Free SAA-C03 Amazon Web Services Updates

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Question 176

A company has an application that collects data from loT sensors on automobiles. The data is streamed and stored in Amazon S3 through Amazon Kinesis Date Firehose The data produces trillions of S3 objects each year. Each morning, the company uses the data from the previous 30 days to retrain a suite of machine learning (ML) models.

Four times each year, the company uses the data from the previous 12 months to perform analysis and train other ML models The data must be available with minimal delay for up to 1 year. After 1 year, the data must be retained for archival purposes.

Which storage solution meets these requirements MOST cost-effectively?

Options:

Use the S3 Intelligent-Tiering storage class. Create an S3 Lifecycle policy to transition objects to S3 Glacier Deep Archive after 1 year

Use the S3 Intelligent-Tiering storage class. Configure S3 Intelligent-Tiering to automatically move objects to S3 Glacier Deep Archive after 1 year.

Use the S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Create an S3 Lifecycle policy to transition objects to S3 Glacier Deep Archive after 1 year.

Use the S3 Standard storage class. Create an S3 Lifecycle policy to transition objects to S3 Standard-Infrequent Access (S3 Standard-IA) after 30 days, and then to S3 Glacier Deep Archive after 1 year.

Question 177

A company has an application that places hundreds of .csv files into an Amazon S3 bucket every hour. The files are 1 GB in size. Each time a file is uploaded, the company needs to convert the file to Apache Parquet format and place the output file into an S3 bucket.

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

Options:

Create an AWS Lambda function to download the .csv files, convert the files to Parquet format, and place the output files in an S3 bucket. Invoke the Lambda function for each S3 PUT event.

Create an Apache Spark job to read the .csv files, convert the files to Parquet format, and place the output files in an S3 bucket. Create an AWS Lambda function for each S3 PUT event to invoke the Spark job.

Create an AWS Glue table and an AWS Glue crawler for the S3 bucket where the application places the .csv files. Schedule an AWS Lambda function to periodically use Amazon Athena to query the AWS Glue table, convert the query results into Parquet format, and place the output files into an S3 bucket.

Create an AWS Glue extract, transform, and load (ETL) job to convert the .csv files to Parquet format and place the output files into an S3 bucket. Create an AWS Lambda function for each S3 PUT event to invoke the ETL job.

Question 178

A company experienced a breach that affected several applications in its on-premises data center The attacker took advantage of vulnerabilities in the custom applications that were running on the servers The company is now migrating its applications to run on Amazon EC2 instances The company wants to implement a solution that actively scans for vulnerabilities on the EC2 instances and sends a report that details the findings

Which solution will meet these requirements?

Options:

Deploy AWS Shield to scan the EC2 instances for vulnerabilities Create an AWS Lambda function to log any findings to AWS CloudTrail.

Deploy Amazon Macie and AWS Lambda functions to scan the EC2 instances for vulnerabilities Log any findings to AWS CloudTrail

Turn on Amazon GuardDuty Deploy the GuardDuty agents to the EC2 instances Configure an AWS Lambda function to automate the generation and distribution of reports that detail the findings

Turn on Amazon Inspector Deploy the Amazon Inspector agent to the EC2 instances Configure an AWS Lambda function to automate the generation and distribution of reports that detail the findings

Question 179

A company is moving its data management application to AWS. The company wants to transition to an event-driven architecture. The architecture needs to the more distributed and to use serverless concepts whit performing the different aspects of the workflow. The company also wants to minimize operational overhead.

Which solution will meet these requirements?

Options:

Build out the workflow in AWS Glue Use AWS Glue to invoke AWS Lambda functions to process the workflow slaps

Build out the workflow in AWS Step Functions Deploy the application on Amazon EC2 Instances Use Step Functions to invoke the workflow steps on the EC2 instances

Build out the workflow in Amazon EventBridge. Use EventBridge to invoke AWS Lambda functions on a schedule to process the workflow steps.

Build out the workflow m AWS Step Functions Use Step Functions to create a stale machine Use the stale machine to invoke AWS Lambda functions to process the workflow steps