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Amazon AWS-Solutions-Architect-Professional Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Accelerate Workload Migration and Modernization	20%	- Migration strategies and planning - Modernization approaches - Migration validation and cutover - Data transfer and synchronization - Application and database migration
Topic 2: Design Solutions for Organizational Complexity	26%	- Cost visibility and management - Hybrid network connectivity - Compliance and regulatory requirements - Centralized governance and security - Multi-account and multi-region architecture
Topic 3: Design for New Solutions	29%	- Service selection and integration - High availability and disaster recovery - Performance and reliability design - Security and data protection - Scalable and resilient architecture
Topic 4: Continuous Improvement for Existing Solutions	25%	- Performance tuning - Security posture improvement - Operational efficiency - Automation and infrastructure as code - Cost optimization strategies

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Amazon AWS Certified Solutions Architect - Professional Sample Questions (Q334-Q339):

NEW QUESTION # 334
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 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
B. 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 instance
C. 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 n
D. 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

Answer: D

NEW QUESTION # 335
How can multiple compute resources be used on the same pipeline in AWS Data Pipeline?

A. You can use multiple compute resources on the same pipeline by defining multiple cluster objects in
your definition file and associating the cluster to use for each activity via its runsOn field.
B. You can use multiple compute resources on the same pipeline by defining multiple clusters for your
activity.
C. You cannot use multiple compute resources on the same pipeline.
D. You can use multiple compute resources on the same pipeline by defining multiple cluster definition
files.

Answer: A

Explanation:
Multiple compute resources can be used on the same pipeline in AWS Data Pipeline by defining multiple
cluster objects in your definition file and associating the cluster to use for each activity via its runsOn field,
which allows pipelines to combine AWS and on-premise resources, or to use a mix of instance types for
their activities.
Reference: https://aws.amazon.com/datapipeline/faqs/

NEW QUESTION # 336
A company wants to deploy an API to AWS. The company plans to run the API on AWS Fargate behind a load balancer. The API requires the use of header-based routing and must be accessible from on-premises networks through an AWS Direct Connect connection and a private VIF.
The company needs to add the client IP addresses that connect to the API to an allow list in AWS. The company also needs to add the IP addresses of the API to the allow list. The company's security team will allow /27 CIDR ranges to be added to the allow list. The solution must minimize complexity and operational overhead.
Which solution will meet these requirements?

A. Create a new Application Load Balancer (ALB) in the same subnets as the Fargate task deployments.
Create a security group that includes only the client IP addresses that need access to the API. Attach the security group to the ALB. Provide the security team with the ALB's IP addresses for the allow list.
B. Create two new /27 subnets. Create a new Application Load Balancer (ALB) that extends across the new subnets. Create a security group that includes only the client IP addresses that need access to the API.
Attach the security group to the ALB. Provide the security team with the new subnet IP ranges for the allow list.
C. Create a new Network Load Balancer (NLB) in the same subnets as the Fargate task deployments.
Create a security group that includes only the client IP addresses that need access to the API. Attach the new security group to the Fargate tasks. Provide the security team with the NLB's IP addresses for the allow list.
D. Create two new '27 subnets. Create a new Network Load Balancer (NLB) that extends across the new subnets. Create a new Application Load Balancer (ALB) within the new subnets. Create a security group that includes only the client IP addresses that need access to the API. Attach the security group to the ALB. Add the ALB's IP addresses as targets behind the NLB. Provide the security team with the NLB's IP addresses for the allow list.

Answer: C

NEW QUESTION # 337
A company needs to cost-effectively persist small data records (up to 1 KiB) for up to 30 days.
The data is read rarely. When reading the data, a 5-minute delay is acceptable.
Which of the following solutions achieve this goal? (Choose two.)

A. Write the records to Amazon Kinesis Data Firehose and configure Kinesis Data Firehose to deliver the data to Amazon S3 after 5 minutes. Set an expiration action at 30 days on the S3 bucket.
B. Use Amazon S3 to collect multiple records in one S3 object. Use a lifecycle configuration to move data to Amazon Glacier immediately after write. Use expedited retrievals when reading the data.
C. Use an AWS Lambda function invoked via Amazon API Gateway to collect data for 5 minutes.
Write data to Amazon S3 just before the Lambda execution stops.
D. Write the records to an Amazon ElastiCache for Redis. Configure the Redis append-only file (AOF) persistence logs to write to Amazon S3. Recover from the log if the ElastiCache instance has failed.
E. Write the records to Amazon DynamoDB configured with a Time To Live (TTL) of 30 days. Read data using the GetItem or BatchGetItem call.

Answer: C,E

NEW QUESTION # 338
A company is having issues with a newly deployed server less infrastructure that uses Amazon API Gateway, Amazon Lambda, and Amazon DynamoDB.
In a steady state, the application performs as expected However, during peak load, tens of thousands of simultaneous invocations are needed and user request fail multiple times before succeeding. The company has checked the logs for each component, focusing specifically on Amazon CloudWatch Logs for Lambd

A. The throttle limit set on API Gateway is very low during peak load, the additional requests are not making their way through to Lambda
B. There are no error logged by the services or applications.
What might cause this problem?
C. Lambda has very memory assigned, which causes the function to fail at peak load.
D. DynamoDB is set up in an auto scaling mode. During peak load, DynamoDB adjust capacity and through successfully.
E. Lambda is in a subnet that uses a NAT gateway to reach out to the internet, and the function instance does not have sufficient Amazon EC2 resources in the VPC to scale with the load.

Answer: B

NEW QUESTION # 339
......

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