Amazon Web Services 2cram all Saa-c03 Test Inside Amazon Web Services Questions by Elspeth q241 vce pdf

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Exam Name:	AWS Certified Solutions Architect - Associate (SAA-C03)
Exam Code:	SAA-C03 Dumps
Vendor:	Amazon Web Services	Certification:	AWS Certified Associate
Questions:	1168 Q&A's	Shared By:	elspeth

Question 132

A company wants to measure the effectiveness of its recent marketing campaigns. The company performs batch processing on csv files of sales data and stores the results in an Amazon S3 bucket once every hour. The S3 bi petabytes of objects. The company runs one-time queries in Amazon Athena to determine which products are most popular on a particular date for a particular region Queries sometimes fail or take longer than expected to finish.

Which actions should a solutions architect take to improve the query performance and reliability? (Select TWO.)

Options:

Reduce the S3 object sizes to less than 126 MB

Partition the data by date and region n Amazon S3

Store the files as large, single objects in Amazon S3.

Use Amazon Kinosis Data Analytics to run the Queries as pan of the batch processing operation

Use an AWS duo extract, transform, and load (ETL) process to convert the csv files into Apache Parquet format.

Discussion

Question 133

A company hosts its core network services, including directory services and DNS, in its on-premises data center. The data center is connected to the AWS Cloud using AWS Direct Connect (DX). Additional AWS accounts are planned that will require quick, cost-effective, and consistent access to these network services.

What should a solutions architect implement to meet these requirements with the LEAST amount of operational overhead?

Options:

Create a DX connection in each new account. Route the network traffic to the on-premises servers.

Configure VPC endpoints in the DX VPC for all required services. Route the network traffic to the on-premises servers.

Create a VPN connection between each new account and the DX VPC. Route the network traffic to the on-premises servers.

Configure AWS Transit Gateway between the accounts. Assign DX to the transit gateway and route network traffic to the on-premises servers.

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Question 134

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?

Options:

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

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

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

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

Discussion

Answer:

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.

[References:, AWS Glue, Amazon Redshift, Amazon Redshift Serverless, Amazon Redshift ML, Amazon EMR, Amazon Aurora Serverless, Amazon Athena, , , ]

Question 135

A company uses GPS trackers to document the migration patterns of thousands of sea turtles. The trackers check every 5 minutes to see if a turtle has moved more than 100 yards (91.4 meters). If a turtle has moved, its tracker sends the new coordinates to a web application running on three Amazon EC2 instances that are in multiple Availability Zones in one AWS Region.

Jgpently. the web application was overwhelmed while processing an unexpected volume of tracker data. Data was lost with no way to replay the events. A solutions

ftitect must prevent this problem from happening again and needs a solution with the least operational overhead.

at should the solutions architect do to meet these requirements?

Options:

Create an Amazon S3 bucket to store the data. Configure the application to scan for new data in the bucket for processing.

Create an Amazon API Gateway endpoint to handle transmitted location coordinates. Use an AWS Lambda function to process each item concurrently.

Create an Amazon Simple Queue Service (Amazon SOS) queue to store the incoming data. Configure the application to poll for new messages for processing.

Create an Amazon DynamoDB table to store transmitted location coordinates. Configure the application to query the table for new data for processing. Use TTL to remove data that has been processed.