Available Number of Questions: Maximum of
294 Questions
Exam Name: AWS Certified Data Engineer - Associate
Exam Duration: 130 Minutes
Related Certification(s):
Amazon AWS Certified Data Engineer Associate Certification
Amazon Amazon-DEA-C01 Exam Topics - You’ll Be Tested in Actual Exam
The Amazon DEA-C01 exam is a comprehensive assessment designed to evaluate your knowledge and skills in various aspects of AWS (Amazon Web Services). This exam covers a wide range of topics, including AWS services, security and identity federation, data analytics and machine learning, and best practices for developing and deploying applications on AWS. When it comes to AWS services, you'll explore the fundamentals of computing, storage, databases, and networking, gaining an understanding of how these services work together to support your applications. Security is a critical aspect, and the exam will test your knowledge of identity and access management, encryption, and security best practices to ensure the protection of your data and resources. Additionally, you'll delve into data analytics, learning about various tools and techniques for analyzing and visualizing data, as well as machine learning concepts and how to integrate them into your AWS solutions. Best practices are also a key focus, covering topics like designing scalable and resilient architectures, optimizing performance, and ensuring high availability for your applications. By mastering these topics, you'll be well-equipped to tackle the challenges of the DEA-C01 exam and demonstrate your expertise in leveraging AWS for efficient and secure application development and deployment.
Amazon Amazon-DEA-C01 Exam Short Quiz
Attempt this Amazon Amazon-DEA-C01 exam quiz to self-assess your preparation for the actual Amazon AWS Certified Data Engineer - Associate exam. CertBoosters also provides premium Amazon Amazon-DEA-C01 exam questions to pass the Amazon AWS Certified Data Engineer - Associate exam in the shortest possible time. Be sure to try our free practice exam software for the Amazon Amazon-DEA-C01 exam.
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Amazon Amazon-DEA-C01 Exam Quiz
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AmazonAmazon-DEA-C01
Q1:
A data engineer needs to debug an AWS Glue job that reads from Amazon S3 and writes to Amazon Redshift. The data engineer enabled the bookmark feature for the AWS Glue job. The data engineer has set the maximum concurrency for the AWS Glue job to 1.
The AWS Glue job is successfully writing the output to Amazon Redshift. However, the Amazon S3 files that were loaded during previous runs of the AWS Glue job are being reprocessed by subsequent runs.
What is the likely reason the AWS Glue job is reprocessing the files?
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AThe AWS Glue job does not have the s3:GetObjectAcl permission that is required for bookmarks to work correctly.
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BThe maximum concurrency for the AWS Glue job is set to 1.
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CThe data engineer incorrectly specified an older version of AWS Glue for the Glue job.
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DThe AWS Glue job does not have a required commit statement.
AmazonAmazon-DEA-C01
Q2:
A car sales company maintains data about cars that are listed for sale in an are
a. The company receives data about new car listings from vendors who upload the data daily as compressed files into Amazon S3. The compressed files are up to 5 KB in size. The company wants to see the most up-to-date listings as soon as the data is uploaded to Amazon S3.
A data engineer must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The data engineer must also provide the ability to perform one-time queries and analytical reporting. The query solution must be scalable.
Which solution will meet these requirements MOST cost-effectively?
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AUse an Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Apache Hive for one-time queries and analytical reporting. Use Amazon OpenSearch Service to bulk ingest the data into compute optimized instances. Use OpenSearch Dashboards in OpenSearch Service for the dashboard.
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BUse a provisioned Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.
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CUse AWS Glue to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Amazon Redshift Spectrum for one-time queries and analytical reporting. Use OpenSearch Dashboards in Amazon OpenSearch Service for the dashboard.
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DUse AWS Glue to process incoming data. Use AWS Lambda and S3 Event Notifications to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.
AmazonAmazon-DEA-C01
Q3:
A company stores employee data in Amazon Redshift A table named Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key. Which queries will MOST increase the speed of a query by using a compound sort key of the table? (Select TWO.)
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ASelect * from Employee where Region ID='North America';
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BSelect * from Employee where Region ID='North America' and Department ID=20;
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CSelect * from Employee where Department ID=20 and Region ID='North America';
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DSelect ' from Employee where Role ID=50;
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ESelect * from Employee where Region ID='North America' and Role ID=50;
AmazonAmazon-DEA-C01
Q4:
A company needs to load customer data that comes from a third party into an Amazon Redshift data warehouse. The company stores order data and product data in the same data warehouse. The company wants to use the combined dataset to identify potential new customers.
A data engineer notices that one of the fields in the source data includes values that are in JSON format.
How should the data engineer load the JSON data into the data warehouse with the LEAST effort?
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AUse the SUPER data type to store the data in the Amazon Redshift table.
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BUse AWS Glue to flatten the JSON data and ingest it into the Amazon Redshift table.
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CUse Amazon S3 to store the JSON data. Use Amazon Athena to query the data.
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DUse an AWS Lambda function to flatten the JSON data. Store the data in Amazon S3.
AmazonAmazon-DEA-C01
Q5:
Two developers are working on separate application releases. The developers have created feature branches named Branch A and Branch B by using a GitHub repository's master branch as the source.
The developer for Branch A deployed code to the production system. The code for Branch B will merge into a master branch in the following week's scheduled application release.
Which command should the developer for Branch B run before the developer raises a pull request to the master branch?
