Amazon Web Services passleader pdf Sap-c02 Study Guide by Alicia q42 vce pdf

Page: 23 / 31

Exam Name:	AWS Certified Solutions Architect - Professional
Exam Code:	SAP-C02 Dumps
Vendor:	Amazon Web Services	Certification:	AWS Certified Professional
Questions:	435 Q&A's	Shared By:	alicia

Question 92

A company uses an AWS CodeCommit repository The company must store a backup copy of the data that is in the repository in a second AWS Region

Which solution will meet these requirements?

Options:

Configure AWS Elastic Disaster Recovery to replicate the CodeCommit repository data to the second Region

Use AWS Backup to back up the CodeCommit repository on an hourly schedule Create a cross-Region copy in the second Region

Create an Amazon EventBridge rule to invoke AWS CodeBuild when the company pushes code to the repository Use CodeBuild to clone the repository Create a zip file of the content Copy the file to an S3 bucket in the second Region

Create an AWS Step Functions workflow on an hourly schedule to take a snapshot of the CodeCommit repository Configure the workflow to copy the snapshot to an S3 bucket in the second Region

Discussion

Ace

No problem! I highly recommend Cramkey Dumps to anyone looking to pass their certification exams. They will help you feel confident and prepared on exam day. Good luck!

Harris (not set)

That sounds amazing. I'll definitely check them out. Thanks for the recommendation!

Pippa

I was so happy to see that almost all the questions on the exam were exactly what I found in their Dumps.

Anastasia (not set)

You are right…It was amazing! The Cramkey Dumps were so comprehensive and well-organized, it made studying for the exam a breeze.

Norah

Cramkey is highly recommended.

Zayan (not set)

Definitely. If you're looking for a reliable and effective study resource, look no further than Cramkey Dumps. They're simply wonderful!

Laila

They're such a great resource for anyone who wants to improve their exam results. I used these dumps and passed my exam!! Happy customer, always prefer. Yes, same questions as above I know you guys are perfect.

Keira (not set)

100% right….And they're so affordable too. It's amazing how much value you get for the price.

Question 93

A company is running an application in the AWS Cloud. The application collects and stores a large amount of unstructured data in an Amazon S3 bucket. The S3 bucket contains several terabytes of data and uses the S3 Standard storage class. The data increases in size by several gigabytes every day.

The company needs to query and analyze the data. The company does not access data that is more than 1-year-old. However, the company must retain all the data indefinitely for compliance reasons.

Which solution will meet these requirements MOST cost-effectively?

Options:

Use S3 Select to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.

Use Amazon Redshift Spectrum to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.

Use an AWS Glue Data Catalog and Amazon Athena to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Glacier Deep Archive.

Use Amazon Redshift Spectrum to query the data. Create an S3 Lifecycle policy to transition data that is more than 1 year old to S3 Intelligent-Tiering.

Discussion

Question 94

A solutions architect is redesigning a three-tier application that a company hosts on premises. The application provides personalized recommendations based on user profiles. The company already has an AWS account and has configured a VPC to host the application.

The frontend is a Java-based application that runs in on-premises VMs. The company hosts a personalization model on a physical application server and uses TensorFlow to implement the model. The personalization model uses artificial intelligence and machine learning (AI/ML). The company stores user information in a Microsoft SQL Server database. The web application calls the personalization model, which reads the user profiles from the database and provides recommendations.

The company wants to migrate the redesigned application to AWS.

Which solution will meet this requirement with the LEAST operational overhead?

Options:

Use AWS Server Migration Service (AWS SMS) to migrate the on-premises physical application server and the web application VMs to AWS. Use AWS Database Migration Service (AWS DMS) to migrate the SQL Server database to Amazon RDS for SQL Server.

Export the personalization model. Store the model artifacts in Amazon S3. Deploy the model to Amazon SageMaker and create an endpoint. Host the Java application in AWS Elastic Beanstalk. Use AWS Database Migration Service {AWS DMS) to migrate the SQL Server database to Amazon RDS for SQL Server.

Use AWS Application Migration Service to migrate the on-premises personalization model and VMs to Amazon EC2 instances in Auto Scaling groups. Use AWS Database Migration Service (AWS DMS) to migrate the SQL Server database to an EC2 instance.

Containerize the personalization model and the Java application. Use Amazon Elastic Kubernetes Service (Amazon EKS) managed node groups to deploy the model and the application to Amazon EKS Host the node groups in a VPC. Use AWS Database Migration Service (AWS DMS) to migrate the SQL Server database to Amazon RDS for SQL Server.

Discussion

Answer:

Explanation:

Explanation:

Amazon SageMaker is a fully managed machine learning service that allows users to build, train, and deploy machine learning models quickly and easily1. Users can export their existing TensorFlow models and store the model artifacts in Amazon S3, a highly scalable and durable object storage service2. Users can then deploy the model to Amazon SageMaker and create an endpoint that can be invoked by the web application to provide recommendations3. This way, the solution can leverage the AI/ML capabilities of Amazon SageMaker without having to rewrite the personalization model.

AWS Elastic Beanstalk is a service that allows users to deploy and manage web applications without worrying about the infrastructure that runs those applications. Users can host their Java application in AWS Elastic Beanstalk and configure it to communicate with the Amazon SageMaker endpoint. This way, the solution can reduce the operational overhead of managing servers, load balancers, scaling, and application health monitoring.

AWS Database Migration Service (AWS DMS) is a service that helps users migrate databases to AWS quickly and securely. Users can use AWS DMS to migrate their SQL Server database to Amazon RDS for SQL Server, a fully managed relational database service that offers high availability, scalability, security, and compatibility. This way, the solution can reduce the operational overhead of managing database servers, backups, patches, and upgrades.

Option A is incorrect because using AWS Server Migration Service (AWS SMS) to migrate the on-premises physical application server and the web application VMs to AWS is not cost-effective or scalable. AWS SMS is a service that helps users migrate on-premises workloads to AWS. However, for this use case, migrating the physical application server and the web application VMs to AWS will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk and Amazon RDS.

Option C is incorrect because using AWS Application Migration Service to migrate the on-premises personalization model and VMs to Amazon EC2 instances in Auto Scaling groups is not cost-effective or scalable. AWS Application Migration Service is a service that helps users migrate applications from on-premises or other clouds to AWS without making any changes to their applications. However, for this use case, migrating the personalization model and VMs to EC2 instances will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk and Amazon RDS.

Option D is incorrect because containerizing the personalization model and the Java application and using Amazon Elastic Kubernetes Service (Amazon EKS) managed node groups to deploy them to Amazon EKS is not necessary or cost-effective. Amazon EKS is a service that allows users to run Kubernetes on AWS without needing to install, operate, and maintain their own Kubernetes control plane or nodes. However, for this use case, containerizing and deploying the personalization model and the Java application will not take advantage of the AI/ML capabilities of Amazon SageMaker or the managed services of AWS Elastic Beanstalk. Moreover, using S3 Glacier Deep Archive as a storage class for images will incur a high retrieval fee and latency for accessing them.

Question 95

A company runs an loT application in the AWS Cloud. The company has millions of sensors that collect data from houses in the United States. The sensors use the MOTT protocol to connect and send data to a custom MQTT broker. The MQTT broker stores the data on a single Amazon EC2 instance. The sensors connect to the broker through the domain named iot.example.com. The company uses Amazon Route 53 as its DNS service. The company stores the data in Amazon DynamoDB.

On several occasions, the amount of data has overloaded the MOTT broker and has resulted in lost sensor data. The company must improve the reliability of the solution.

Which solution will meet these requirements?

Options:

Create an Application Load Balancer (ALB) and an Auto Scaling group for the MOTT broker. Use the Auto Scaling group as the target for the ALB. Update the DNS record in Route 53 to an alias record. Point the alias record to the ALB. Use the MQTT broker to store the data.

Set up AWS loT Core to receive the sensor data. Create and configure a custom domain to connect to AWS loT Core. Update the DNS record in Route 53 to point to the AWS loT Core Data-ATS endpoint. Configure an AWS loT rule to store the data.

Create a Network Load Balancer (NLB). Set the MQTT broker as the target. Create an AWS Global Accelerator accelerator. Set the NLB as the endpoint for the accelerator. Update the DNS record in Route 53 to a multivalue answer record. Set the Global Accelerator IP addresses as values. Use the MQTT broker to store the data.

Set up AWS loT Greengrass to receive the sensor data. Update the DNS record in Route 53 to point to the AWS loT Greengrass endpoint. Configure an AWS loT rule to invoke an AWS Lambda function to store the data.