Amazon Web ServicesAssociate levelDEA-C01Page 4 of 6

DEA-C01 exam dumps: questions 301 to 400 of 549

Page 4 of the free DEA-C01 question bank for the AWS Certified Data Engineer - Associate exam. Questions 301 to 400 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated December 2024

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Free DEA-C01 practice questions

Questions 301 to 305 of 549

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

DEA-C01 Question 301

Single answer

Your company processes customer transaction data daily using an AWS Glue ETL job. Recently, the business team reported inconsistencies in the daily summary report generated by the ETL workflow. Upon investigation, you suspect that some input files were processed multiple times due to a misconfiguration in the workflow. How can you modify the workflow to ensure data is processed exactly once and maintain repeatable business outcomes?

  1. A

    Enable job bookmarks in the AWS Glue ETL job.

  2. B

    Increase the maximum number of retries for the AWS Glue ETL job.

  3. C

    Use Amazon S3 event notifications to trigger the job instead of a scheduled run.

  4. D

    Configure a deduplication step in the ETL job to remove duplicate records.

Show answer and explanation

Correct answer: A

Explanation

Enabling job bookmarks in AWS Glue is the most effective solution to ensure data is processed exactly once. Job bookmarks track the progress of the ETL job, allowing it to process only new or modified data. This prevents the reprocessing of files and ensures consistent and repeatable business outcomes.

  • A. Correct.

    Enabling job bookmarks in AWS Glue ensures that the ETL job processes only new or modified data since the last successful run, preventing duplicate processing and maintaining repeatable outcomes.

  • B. Incorrect.

    Increasing the maximum number of retries does not address the issue of duplicate processing; it only impacts job retry behavior in case of failures.

  • C. Incorrect.

    Using Amazon S3 event notifications to trigger the job changes the scheduling mechanism but does not prevent duplicate processing of files.

  • D. Incorrect.

    While a deduplication step can help remove duplicate records in the output, it does not address the root cause of the issue, which is the input data being processed multiple times.

DEA-C01 Question 302

Select 3

Your team is designing a data processing pipeline using AWS Glue to transform incoming data for downstream analytics. Recently, you observed that some jobs fail intermittently due to schema mismatches in the incoming data files. To ensure repeatable business outcomes, what steps should you take to identify and resolve this issue?

  1. A

    Enable AWS Glue job bookmarks to track processed data and prevent reprocessing.

  2. B

    Implement AWS Glue DynamicFrames to handle schema inconsistencies during transformation.

  3. C

    Use AWS Glue Data Catalog to enforce a strict schema for incoming datasets.

  4. D

    Enable AWS Glue's built-in error-handling capabilities with retry policies.

  5. E

    Configure Amazon CloudWatch Logs for the AWS Glue job to monitor for schema-related errors.

Show answer and explanation

Correct answers: B, C, E

Explanation

To troubleshoot and resolve schema mismatch issues in AWS Glue, you can use DynamicFrames to handle schema inconsistencies, enforce a strict schema using the Glue Data Catalog, and monitor job logs via CloudWatch to identify errors. These steps ensure repeatable and reliable data processing outcomes.

  • A. Incorrect.

    While AWS Glue job bookmarks help prevent reprocessing of previously processed data, they do not directly address schema mismatch issues.

  • B. Correct.

    AWS Glue DynamicFrames are designed to handle semi-structured data and schema inconsistencies, making them suitable for resolving schema mismatch problems.

  • C. Correct.

    Using the AWS Glue Data Catalog to enforce a strict schema ensures that incoming data adheres to a predefined structure, reducing the chances of schema mismatches.

  • D. Incorrect.

    AWS Glue's error-handling capabilities with retry policies are useful for transient errors but do not directly resolve schema mismatch issues.

  • E. Correct.

    Configuring Amazon CloudWatch Logs can help identify and diagnose schema-related errors, providing valuable insights into the root cause of the problem.

DEA-C01 Question 303

Select 3

Your company processes sales transaction data using an AWS Glue ETL job, which runs daily to populate an Amazon Redshift data warehouse. Recently, the business has reported inconsistent data in the dashboards due to missing or incomplete records. How can you maintain consistent and repeatable data processing outcomes while troubleshooting the issue?

  1. A

    Use AWS Glue job bookmarks to ensure that the ETL job processes only new or updated data.

  2. B

    Enable CloudWatch Logs for the AWS Glue job to monitor for errors or failed tasks during ETL execution.

  3. C

    Switch to an on-demand AWS Glue job trigger to manually review data before every job execution.

  4. D

    Implement Amazon Redshift's COPY command with the COMPUPDATE option to automatically handle schema changes.

  5. E

    Set up data quality checks using AWS Deequ or custom validation scripts to verify data completeness.

Show answer and explanation

Correct answers: A, B, E

Explanation

To maintain consistent and repeatable data processing outcomes, you should leverage AWS Glue job bookmarks to process data incrementally, enable logging to troubleshoot errors, and implement data quality checks to validate the integrity of the processed data. These practices collectively ensure that your ETL pipeline is reliable and produces accurate results for downstream analytics.

  • A. Correct.

    Using AWS Glue job bookmarks ensures that your ETL job processes only new or updated data, preventing duplication or missing records across job runs. This is crucial for repeatable outcomes.

  • B. Correct.

    Enabling CloudWatch Logs provides visibility into ETL job execution, helping troubleshoot errors or identify issues during data processing.

  • C. Incorrect.

    Switching to an on-demand trigger introduces manual intervention, which can lead to inconsistent execution schedules and increased operational overhead. This is not a recommended practice for maintaining repeatable outcomes.

  • D. Incorrect.

    While the COMPUPDATE option in Amazon Redshift's COPY command is useful for handling schema changes, it does not address the issue of missing or incomplete records in the ETL pipeline.

  • E. Correct.

    Data quality checks using AWS Deequ or custom validation scripts ensure data completeness and consistency, which is a critical step to maintain repeatable and reliable outcomes.

DEA-C01 Question 304

Select 3

A data engineering team processes customer transaction data daily using an AWS Glue ETL job and stores the results in Amazon S3. Recently, the team noticed unexpected discrepancies in the output, leading to incorrect business insights. You are tasked with ensuring the data processing pipeline consistently produces accurate and repeatable results. What actions should you take to address the issue?

  1. A

    Enable AWS Glue job bookmarking to ensure incremental processing of new data.

  2. B

    Implement input data validation checks before processing to ensure data quality.

  3. C

    Use AWS Glue job parameters to pass dynamic input configurations during ETL execution.

  4. D

    Enable CloudWatch Logs for the AWS Glue job to monitor errors and troubleshoot failures.

  5. E

    Switch to an on-demand AWS Lambda function for ETL processing for better scalability.

Show answer and explanation

Correct answers: A, B, D

Explanation

To ensure consistent and repeatable business outcomes, you must address potential data quality issues, monitor processing for errors, and optimize the ETL pipeline for incremental data processing. AWS Glue job bookmarking prevents reprocessing old data, input data validation ensures only clean data is processed, and CloudWatch Logs provide insights into errors for troubleshooting. Together, these actions help maintain and troubleshoot the pipeline effectively.

  • A. Correct.

    Enabling AWS Glue job bookmarking ensures that the ETL job processes only new or updated data, which helps maintain consistency and avoids reprocessing errors. This is critical for repeatable business outcomes.

  • B. Correct.

    Input data validation checks are essential to ensure the quality and accuracy of the data being processed. Without validation, corrupt or incomplete data can lead to discrepancies in the output.

  • C. Incorrect.

    Using AWS Glue job parameters is helpful for flexibility in ETL processing but does not directly address the data consistency or troubleshooting issues in this scenario.

  • D. Correct.

    Enabling CloudWatch Logs for the AWS Glue job provides visibility into errors or anomalies during processing, making it easier to troubleshoot issues and ensure repeatable outcomes.

  • E. Incorrect.

    Switching to AWS Lambda for ETL processing is not a direct solution to the problem of discrepancies in the current pipeline and may introduce additional complexity.

DEA-C01 Question 305

Select 2

Your organization processes financial transaction data using AWS Glue. Recently, there have been inconsistencies in the data outputs, leading to errors in downstream analytics. To ensure repeatable and reliable data processing, you need to implement measures to identify issues early and improve the consistency of the data pipeline. Which of the following actions should you take? (Select TWO)

  1. A

    Implement AWS Glue job bookmarks to ensure idempotent processing of data.

  2. B

    Enable AWS CloudTrail logs to monitor all API activities in the data pipeline.

  3. C

    Use AWS Glue Workflows to define, track, and monitor the sequence of data processing steps.

  4. D

    Implement AWS Glue Data Quality rules to validate data at various stages of the pipeline.

  5. E

    Use Amazon QuickSight to visualize inconsistencies in the data processing pipeline in real-time.

Show answer and explanation

Correct answers: A, D

Explanation

To ensure repeatable and consistent data processing, it is important to implement mechanisms that prevent processing errors and validate the quality of incoming or processed data. Job bookmarks in AWS Glue help maintain idempotency, ensuring that data is not reprocessed unnecessarily. AWS Glue Data Quality rules provide the ability to validate and troubleshoot inconsistencies in the data pipeline, allowing for early detection and resolution of issues. These measures work together to maintain reliable and repeatable business outcomes.

  • A. Correct.

    Implementing AWS Glue job bookmarks ensures that the data pipeline only processes new or unprocessed data, which helps maintain consistency and idempotency in repeatable processing.

  • B. Incorrect.

    While AWS CloudTrail can help monitor API calls, it does not directly address inconsistencies in data outputs or processing logic.

  • C. Incorrect.

    AWS Glue Workflows are useful for orchestrating and monitoring the steps in a pipeline but do not detect or resolve issues causing data inconsistencies.

  • D. Correct.

    AWS Glue Data Quality rules allow you to define and enforce constraints on your data, which helps identify and troubleshoot inconsistencies early in the pipeline.

  • E. Incorrect.

    Amazon QuickSight is a visualization tool but does not directly contribute to maintaining or troubleshooting data processing pipelines.

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DEA-C01 practice questions 301 to 400 of 549

Every question has a page with the answer and explanation. Numbers are stable, so you can bookmark or share them. The bank is split into 6 pages of up to 100 questions.

  1. 301.Your company processes customer transaction data daily using an AWS Glue ETL job. Recently, the business team...
  2. 302.Your team is designing a data processing pipeline using AWS Glue to transform incoming data for downstream...
  3. 303.Your company processes sales transaction data using an AWS Glue ETL job, which runs daily to populate an...
  4. 304.A data engineering team processes customer transaction data daily using an AWS Glue ETL job and stores the...
  5. 305.Your organization processes financial transaction data using AWS Glue. Recently, there have been...
  6. 306.You are designing a data processing pipeline where you need to ingest real-time streaming data into Amazon...
  7. 307.You are a data engineer tasked with processing large-scale event data from IoT devices in real-time. You use...
  8. 308.You are building a data processing application on AWS that processes large datasets in real-time and...
  9. 309.You are designing a data processing pipeline on AWS for a retail application that processes orders in...
  10. 310.You are designing a data processing pipeline on AWS and want to process large amounts of data in near...
  11. 311.A data engineering team needs to process large-scale data transformations, run SQL queries on a data...
  12. 312.You are designing a data processing pipeline for a large e-commerce company. The pipeline needs to process...
  13. 313.You are a data engineer tasked with processing and transforming large datasets in the AWS cloud. You want to...
  14. 314.You are working on a big data project where data needs to be processed, transformed, and analyzed. You want...
  15. 315.A data engineering team needs to process large-scale data transformations and run custom scripts as part of...
  16. 316.You are a data engineer for a retail company. The company wants to analyze customer purchasing patterns to...
  17. 317.A company ingests real-time streaming data from IoT sensors into an Amazon Kinesis Data Stream. They need to...
  18. 318.You are a data engineer at a retail company. The company collects transactional data from its online...
  19. 319.A company is using Amazon S3 to store large amounts of raw log files generated by its web application. The...
  20. 320.A retail company collects transactional data from its point-of-sale systems and stores it in an Amazon S3...
  21. 321.You are working as a data engineer for an e-commerce company that processes large volumes of clickstream...
  22. 322.You are designing a data pipeline to process large volumes of log data from multiple sources in near...
  23. 323.A company is building a data lake using Amazon S3 to store raw and processed data. They want to enforce...
  24. 324.A company is building a data pipeline to process large volumes of clickstream data in real-time. They need to...
  25. 325.You are a Data Engineer designing a real-time data processing pipeline for an e-commerce platform. The...
  26. 326.A company is building a real-time data processing pipeline for streaming sensor data from IoT devices. They...
  27. 327.You are designing a data processing pipeline in AWS for a company that requires processing high volumes of...
  28. 328.You are designing a data processing pipeline for a financial analytics application. The pipeline ingests...
  29. 329.A data engineering team is designing a data processing pipeline for analyzing customer transaction data. The...
  30. 330.A data engineering team is designing a data processing pipeline on AWS to analyze streaming data from IoT...
  31. 331.You are working as a data engineer for a retail company. The company stores sales data in an Amazon Redshift...
  32. 332.You are working as a data engineer for a company that uses Amazon Redshift as their data warehouse. You are...
  33. 333.You are managing a data pipeline for an e-commerce company, and the sales data is stored in an Amazon Aurora...
  34. 334.You are designing a data pipeline that ingests customer orders into an Amazon RDS PostgreSQL database. You...
  35. 335.You are working with an Amazon Redshift cluster and have a table named 'sales' that stores transactional...
  36. 336.You are working as a data engineer at a company that uses Amazon Redshift to store large volumes of...
  37. 337.You are a data engineer at a retail company that stores large amounts of sales data in Amazon Redshift. The...
  38. 338.You are working as a Data Engineer for a company that stores its sales data in Amazon Redshift. The business...
  39. 339.A company has stored its sales data in Amazon S3 in Parquet format. The data engineering team needs to create...
  40. 340.You are a data engineer working for a company that uses Amazon Redshift as their data warehouse. The business...
  41. 341.You are a data engineer tasked with building a data pipeline on AWS for processing customer transaction data....
  42. 342.An e-commerce company is analyzing customer reviews stored in an Amazon S3 bucket. These reviews contain...
  43. 343.A company is ingesting customer purchase data from multiple sources into an Amazon S3 bucket for downstream...
  44. 344.You are designing a data pipeline using AWS Glue to process customer transaction data stored in Amazon S3....
  45. 345.You are working as a Data Engineer for a company that collects user clickstream data from its website. The...
  46. 346.You are working as a data engineer for an e-commerce platform. The sales data for each transaction is stored...
  47. 347.A retail company stores its sales data in an Amazon S3 bucket as CSV files. The data includes columns for...
  48. 348.You are working as a data engineer for a retail company that processes daily sales data in Amazon Redshift....
  49. 349.You are working as a data engineer at a company that processes customer transaction data in AWS. The data is...
  50. 350.A retail company stores sales data in an Amazon S3 bucket as CSV files. The data includes columns for...
  51. 351.You are responsible for maintaining a data pipeline in AWS that processes real-time streaming data using...
  52. 352.You are managing a data pipeline in AWS Glue that processes large volumes of streaming data from an Amazon...
  53. 353.You are managing a data pipeline in AWS that ingests data from Amazon Kinesis Data Streams, processes it...
  54. 354.You are a data engineer managing an AWS-based data pipeline that uses AWS Glue for ETL tasks. The pipeline...
  55. 355.A data engineering team is tasked with building a real-time analytics application on AWS. The application...
  56. 356.You are working as a Data Engineer for a company that processes e-commerce order data. The raw data is stored...
  57. 357.You are a data engineer working for a company that processes large-scale streaming data from IoT sensors...
  58. 358.Your team is building a data processing workflow to analyze customer transaction data stored in an Amazon S3...
  59. 359.A company is building a data lake on Amazon S3 and wants to query the data using Amazon Athena. However, the...
  60. 360.You are building a serverless application on AWS and need to log application data for debugging and...
  61. 361.You are working as a Data Engineer for a company that runs a real-time application hosted on Amazon ECS. The...
  62. 362.You are designing a logging solution for an application hosted on Amazon EC2 instances. The application...
  63. 363.A data engineering team is building a microservices-based application on AWS. They need to log application...
  64. 364.You are working on an application that processes and analyzes e-commerce transactions in real-time using AWS...
  65. 365.You are working as a Data Engineer at an organization that processes a large volume of clickstream data using...
  66. 366.You are a data engineer working for a company that uses Amazon Redshift as its primary data warehouse. Your...
  67. 367.You are managing a data pipeline in AWS that processes large amounts of data stored in Amazon S3 and writes...
  68. 368.A data engineering team is designing an Amazon Redshift cluster to process a large dataset for their...
  69. 369.You are designing a data pipeline in AWS that processes terabytes of data daily using Amazon Redshift. The...
  70. 370.A data engineering team needs to monitor and log access to an Amazon S3 bucket that stores critical business...
  71. 371.Your company wants to monitor and log access to AWS services to improve security and compliance. Which of the...
  72. 372.A data engineering team needs to log all access requests made to their AWS S3 buckets for compliance...
  73. 373.You are a data engineer managing an AWS environment that processes sensitive customer data. To ensure...
  74. 374.You are a data engineer tasked with ensuring all access to your company's AWS services is logged for security...
  75. 375.Your company stores sensitive data in Amazon S3 buckets and needs to ensure that any unauthorized access...
  76. 376.A company is using Amazon S3 to store sensitive customer data. To comply with security and compliance...
  77. 377.Your organization stores sensitive customer information in Amazon S3 buckets. To comply with security and...
  78. 378.Your organization is using Amazon S3 to store sensitive customer data, and you are tasked with implementing a...
  79. 379.A financial institution is required to monitor its sensitive data stored in Amazon S3 buckets and track...
  80. 380.Your organization has implemented an AWS Glue ETL job to process incoming data from multiple sources into an...
  81. 381.A company is using AWS Glue to process incoming data from multiple sources. During the ETL process, some data...
  82. 382.Your team is responsible for building a data pipeline in AWS to process customer transaction data. The data...
  83. 383.You are working as a Data Engineer for a retail company that processes large datasets of transaction logs...
  84. 384.A data engineering team is using AWS Glue to process incoming data from an S3 bucket. During a recent review,...
  85. 385.You are a data engineer tasked with designing a data lake architecture for a retail company on AWS. The...
  86. 386.You are designing a data pipeline for a retail company that collects real-time sales transactions from...
  87. 387.You are designing a data pipeline for a real-time analytics application. The incoming data is ingested from a...
  88. 388.You are designing a data pipeline in AWS to process large amounts of streaming data from IoT devices in...
  89. 389.You are a data engineer managing a data pipeline that ingests streaming data from IoT devices into Amazon...
  90. 390.You are designing a data pipeline on AWS to process a large dataset stored in Amazon S3. To speed up your...
  91. 391.You are working on an AWS Glue ETL job to process a large dataset stored in Amazon S3 for machine learning...
  92. 392.You are working on a machine learning pipeline using Amazon SageMaker, and your dataset contains 1 billion...
  93. 393.You are working on a data pipeline in AWS Glue that processes a large dataset stored in Amazon S3. To...
  94. 394.You are working on a data engineering project where you need to train a machine learning model on a large...
  95. 395.You are working with an Apache Spark application on Amazon EMR that processes a large dataset. During...
  96. 396.You are working on an ETL pipeline in AWS Glue to process data from an S3 bucket. During the data processing,...
  97. 397.You are designing an Amazon EMR-based solution to process a large dataset stored in Amazon S3 using Apache...
  98. 398.You are working on optimizing a data pipeline in AWS Glue that processes large datasets stored in Amazon S3....
  99. 399.You are designing a data processing pipeline in AWS Glue. During the processing of a large dataset, you...
  100. 400.You are a data engineer working for an e-commerce company. The company ingests sales data from multiple...

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