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Amazon das-c01 exam practice test
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A company has a business unit uploading .csv files to an Amazon S3 bucket. The company\\’s data platform team has
set up an AWS Glue crawler to do discovery, and create tables and schemas. An AWS Glue job writes processed data
from the created tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creating the
Amazon Redshift table appropriately. When the AWS Glue job is rerun for any reason in a day, duplicate records are
introduced into the Amazon Redshift table.
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An airline has been collecting metrics on flight activities for analytics. A recently completed proof of concept
demonstrates how the company provides insights to data analysts to improve on-time departures. The proof of concept used objects in Amazon S3, which contained the metrics in .csv format, and used Amazon Athena for querying the data.
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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 then 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.