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Ideal Amazon AWS-Solutions-Architect-Professional Exam Questions For Quick Succe

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Ideal Amazon AWS-Solutions-Architect-Professional Exam Questions For Quick Succe

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Amazon AWS Certified Solutions Architect - Professional Sample Questions (Q343-Q348):NEW QUESTION # 343
A company collects a steady stream of 10 million data records from 100,000 sources each day. These records are written to an Amazon RDS MySQL DB. A query must produce the daily average of a data source over the past 30 days. There are twice as many reads as writes. Queries to the collected data are for one source ID at a time.
How can the Solutions Architect improve the reliability and cost effectiveness of this solution?
  • A. Use Amazon Aurora with MySQL in a Multi-AZ mode. Use four additional read replicas.
  • B. Use Amazon DynamoDB with the source ID as the partition key. Use a different table each day.
  • C. Ingest data into Amazon Kinesis using a retention period of 30 days. Use AWS Lambda to write data records to Amazon ElastiCache for read access.
  • D. Use Amazon DynamoDB with the source ID as the partition key and the timestamp as the sort key. Use a Time to Live (TTL) to delete data after 30 days.
Answer: A

NEW QUESTION # 344
Which of the following cache engines does Amazon ElastiCache support?
  • A. Amazon ElastiCache supports Memcached and Redis.
  • B. Amazon ElastiCache supports Redis and WinCache.
  • C. Amazon ElastiCache supports Memcached and Hazelcast.
  • D. Amazon ElastiCache supports Memcached only.
Answer: A
Explanation:
The cache engines supported by Amazon ElastiCache are Memcached and Redis.
Reference: http://docs.aws.amazon.com/Amazo ... e/SelectEngine.html

NEW QUESTION # 345
An organization has a write-intensive mobile application that uses Amazon API Gateway, AWS Lambda, and Amazon DynamoDB. The application has scaled well, however, costs have increased exponentially because of higher than anticipated Lambda costs. The application's use is unpredictable, but there has been a steady 20% increase in utilization every month.
While monitoring the current Lambda functions, the Solutions Architect notices that the execution-time averages 4.5 minutes. Most of the wait time is the result of a high-latency network call to a 3-TB MySQL database server that is on-premises. A VPN is used to connect to the VPC, so the Lambda functions have been configured with a five-minute timeout.
How can the Solutions Architect reduce the cost of the current architecture?
  • A. Migrate the MySQL database server into a Multi-AZ Amazon RDS for MySQL.Enable API caching on API Gateway to reduce the number of Lambda function invocations.Continue to monitor the AWS Lambda function performance; gradually adjust the timeout and memory properties to lower values while maintaining an acceptable execution time.Enable Auto Scaling in DynamoDB.
  • B. Migrate the MySQL database server into a Multi-AZ Amazon RDS for MySQL.Enable caching of the Amazon API Gateway results in Amazon CloudFront to reduce the number of Lambda function invocations.Monitor the Lambda function performance; gradually adjust the timeout and memory properties to lower values while maintaining an acceptable execution time.Enable DynamoDB Accelerator for frequently accessed records, and enable the DynamoDB Auto Scaling feature.
  • C. Replace the VPN with AWS Direct Connect to reduce the network latency to the on-premises MySQL database.Enable local caching in the mobile application to reduce the Lambda function invocation calls.Monitor the Lambda function performance; gradually adjust the timeout and memory properties to lower values while maintaining an acceptable execution time.Offload the frequently accessed records from DynamoDB to Amazon ElastiCache.
  • D. Replace the VPN with AWS Direct Connect to reduce the network latency to the on-premises MySQL database.Cache the API Gateway results to Amazon CloudFront.Use Amazon EC2 Reserved Instances instead of Lambda.Enable Auto Scaling on EC2, and use Spot Instances during peak times.Enable DynamoDB Auto Scaling to manage target utilization.
Answer: A

NEW QUESTION # 346
An ecommerce website running on AWS uses an Amazon RDS for MySQL DB instance with General Purpose SSD storage. The developers chose an appropriate instance type based on demand, and configured 100 GB of storage with a sufficient amount of free space.
The website was running smoothly for a few weeks until a marketing campaign launched On the second day of the campaign, users reported long wait times and time outs Amazon CloudWatch metrics indicated that both reads and writes to the DB instance were experiencing long response times. The CloudWatch metrics show 40% to 50% CPU and memory utilization, and sufficient free storage space is still available The application server logs show no evidence of database connectivity issues What could be the root cause of the issue with the marketing campaign?
  • A. It exhausted the maximum number of allowed connections to the database instance
  • B. It exhausted the network bandwidth available to the RDS for MySQL DB instance
  • C. It exhausted the I/O credit balance due to provisioning low disk storage during the setup phase
  • D. It caused the data in the tables to change frequently, requiring indexes to be rebuilt to optimize queries
Answer: C

NEW QUESTION # 347
A company deploys a new web application As part of the setup, the company configures AWS WAF to log to Amazon S3 through Amazon Kinesis Data Firehose. The company develops an Amazon Athena query that runs once daily to return AWS WAF log data from the previous 24 hours. The volume of daily logs is constant However over time, the same query is taking more time to run A solutions architect needs to design a solution to prevent the query time from continuing to increase. The solution must minimize operational overhead Which solution will meet these requirements?
  • A. Update the Kinesis Data Firehose configuration to partition the data in Amazon S3 by date and time Create external tables for Amazon Redshift Configure Amazon Redshift Spectrum to query the data source
  • B. Modify the Kinesis Data Firehose configuration and Athena table definition to partition the data by date and time. Change the Athena query to view the relevant partitions
  • C. Create an AWS Lambda function that consolidates each day's AWS WAF logs into one log file
  • D. Reduce the amount of data scanned by configuring AWS WAF to send logs to a different S3 bucket each day
Answer: B

NEW QUESTION # 348
......
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