Amazon Web Services certkiller aws Certified Associate Saa-c03 Syllabus Exam Questions Answers by Cece q321 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:	576 Q&A's	Shared By:	cece

Question 92

A company wants to optimize costs for its AWS infrastructure. The company wants to receive notifications when actual costs or forecasted costs exceed a specified budget. The company does not want to develop a custom solution.

Which solution will meet these requirements?

Options:

Use AWS Trusted Advisor to set up budget notifications. Configure Amazon CloudWatch to monitor costs. Export CloudWatch data to Amazon S3. Use machine learning (ML) to estimate future trends based on the CloudWatch data.

Create a budget in AWS Budgets that has a specified cost threshold. Create an AWS Lambda function that sends a notification to the company when costs reach the specified threshold. Use AWS Billing and Cost Management reports to monitor costs.

Use AWS Cost Explorer to set a specified budget threshold. Create an AWS Lambda function to calculate cost estimates. Configure the Lambda function to send a notification to an Amazon Simple Notification Service (Amazon SNS) topic if estimated costs exceed the specified threshold.

Create a budget in AWS Budgets that has a specified cost threshold. Configure AWS Budgets to send budget alerts to an Amazon Simple Notification Service (Amazon SNS) topic. Use AWS Cost Explorer to monitor costs.

Discussion

Question 93

An ecommerce company stores terabytes of customer data in the AWS Cloud. The data contains personally identifiable information (PII). The company wants to use the data in three applications. Only one of the applications needs to process the PII. The PII must be removed before the other two applications process the data.

Which solution will meet these requirements with the LEAST operational overhead?

Options:

Store the data in an Amazon DynamoDB table. Create a proxy application layer to intercept and process the data that each application requests.

Store the data in an Amazon S3 bucket. Process and transform the data by using S3 Object Lambda before returning the data to the requesting application.

Process the data and store the transformed data in three separate Amazon S3 buckets so that each application has its own custom dataset. Point each application to its respective S3 bucket.

Process the data and store the transformed data in three separate Amazon DynamoDB tables so that each application has its own custom dataset. Point each application to its respective DynamoDB table.

Discussion

Question 94

A startup company is hosting a website for its customers on an Amazon EC2 instance. The website consists of a stateless Python application and a MySQL database. The website serves only a small amount of traffic. The company is concerned about the reliability of the instance and needs to migrate to a highly available architecture. The company cannot modify the application code.

Which combination of actions should a solutions architect take to achieve high availability for the website? (Select TWO.)

Options:

Provision an internet gateway in each Availability Zone in use.

Migrate the database to an Amazon RDS for MySQL Multi-AZ DB instance.

Migrate the database to Amazon DynamoDB. and enable DynamoDB auto scaling.

Use AWS DataSync to synchronize the database data across multiple EC2 instances.

Create an Application Load Balancer to distribute traffic to an Auto Scaling group of EC2 instances that are distributed across two Availability Zones.

Discussion

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Question 95

A company's reporting system delivers hundreds of .csv files to an Amazon S3 bucket each day. The company must convert these files to Apache Parquet format and must store the files in a transformed data bucket.

Which solution will meet these requirements with the LEAST development effort?

Options:

Create an Amazon EMR cluster with Apache Spark installed. Write a Spark application to transform the data. Use EMR File System (EMRFS) to write files to the transformed data bucket.

Create an AWS Glue crawler to discover the data. Create an AWS Glue extract, transform, and load (ETL) job to transform the data. Specify the transformed data bucket in the output step.

Use AWS Batch to create a job definition with Bash syntax to transform the data and output the data to the transformed data bucket. Use the job definition to submit a job. Specify an array job as the job type.

Create an AWS Lambda function to transform the data and output the data to the transformed data bucket. Configure an event notification for the S3 bucket. Specify the Lambda function as the destination for the event notification.