Amazon Web Services SAP-C02 Questions Answers

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Question 28

A video processing company wants to build a machine learning (ML) model by using 600 TB of compressed data that is stored as thousands of files in the company's on-premises network attached storage system. The company does not have the necessary compute resources on premises for ML experiments and wants to use AWS.

The company needs to complete the data transfer to AWS within 3 weeks. The data transfer will be a one-time transfer. The data must be encrypted in transit. The measured upload speed of the company's internet connection is 100 Mbps, and multiple departments share the connection.

Which solution will meet these requirements MOST cost-effectively?

Options:

Order several AWS Snowball Edge Storage Optimized devices by using the AWS Management Console. Configure the devices with a destination S3 bucket. Copy the data to the devices. Ship the devices back to AWS.

Set up a 10 Gbps AWS Direct Connect connection between the company location and the nearest AWS Region. Transfer the data over a VPN connection into the Region to store the data in Amazon S3.

Create a VPN connection between the on-premises network storage and the nearest AWS Region. Transfer the data over the VPN connection.

Deploy an AWS Storage Gateway file gateway on premises. Configure the file gateway with a destination S3 bucket. Copy the data to the file gateway.

Question 29

A company is hosting a critical application on a single Amazon EC2 instance. The application uses an Amazon ElastiCache for Redis single-node cluster for an in-memory data store. The application uses an Amazon RDS for MariaDB DB instance for a relational database. For the application to function, each piece of the infrastructure must be healthy and must be in an active state.

A solutions architect needs to improve the application's architecture so that the infrastructure can automatically recover from failure with the least possible downtime.

Which combination of steps will meet these requirements? (Select THREE.)

Options:

Use an Elastic Load Balancer to distribute traffic across multiple EC2 instances. Ensure that the EC2 instances are part of an Auto Scaling group that has a minimum capacity of two instances.

Use an Elastic Load Balancer to distribute traffic across multiple EC2 instances Ensure that the EC2 instances are configured in unlimited mode.

Modify the DB instance to create a read replica in the same Availability Zone. Promote the read replica to be the primary DB instance in failure scenarios.

Modify the DB instance to create a Multi-AZ deployment that extends across two Availability Zones.

Create a replication group for the ElastiCache for Redis cluster. Configure the cluster to use an Auto Scaling group that has a minimum capacity of two instances.

Create a replication group for the ElastiCache for Redis cluster. Enable Multi-AZ on the cluster.

Question 30

A company runs a Python script on an Amazon EC2 instance to process data. The script runs every 10 minutes. The script ingests files from an Amazon S3 bucket and processes the files. On average, the script takes approximately 5 minutes to process each file The script will not reprocess a file that the script has already processed.

The company reviewed Amazon CloudWatch metrics and noticed that the EC2 instance is idle for approximately 40% of the time because of the file processing speed. The company wants to make the workload highly available and scalable. The company also wants to reduce long-term management overhead.

Which solution will meet these requirements MOST cost-effectively?

Options:

Migrate the data processing script to an AWS Lambda function. Use an S3 event notification to invoke the Lambda function to process the objects when the company uploads the objects.

Create an Amazon Simple Queue Service (Amazon SQS) queue. Configure Amazon S3 to send event notifications to the SQS queue. Create an EC2 Auto Scaling group with a minimum size of one instance. Update the data processing script to poll the SQS queue. Process the S3 objects that the SQS message identifies.

Migrate the data processing script to a container image. Run the data processing container on an EC2 instance. Configure the container to poll the S3 bucket for new objects and to process the resulting objects.

Migrate the data processing script to a container image that runs on Amazon Elastic Container Service (Amazon ECS) on AWS Fargate. Create an AWS Lambda function that calls the Fargate RunTaskAPI operation when the container processes the file. Use an S3 event notification to invoke the Lambda function.

Question 31

A life sciences company is using a combination of open source tools to manage data analysis workflows and Docker containers running on servers in its on-premises data center to process genomics data Sequencing data is generated and stored on a local storage area network (SAN), and then the data is processed. The research and development teams are running into capacity issues and have decided to re-architect their genomics analysis platform on AWS to scale based on workload demands and reduce the turnaround time from weeks to days

The company has a high-speed AWS Direct Connect connection Sequencers will generate around 200 GB of data for each genome, and individual jobs can take several hours to process the data with ideal compute capacity. The end result will be stored in Amazon S3. The company is expecting 10-15 job requests each day

Which solution meets these requirements?

Options:

Use regularly scheduled AWS Snowball Edge devices to transfer the sequencing data into AWS When AWS receives the Snowball Edge device and the data is loaded into Amazon S3 use S3 events to trigger an AWS Lambda function to process the data

Use AWS Data Pipeline to transfer the sequencing data to Amazon S3 Use S3 events to trigger an Amazon EC2 Auto Scaling group to launch custom-AMI EC2 instances running the Docker containers to process the data

Use AWS DataSync to transfer the sequencing data to Amazon S3 Use S3 events to trigger an AWS Lambda function that starts an AWS Step Functions workflow Store the Docker images in Amazon Elastic Container Registry (Amazon ECR) and trigger AWS Batch to run the container and process the sequencing data

Use an AWS Storage Gateway file gateway to transfer the sequencing data to Amazon S3 Use S3 events to trigger an AWS Batch job that runs on Amazon EC2 instances running the Docker containers to process the data