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A marketing company wants to improve its reporting and business intelligence capabilities. During the planning phase, the company interviewed the relevant stakeholders and discovered that:

The operations team reports are run hourly for the current month's data.

The sales team wants to use multiple Amazon QuickSight dashboards to show a rolling view of the last 30 days based on several categories. The sales team also wants to view the data as soon as it reaches the reporting backend.

The finance team's reports are run daily for last month's data and once a month for the last 24 months of data.

Currently, there is 400 TB of data in the system with an expected additional 100 TB added every month. The company is looking for a solution that is as cost-effective as possible.

Which solution meets the company's requirements?

A. Store the last 24 months of data in Amazon Redshift. Configure Amazon QuickSight with Amazon Redshift as the data source.

B. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Set up an external schema and table for Amazon Redshift Spectrum. Configure Amazon QuickSight with Amazon Redshift as the data source.

C. Store the last 24 months of data in Amazon S3 and query it using Amazon Redshift Spectrum. Configure Amazon QuickSight with Amazon Redshift Spectrum as the data source.

D. Store the last 2 months of data in Amazon Redshift and the rest of the months in Amazon S3. Use a long-running Amazon EMR with Apache Spark cluster to query the data as needed. Configure Amazon QuickSight with Amazon EMR as the data source.

A company wants to provide its data analysts with uninterrupted access to the data in its Amazon Redshift cluster. All data is streamed to an Amazon S3 bucket with Amazon Kinesis Data Firehose. An AWS Glue job that is scheduled to run every 5 minutes issues a COPY command to move the data into Amazon Redshift.

The amount of data delivered is uneven throughout the day, and cluster utilization is high during certain periods. The COPY command usually completes within a couple of seconds. However, when load spike occurs, locks can exist and data can be missed. Currently, the AWS Glue job is configured to run without retries, with timeout at 5 minutes and concurrency at 1.

How should a data analytics specialist configure the AWS Glue job to optimize fault tolerance and improve data availability in the Amazon Redshift cluster?

A. Increase the number of retries. Decrease the timeout value. Increase the job concurrency.

B. Keep the number of retries at 0. Decrease the timeout value. Increase the job concurrency.

C. Keep the number of retries at 0. Decrease the timeout value. Keep the job concurrency at 1.

D. Keep the number of retries at 0. Increase the timeout value. Keep the job concurrency at 1.

A global pharmaceutical company receives test results for new drugs from various testing facilities worldwide. The results are sent in millions of 1 KB-sized JSON objects to an Amazon S3 bucket owned by the company. The data engineering team needs to process those files, convert them into Apache Parquet format, and load them into Amazon Redshift for data analysts to perform dashboard reporting. The engineering team uses AWS Glue to process the objects, AWS Step Functions for process orchestration, and Amazon CloudWatch for job scheduling.

More testing facilities were recently added, and the time to process files is increasing.

What will MOST efficiently decrease the data processing time?

A. Use AWS Lambda to group the small files into larger files. Write the files back to Amazon S3. Process the files using AWS Glue and load them into Amazon Redshift tables.

B. Use the AWS Glue dynamic frame file grouping option while ingesting the raw input files. Process the files and load them into Amazon Redshift tables.

C. Use the Amazon Redshift COPY command to move the files from Amazon S3 into Amazon Redshift tables directly. Process the files in Amazon Redshift.

D. Use Amazon EMR instead of AWS Glue to group the small input files. Process the files in Amazon EMR and load them into Amazon Redshift tables.

An online food delivery company wants to optimize its storage costs. The company has been collecting operational data for the last 10 years in a data lake that was built on Amazon S3 by using a Standard storage class. The company does not keep data that is older than 7 years. The data analytics team frequently uses data from the past 6 months for reporting and runs queries on data from the last 2 years about once a month. Data that is more than 2 years old is rarely accessed and is only used for audit purposes.

Which combination of solutions will optimize the company's storage costs? (Choose two.)

A. Create an S3 Lifecycle configuration rule to transition data that is older than 6 months to the S3 Standard-Infrequent Access (S3 Standard-IA) storage class. Create another S3 Lifecycle configuration rule to transition data that is older than 2 years to the S3 Glacier Deep Archive storage class.

B. Create an S3 Lifecycle configuration rule to transition data that is older than 6 months to the S3 One Zone-Infrequent Access (S3 One Zone-IA) storage class. Create another S3 Lifecycle configuration rule to transition data that is older than 2 years to the S3 Glacier Flexible Retrieval storage class.

C. Use the S3 Intelligent-Tiering storage class to store data instead of the S3 Standard storage class.

D. Create an S3 Lifecycle expiration rule to delete data that is older than 7 years.

E. Create an S3 Lifecycle configuration rule to transition data that is older than 7 years to the S3 Glacier Deep Archive storage class.

A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient's protected health information (PHI) from the streaming data before storing the data in durable storage.

Which solution meets these requirements with the LEAST operational overhead?

A. Ingest the data by using Amazon Kinesis Data Streams. Process the data by using Amazon EC2 instances that use Amazon Kinesis Client Library (KCL) and custom logic to remove all PHI from the data. Write the data to Amazon S3.

B. Ingest the data by using Amazon Kinesis Data Firehose and write the data to Amazon S3. Have Amazon S3 invoke an AWS Lambda function that removes all PHI.

C. Ingest the data by using Amazon Kinesis Data Streams to write the data to Amazon S3. Have Amazon S3 invoke an AWS Lambda function that removes all PHI.

D. Ingest the data by using Amazon Kinesis Data Firehose. Invoke a Kinesis Data Firehose data transformation by using an AWS Lambda function to remove all PHI. Configure Kinesis Data Firehose so that Amazon S3 is the destination.