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Die AWS Certified Solutions Architect - Associate Zertifizierung wird in der IT-Branche sehr geschätzt, insbesondere für Personen, die mit AWS arbeiten. Diese Zertifizierung zeigt, dass die Person die notwendigen Fähigkeiten und Kenntnisse hat, um skalierbare, sichere und zuverlässige Systeme auf AWS zu entwerfen und bereitzustellen. Die Zertifizierung eröffnet auch neue Karrieremöglichkeiten und höhere Gehälter für die Person.

Die AWS-Solutions-Associate (AWS Certified Solutions Architect - Associate (SAA-C02)) Zertifizierungsprüfung ist eine branchenweit anerkannte Zertifizierung, die die Expertise einer Person in der Gestaltung und Bereitstellung skalierbarer, hochverfügbarer und fehlertoleranter Systeme auf Amazon Web Services (AWS) validiert. Die Prüfung ist für Personen gedacht, die Erfahrung in der Gestaltung und Bereitstellung von Anwendungen auf AWS haben und ihre Fähigkeiten und Kenntnisse in diesem Bereich validieren möchten. Die Zertifizierungsprüfung umfasst eine Vielzahl von AWS-Diensten, einschließlich Compute, Storage, Networking, Security und Database Services.

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Die Zertifizierungsprüfung der Amazon AWS-Solutions-Architect-Associate (AWS Certified Solutions Architect-Associate (SAA-C02)) ist eine äußerst wertvolle Zertifizierung für IT-Fachkräfte, die an einer Karriere im Cloud Computing interessiert sind. Diese Zertifizierung wurde entwickelt, um die Fähigkeiten und Kenntnisse zu validieren, die zum Entwerfen, Bereitstellen und Betrieb von Anwendungen und Infrastruktur in der AWS -Cloud (Amazon Web Services) erforderlich sind.

Amazon AWS Certified Solutions Architect - Associate (SAA-C03) AWS-Solutions-Associate Prüfungsfragen mit Lösungen (Q403-Q408):

403. Frage
Your company is in the process of developing a next generation pet collar that collects biometric information to assist families with promoting healthy lifestyles for their pets Each collar will push 30kb of biometric data In JSON format every 2 seconds to a collection platform that will process and analyze the data providing health trending information back to the pet owners and veterinarians via a web portal Management has tasked you to architect the collection platform ensuring the following requirements are met.
Provide the ability for real-time analytics of the inbound biometric data
Ensure processing of the biometric data is highly durable. Elastic and parallel
The results of the analytic processing should be persisted for data mining
Which architecture outlined below win meet the initial requirements for the collection platform?

A. Utilize Amazon Kinesis to collect the inbound sensor data, analyze the data with Kinesis clients and save the results to a Redshift cluster using EMR.
B. Utilize S3 to collect the inbound sensor data analyze the data from S3 with a daily scheduled Data Pipeline and save the results to a Redshift Cluster.
C. Utilize SQS to collect the inbound sensor data analyze the data from SQS with Amazon Kinesis and save the results to a Microsoft SQL Server RDS instance.
D. Utilize EMR to collect the inbound sensor data, analyze the data from EUR with Amazon Kinesis and save me results to DynamoDB.

Antwort: A

404. Frage
While creating the snapshots using the API, which Action should I be using?

A. MakeSnapShot
B. FreshSnapshot
C. CreateSnapshot
D. DeploySnapshot

Antwort: C

405. Frage
An organization has two Amazon EC2 instances:
* The first is running an ordering application and an inventory application.
* The second is running a queuing system.
During certain times of the year, several thousand orders are placed per second. Some orders were lost when the queuing system was down. Also, the organization's inventory application has the incorrect quantity of products because some orders were processed twice.
What should be done to ensure that the applications can handle the increasing number of orders?

A. Put the ordering and inventory applications into their own AWS Lambda functions. Have the ordering application write the messages into an Amazon SQS FIFO queue.
B. Put the ordering and inventory applications into their own Amazon ECS containers, and create an Auto Scaling group for each application. Then, deploy the message queuing server in multiple Availability Zones.
C. Put the ordering and inventory applications into their own Amazon EC2 instances, and create an Auto Scaling group for each application. Use Amazon SQS standard queues for the incoming orders, and implement idempotency in the inventory application.
D. Put the ordering and inventory applications into their own Amazon EC2 instances. Write the incoming orders to an Amazon Kinesis data stream Configure AWS Lambda to poll the stream and update the inventory application.

Antwort: C

406. Frage
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
B. 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
C. 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
D. 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

Antwort: B

Begründung:
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

407. Frage
A gaming company uses Amazon DynamoDB to store user information such as geographic location, player data, and leaderboards. The company needs to configure continuous backups to an Amazon S3 bucket with a minimal amount of coding. The backups must not affect availability of the application and must not affect the read capacity units (RCUs) that are defined for the table Which solution meets these requirements?

A. Export the data directly from DynamoDB to Amazon S3 with continuous backups. Turn on point-in- time recovery for the table.
B. Create an AWS Lambda function to export the data from the database tables to Amazon S3 on a regular basis. Turn on point-in-time recovery for the table.
C. Use an Amazon EMR cluster. Create an Apache Hive job to back up the data to Amazon S3.
D. Configure Amazon DynamoDB Streams. Create an AWS Lambda function to consume the stream and export the data to an Amazon S3 bucket.

Antwort: A

Begründung:
https://aws.amazon.com/blogs/database/dynamodb-streams-use-cases-and-design-patterns/
https://aws.amazon.com/premiumsupport/knowledge-center/back-up-dynamodb-s3/

408. Frage
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