Amazon Web Services techtarget pass Using Sap-c02 Exam Dumps by Luella q126 vce pdf

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Exam Name:	AWS Certified Solutions Architect - Professional
Exam Code:	SAP-C02 Dumps
Vendor:	Amazon Web Services	Certification:	AWS Certified Professional
Questions:	483 Q&A's	Shared By:	luella

Question 124

A financial services company receives a regular data feed from its credit card servicing partner Approximately 5.000 records are sent every 15 minutes in plaintext, delivered over HTTPS directly into an Amazon S3 bucket with server-side encryption. This feed contains sensitive credit card primary account number (PAN) data The company needs to automatically mask the PAN before sending the data to another S3 bucket for additional internal processing. The company also needs to remove and merge specific fields, and then transform the record into JSON format Additionally, extra feeds are likely to be added in the future, so any design needs to be easily expandable.

Which solutions will meet these requirements?

Options:

Trigger an AWS Lambda function on file delivery that extracts each record and writes it to an Amazon SQS queue. Trigger another Lambda function when new messages arrive in the SQS queue to process the records, writing the results to a temporary location in Amazon S3. Trigger a final Lambda function once the SQS queue is empty to transform the records into JSON format and send the results to another S3 bucket for internal processing.

Trigger an AWS Lambda function on file delivery that extracts each record and writes it to an Amazon SQS queue. Configure an AWS Fargate container application to automatically scale to a single instance when the SQS queue contains messages. Have the application process each record, and transform the record into JSON format. When the queue is empty, send the results to another S3 bucket for internal processing and scale down the AWS Fargate

Create an AWS Glue crawler and custom classifier based on the data feed formats and build a table definition to match. Trigger an AWS Lambda function on file delivery to start an AWS Glue ETL job to transform the entire record according to the processing and transformation requirements. Define the output format as JSON. Once complete, have the ETL job send the results to another S3 bucket for internal processing.

Create an AWS Glue crawler and custom classifier based upon the data feed formats and build a table definition to match. Perform an Amazon Athena query on file delivery to start an Amazon EMR ETL job to transform the entire record according to the processing and transformation requirements. Define the output format as JSON. Once complete, send the results to another S3 bucket for internal processing and scale down the EMR cluster.

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Question 125

A company is running a data-intensive application on AWS. The application runs on a cluster of hundreds of Amazon EC2 instances. A shared file system also runs on several EC2 instances that store 200 TB of data. The application reads and modifies the data on the shared file system and generates a report. The job runs once monthly, reads a subset of the files from the shared file system, and takes about 72 hours to complete. The compute instances scale in an Auto Scaling group, but the instances that host the shared file system run continuously. The compute and storage instances are all in the same AWS Region.

A solutions architect needs to reduce costs by replacing the shared file system instances. The file system must provide high performance access to the needed data for the duration of the 72-hour run.

Which solution will provide the LARGEST overall cost reduction while meeting these requirements?

Options:

Migrate the data from the existing shared file system to an Amazon S3 bucket that uses the S3 Intelligent-Tiering storage class. Before the job runs each month, use Amazon FSx for Lustre to create a new file system with the data from Amazon S3 by using lazy loading. Use the new file system as the shared storage for the duration of the job. Delete the file system when the job is complete.

Migrate the data from the existing shared file system to a large Amazon Elastic Block Store (Amazon EBS) volume with Multi-Attach enabled. Attach the EBS volume to each of the instances by using a user data script in the Auto Scaling group launch template. Use the EBS volume as the shared storage for the duration of the job. Detach the EBS volume when the job is complete.

Migrate the data from the existing shared file system to an Amazon S3 bucket that uses the S3 Standard storage class. Before the job runs each month, use Amazon FSx for Lustre to create a new file system with the data from Amazon S3 by using batch loading. Use the new file system as the shared storage for the duration of the job. Delete the file system when the job is complete.

Migrate the data from the existing shared file system to an Amazon S3 bucket. Before the job runs each month, use AWS Storage Gateway to create a file gateway with the data from Amazon S3. Use the file gateway as the shared storage for the job. Delete the file gateway when the job is complete.

Discussion

Question 126

A solutions architect is auditing the security setup of an AWS Lambda function for a company. The Lambda function retrieves the latest changes from an Amazon Aurora database. The Lambda function and the database run in the same VPC. Lambda environment variables are providing the database credentials to the Lambda function.

The Lambda function aggregates data and makes the data available in an Amazon S3 bucket that is configured for server-side encryption with AWS KMS managed encryption keys (SSE-KMS). The data must not travel across the internet. If any database credentials become compromised, the company needs a solution that minimizes the impact of the compromise.

What should the solutions architect recommend to meet these requirements?

Options:

Enable IAM database authentication on the Aurora DB cluster. Change the IAM role for the Lambda function to allow the function to access the database by using IAM database authentication. Deploy a gateway VPC endpoint for Amazon S3 in the VPC.

Enable IAM database authentication on the Aurora DB cluster. Change the IAM role for the Lambda function to allow the function to access the database by using IAM database authentication. Enforce HTTPS on the connection to Amazon S3 during data transfers.

Save the database credentials in AWS Systems Manager Parameter Store. Set up password rotation on the credentials in Parameter Store. Change the IAM role for the Lambda function to allow the function to access Parameter Store. Modify the Lambda function to retrieve the credentials from Parameter Store. Deploy a gateway VPC endpoint for Amazon S3 in the VPC.

Save the database credentials in AWS Secrets Manager. Set up password rotation on the credentials in Secrets Manager. Change the IAM role for the Lambda function to allow the function to access Secrets Manager. Modify the Lambda function to retrieve the credentials Om Secrets Manager. Enforce HTTPS on the connection to Amazon S3 during data transfers.

Discussion

Question 127

A company wants to use AWS to create a business continuity solution in case the company's main on-premises application fails. The application runs on physical servers that also run other applications. The on-premises application that the company is planning to migrate uses a MySQL database as a data store. All the company's on-premises applications use operating systems that are compatible with Amazon EC2.

Which solution will achieve the company's goal with the LEAST operational overhead?

Options:

Install the AWS Replication Agent on the source servers, including the MySQL servers. Set up replication for all servers. Launch test instances for regular drills. Cut over to the test instances to fail over the workload in the case of a failure event.

Install the AWS Replication Agent on the source servers, including the MySQL servers. Initialize AWS Elastic Disaster Recovery in the target AWS Region. Define the launch settings. Frequently perform failover and fallback from the most recent point in time.

Create AWS Database Migration Service (AWS DMS) replication servers and a target Amazon Aurora MySQL DB cluster to host the database. Create a DMS replication task to copy the existing data to the target DB cluster. Create a local AWS Schema Conversion Tool (AWS SCT) change data capture (CDC) task to keep the data synchronized. Install the rest of the software on EC2 instances by starting with a compatible base AMI.

Deploy an AWS Storage Gateway Volume Gateway on premises. Mount volumes on all on-premises servers. Install the application and the MySQL database on the new volumes. Take regular snapshots. Install all the software on EC2 Instances by starting with a compatible base AMI. Launch a Volume Gateway on an EC2 instance. Restore the volumes from the latest snapshot. Mount the new volumes on the EC2 instances in the case of a failure event.