazon Redshift does. Amazon Athena is more suitable for ad-hoc queries than complex analytics and ML.
References:
* AWS Glue
* Amazon Redshift
* Amazon Redshift Serverless
* Amazon Redshift ML
* Amazon EMR
* Amazon Aurora Serverless
* Amazon Athena
質問 # 487
A company has an Amazon S3 data lake that is governed by AWS Lake Formation The company wants to create a visualization in Amazon QuickSight by joining the data in the data lake with operational data that is stored in an Amazon Aurora MySQL database The company wants to enforce column-level authorization so that the company's marketing team can access only a subset of columns in the database Which solution will meet these requirements with the LEAST operational overhead?
正解：A
質問 # 488
[Design Secure Architectures]
A company needs to save the results from a medical trial to an Amazon S3 repository. The repository must allow a few scientists to add new files and must restrict all other users to read-only access. No users can have the ability to modify or delete any files in the repository. The company must keep every file in the repository for a minimum of 1 year after its creation date.
Which solution will meet these requirements?
正解：C
解説：
In compliance mode, a protected object version can't be overwritten or deleted by any user, including the root user in your AWS account. When an object is locked in compliance mode, its retention mode can't be changed, and its retention period can't be shortened. Compliance mode helps ensure that an object version can't be overwritten or deleted for the duration of the retention period. In governance mode, users can't overwrite or delete an object version or alter its lock settings unless they have special permissions. With governance mode, you protect objects against being deleted by most users, but you can still grant some users permission to alter the retention settings or delete the object if necessary. In Governance mode, Objects can be deleted by some users with special permissions, this is against the requirement.
Compliance:
- Object versions can't be overwritten or deleted by any user, including the root user
- Objects retention modes can't be changed, and retention periods can't be shortened Governance:
- Most users can't overwrite or delete an object version or alter its lock settings
- Some users have special permissions to change the retention or delete the object
質問 # 489
......
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