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A solutions architect is developing a multiple-subnet VPC architecture. The solution will consist of six subnets in two Availability Zones. The subnets are defined as public, private and dedicated for databases. Only the Amazon EC2 instances running in the private subnets should be able to access a database. Which solution meets these requirements?
A company creates operations data and stores the data in an Amazon S3 bucket for the company's annual audit, an external consultant needs to access an annual report that is stored in the S3 bucket. The external consultant needs to access the report for 7 days. The company must implement a solution to allow the external consultant access to only the report. Which solution will meet these requirements with the MOST operational efficiency?
A company stores data in an on-premises Oracle relational database. The company needs to make the data available in Amazon Aurora PostgreSQL for analysis The company uses an AWS Site-to-Site VPN connection to connect its on-premises network to AWS. The company must capture the changes that occur to the source database during the migration to Aurora PostgreSQL. Which solution will meet these requirements?
A development team uses multiple AWS accounts for its development, staging, and production environments. Team members have been launching large Amazon EC2 instances that are underutilized. A solutions architect must prevent large instances from being launched in all accounts. How can the solutions architect meet this requirement with the LEAST operational overhead?
A company hosts its multi-tier, public web application in the AWS Cloud. The web application runs on Amazon EC2 instances, and its database runs on Amazon RDS. The company is anticipating a large increase in sales during an upcoming holiday weekend. A solutions architect needs to build a solution to analyze the performance of the web application with a granularity of no more than 2 minutes. What should the solutions architect do to meet this requirement?
A company stores customer data in a multitenant Amazon S3 bucket. Each customer's data is stored in a prefix that is unique to the customer. The company needs to migrate data for specific customers to a new. dedicated S3 bucket that is in the same AWS Region as the source bucket. The company must preserve object metadata such as creation date and version IDs. After the migration is finished, the company must delete the source data for the migrated customers from the original multitenant S3 bucket. Which combination of solutions will meet these requirements with the LEAST overhead? (Select THREE.)
A company is implementing a new application on AWS. The company will run the application on multiple Amazon EC2 instances across multiple Availability Zones within multiple AWS Regions. The application will be available through the internet. Users will access the application from around the world. The company wants to ensure that each user who accesses the application is sent to the EC2 instances that are closest to the user's location. Which solution will meet these requirements?
A company hosts a web application on multiple Amazon EC2 instances The EC2 instances are in an Auto Scaling group that scales in response to user demand The company wants to optimize cost savings without making a long-term commitment Which EC2 instance purchasing option should a solutions architect recommend to meet these requirements'?
A company recently signed a contract with an AWS Managed Service Provider (MSP) Partner for help with an application migration initiative. A solutions architect needs to share an Amazon Machine Image (AMI) from an existing AWS account with the MSP Partner's AWS account. The AMI is backed by Amazon Elastic Block Store (Amazon EBS) and uses a customer managed customer master key (CMK) to encrypt EBS volume snapshots. What is the MOST secure way for the solutions architect to share the AMI with the MSP Partner's AWS account?
A financial services company plans to launch a new application on AWS to handle sensitive financial transactions. The company will deploy the application on Amazon EC2 instances. The company will use Amazon RDS for MySQL as the database. The company's security policies mandate that data must be encrypted at rest and in transit. Which solution will meet these requirements with the LEAST operational overhead?

Question 688 - SAA-C03 discussion

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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?

A.
Run a daily Amazon EMR job to transform the data and load the data into Amazon Redshift Use Amazon Redshift ML to create and train the ML models
Answers
A.
Run a daily Amazon EMR job to transform the data and load the data into Amazon Redshift Use Amazon Redshift ML to create and train the ML models
B.
Run a daily Amazon EMR job to transform the data and load the data into Amazon Aurora Serverless Use Amazon Aurora ML to create and train the ML models
Answers
B.
Run a daily Amazon EMR job to transform the data and load the data into Amazon Aurora Serverless Use Amazon Aurora ML to create and train the ML models
C.
Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift Serverless Use Amazon Redshift ML to create and tram the ML models
Answers
C.
Run a daily AWS Glue job to transform the data and load the data into Amazon Redshift Serverless Use Amazon Redshift ML to create and tram the ML models
D.
Run a daily AWS Glue job to transform the data and load the data into Amazon Athena tables Use Amazon Athena ML to create and train the ML models
Answers
D.
Run a daily AWS Glue job to transform the data and load the data into Amazon Athena tables Use Amazon Athena ML to create and train the ML models
Suggested answer: C

Explanation:

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.

AWS Glue

Amazon Redshift

Amazon Redshift Serverless

Amazon Redshift ML

Amazon EMR

Amazon Aurora Serverless

Amazon Athena

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asked 16/09/2024
Kevin Boddy
31 questions
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