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Free AWS Certified Data Engineer - Associate DEA-C01 Exam Braindumps (page: 17)

Page 17 of 39
PDF with 194 Questions

A data engineer must build an extract, transform, and load (ETL) pipeline to process and load data from 10 source systems into 10 tables that are in an Amazon Redshift database. All the source systems generate .csv, JSON, or Apache Parquet files every 15 minutes. The source systems all deliver files into one Amazon S3 bucket. The file sizes range from 10 MB to 20 GB. The ETL pipeline must function correctly despite changes to the data schema.
Which data pipeline solutions will meet these requirements? (Choose two.)

  1. Use an Amazon EventBridge rule to run an AWS Glue job every 15 minutes. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
  2. Use an Amazon EventBridge rule to invoke an AWS Glue workflow job every 15 minutes. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
  3. Configure an AWS Lambda function to invoke an AWS Glue crawler when a file is loaded into the S3 bucket. Configure an AWS Glue job to process and load the data into the Amazon Redshift tables. Create a second Lambda function to run the AWS Glue job. Create an Amazon EventBridge rule to invoke the second Lambda function when the AWS Glue crawler finishes running successfully.
  4. Configure an AWS Lambda function to invoke an AWS Glue workflow when a file is loaded into the S3 bucket. Configure the AWS Glue workflow to have an on-demand trigger that runs an AWS Glue crawler and then runs an AWS Glue job when the crawler finishes running successfully. Configure the AWS Glue job to process and load the data into the Amazon Redshift tables.
  5. Configure an AWS Lambda function to invoke an AWS Glue job when a file is loaded into the S3 bucket. Configure the AWS Glue job to read the files from the S3 bucket into an Apache Spark DataFrame. Configure the AWS Glue job to also put smaller partitions of the DataFrame into an Amazon Kinesis Data Firehose delivery stream. Configure the delivery stream to load data into the Amazon Redshift tables.

Answer(s): B,D



A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company's use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?

  1. Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day.
  2. Use the query result reuse feature of Amazon Athena for the SQL queries.
  3. Add an Amazon ElastiCache cluster between the BI application and Athena.
  4. Change the format of the files that are in the dataset to Apache Parquet.

Answer(s): B



A company's data engineer needs to optimize the performance of table SQL queries. The company stores data in an Amazon Redshift cluster. The data engineer cannot increase the size of the cluster because of budget constraints.
The company stores the data in multiple tables and loads the data by using the EVEN distribution style. Some tables are hundreds of gigabytes in size. Other tables are less than 10 MB in size.
Which solution will meet these requirements?

  1. Keep using the EVEN distribution style for all tables. Specify primary and foreign keys for all tables.
  2. Use the ALL distribution style for large tables. Specify primary and foreign keys for all tables.
  3. Use the ALL distribution style for rarely updated small tables. Specify primary and foreign keys for all tables.
  4. Specify a combination of distribution, sort, and partition keys for all tables.

Answer(s): C



A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

Which solution will meet this requirement with the LEAST coding effort?

  1. Use AWS Glue DataBrew to read the files. Use the NEST_TO_ARRAY transformation to create the new column.
  2. Use AWS Glue DataBrew to read the files. Use the NEST_TO_MAP transformation to create the new column.
  3. Use AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.
  4. Write a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.

Answer(s): B



Page 17 of 39



Post your Comments and Discuss Amazon AWS Certified Data Engineer - Associate DEA-C01 exam with other Community members:

Abhishek commented on December 21, 2024
It was Nice
Anonymous
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saif Ali commented on October 24, 2024
for Question no 50 The answer would be using lambda vdf as this provides automation
INDIA
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Josh commented on October 09, 2024
Team, thanks for the wonderful support. This guide helped me a lot.
UNITED STATES
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Ming commented on September 19, 2024
Very cool very precise. I highly recommend this study package.
UNITED STATES
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Geovani commented on September 18, 2024
Very useful content and point by point explanation. And also the payment and download process was straight forward. Good job guys.
Italy
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