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The topics covered in the SAP-C02 exam include advanced AWS services such as AWS Lambda, AWS Elastic Beanstalk, AWS CloudFormation, and AWS CloudTrail. Candidates are also tested on their knowledge of designing and deploying multi-tier architectures, hybrid architectures, and highly available and fault-tolerant systems on AWS. Security, compliance, and troubleshooting are also important topics covered in the exam.

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Amazon AWS Certified Solutions Architect - Professional (SAP-C02) Sample Questions (Q152-Q157):

NEW QUESTION # 152
A development team s Deploying new APIs as serverless applications within a company. The team is currently using the AWS Maragement Console to provision Amazon API Gateway. AWS Lambda, and Amazon DynamoDB resources A solutions architect has been tasked with automating the future deployments of these serveriess APIs
How can this be accomplished?

  • A. Use the AWS Serverless Application Model to define the resources Upload a YAML template and application files to the code repository Use AWS CodePipeline to conned to the code repository and to create an action to build using AWS CodeBuild. Use the AWS CloudFormabon deployment provider m CodePipeline to deploy the solution.
  • B. Use AWS CloudFonTiation with a Lambda-backed custom resource to provision API Gateway Use the MfS: :OynMoDB::Table and AWS::Lambda::Function resources to create the Amazon DynamoOB table and Lambda functions Write a script to automata the deployment of the CloudFormation template.
  • C. Commit the application code to the AWS CodeCommit code repository. Use AWS CodePipeline and connect to the CodeCommit code repository Use AWS CodeBuild to build and deploy the Lambda functions using AWS CodeDeptoy Specify the deployment preference type in CodeDeploy to gradually shift traffic over to the new version.
  • D. Use AWS CloudFormation to define the serverless application. Implement versioning on the Lambda functions and create aliases to point to the versions. When deploying, configure weights to implement shifting traffic to the newest version, and gradually update the weights as traffic moves over

Answer: A


NEW QUESTION # 153
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?

  • A. 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.
  • B. Tigger an AWS Lambda function on file delivery that extracts each record and wntes it to an Amazon SOS queue. Configure an AWS Fargate container application to
  • C. 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.
  • D. 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 SOS queue to process the records, writing the results to a temporary location in Amazon S3. Trigger a final Lambda function once the SOS queue is empty to transform the records into JSON format and send the results to another S3 bucket for internal processing.
  • E. automatically scale to a single instance when the SOS 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 instance.

Answer: E

Explanation:
Explanation
You can use a Glue crawler to populate the AWS Glue Data Catalog with tables. The Lambda function can be triggered using S3 event notifications when object create events occur. The Lambda function will then trigger the Glue ETL job to transform the records masking the sensitive data and modifying the output format to JSON. This solution meets all requirements.
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.
https://docs.aws.amazon.com/glue/latest/dg/trigger-job.html
https://d1.awsstatic.com/Products/product-name/diagrams/product-page-diagram_Glue_Event-driven-ETL-Pipel


NEW QUESTION # 154
A company needs to architect a hybrid DNS solution. This solution will use an Amazon Route 53 private hosted zone for the domain cloud.example.com for the resources stored within VPCs.
The company has the following DNS resolution requirements:
* On-premises systems should be able to resolve and connect to cloud.example.com.
* All VPCs should be able to resolve cloud.example.com.
There is already an AWS Direct Connect connection between the on-premises corporate network and AWS Transit Gateway. Which architecture should the company use to meet these requirements with the HIGHEST performance?

  • A. Associate the private hosted zone to all the VPCs. Deploy an Amazon EC2 conditional forwarder in the shared services VPC. Attach all VPCs to the transit gateway and create forwarding rules in the on-premises DNS server for cloud.example.com that point to the conditional forwarder.
  • B. Associate the private hosted zone to the shared services VPC. Create a Route 53 inbound resolver in the shared services VPC. Attach the shared services VPC to the transit gateway and create forwarding rules in the on-premises DNS server for cloud.example.com that point to the inbound resolver.
  • C. Associate the private hosted zone to the shared services VPC. Create a Route 53 outbound resolver in the shared services VPC. Attach all VPCs to the transit gateway and create forwarding rules in the on-premises DNS server for cloud.example.com that point to the outbound resolver.
  • D. Associate the private hosted zone to all the VPCs. Create a Route 53 inbound resolver in the shared services VPC. Attach all VPCs to the transit gateway and create forwarding rules in the on-premises DNS server for cloud.example.com that point to the inbound resolver.

Answer: B

Explanation:
Explanation
https://aws.amazon.com/blogs/networking-and-content-delivery/centralized-dns-management-of-hybrid-cloud-w


NEW QUESTION # 155
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 dat
a. 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?

  • A. 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.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: C

Explanation:
Generally, unstructured data should be converted structured data before querying them. AWS Glue can do that. https://docs.aws.amazon.com/glue/latest/dg/schema-relationalize.html https://docs.aws.amazon.com/athena/latest/ug/glue-athena.html


NEW QUESTION # 156
A company recently deployed an application on AWS. The application uses Amazon DynamoDB. The company measured the application load and configured the RCUs and WCUs on the DynamoDB table to match the expected peak load. The peak load occurs once a week for a 4-hour period and is double the average load. The application load is close to the average load tor the rest of the week. The access pattern includes many more writes to the table than reads of the table.
A solutions architect needs to implement a solution to minimize the cost of the table.
Which solution will meet these requirements?

  • A. Use AWS Application Auto Scaling to increase capacity during the peak period. Purchase reserved RCUs and WCUs to match the average load.
  • B. Configure DynamoDB Accelerator (DAX) in front of the table. Reduce the provisioned read capacity to match the new peak load on the table.
  • C. Configure DynamoDB Accelerator (DAX) in front of the table. Configure on-demand capacity mode for the table.
  • D. Configure on-demand capacity mode for the table.

Answer: A

Explanation:
This solution meets the requirements by using Application Auto Scaling to automatically increase capacity during the peak period, which will handle the double the average load. And by purchasing reserved RCUs and WCUs to match the average load, it will minimize the cost of the table for the rest of the week when the load is close to the average.


NEW QUESTION # 157
......

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