Amazon Web Services testinside download Latest Saa-c03 Questions by Zayaan q160 vce pdf

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Exam Name:	AWS Certified Solutions Architect - Associate (SAA-C03)
Exam Code:	SAA-C03 Dumps
Vendor:	Amazon Web Services	Certification:	AWS Certified Associate
Questions:	649 Q&A's	Shared By:	zayaan

Question 20

A company runs an application on Amazon EC2 instances across multiple Availability Zones in the same AWS Region. The EC2 instances share an Amazon Elastic File System (Amazon EFS) volume that is mounted on all the instances. The EFS volume stores a variety of files such as installation media, third-party files, interface files, and other one-time files.

The company accesses some EFS files frequently and needs to retrieve the files quickly. The company accesses other files rarely. The EFS volume is multiple terabytes in size. The company needs to optimize storage costs for Amazon EFS.

Which solution will meet these requirements with the LEAST effort?

Options:

Move the files to Amazon S3. Set up a lifecycle policy to move the files to S3 Glacier Flexible Retrieval.

Apply a lifecycle policy to the EFS files to move the files to EFS Infrequent Access.

Move the files to Amazon Elastic Block Store (Amazon EBS) Cold HDD Volumes (sc1).

Move the files to Amazon S3. Set up a lifecycle policy to move the rarely-used files to S3 Glacier Deep Archive.

Discussion

Question 21

A company uses an Amazon RDS MySQL database to store data for several applications. The company wants to understand use patterns for the database so the company can identify oppor-tunities to optimize costs.

A solutions architect needs to analyze the RDS DB instance to identify right-sizing opportuni-ties.

Which solution will meet these requirements with the LEAST effort?

Options:

Enable AWS CloudTrail data events. Use Amazon Athena to query CloudTrail events. Right-size the RDS DB instance based on the number of transactions.

Enable Performance Insights for the RDS DB instance. Right-size the RDS DB instance based on the maximum CPU utilization.

Enable AWS X-Ray to understand the transactions that run on the RDS DB instance. Right-size the RDS DB instance based on the number of transactions.

Enable Amazon CloudWatch Logs for the applications. Aggregate the data from Cloud-Watch Logs for all the applications. Right-size the RDS DB instance based on the aggregated logs.

Discussion

Question 22

A company needs to ingest and analyze telemetry data from vehicles at scale for machine learning and reporting.

Which solution will meet these requirements?

Options:

Use Amazon Timestream for LiveAnalytics to store data points. Grant Amazon SageMaker permission to access the data. Use Amazon QuickSight to visualize the data.

Use Amazon DynamoDB to store data points. Use DynamoDB Connector to ingest data into Amazon EMR for processing. Use Amazon QuickSight to visualize the data.

Use Amazon Neptune to store data points. Use Amazon Kinesis Data Streams to ingest data into a Lambda function for processing. Use Amazon QuickSight to visualize the data.

Use Amazon Timestream for LiveAnalytics to store data points. Grant Amazon SageMaker permission to access the data. Use Amazon Athena to visualize the data.

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Question 23

A company needs a solution to ingest streaming sensor data from 100,000 devices, transform the data in near real time, and load the data into Amazon S3 for analysis. The solution must be fully managed, scalable, and maintain sub-second ingestion latency.

Options:

Use Amazon Kinesis Data Streams to ingest the data. Use Amazon Managed Service for Apache Flink to process the data in near real time. Use an Amazon Data Firehose stream to send processed data to Amazon S3.

Use Amazon Simple Queue Service (Amazon SQS) standard queues to collect the sensor data. Invoke AWS Lambda functions to transform and process SQS messages in batches. Configure the Lambda functions to use an AWS SDK to write transformed data to Amazon S3.

Deploy a fleet of Amazon EC2 instances that run Apache Kafka to ingest the data. Run Apache Spark on Amazon EMR clusters to process the data. Configure Spark to write processed data directly to Amazon S3.

Implement Amazon EventBridge to capture all sensor data. Use AWS Batch to run containerized transformation jobs on a schedule. Configure AWS Batch jobs to process data in chunks. Save results to Amazon S3.