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Databricks Data Engineer Associate exam dumps: questions 301 to 400 of 531

Page 4 of the free Databricks Data Engineer Associate question bank for the Databricks 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 January 2025

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Free Databricks Data Engineer Associate practice questions

Questions 301 to 305 of 531

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

Databricks Data Engineer Associate Question 301

Single answer

You are managing a Delta Lake table in Databricks, and you have recently deleted a large number of records from the table. You want to permanently remove these deleted records to free up storage space. Which of the following statements about the VACUUM command in Delta Lake is correct?

  1. A

    The VACUUM command permanently removes deleted records by default, regardless of whether they are committed or not.

  2. B

    The VACUUM command removes only those deleted files that are no longer referenced by any Delta table commits.

  3. C

    The VACUUM command can be used to recover deleted files if issued within the default retention period.

  4. D

    The VACUUM command requires a table to have a retention period of at least 0 hours.

Show answer and explanation

Correct answer: B

Explanation

The VACUUM command in Delta Lake is used to permanently delete data files that are no longer referenced by any Delta table commit, thereby reclaiming storage space. It does not operate on uncommitted data and respects the retention period configured for safety. The default retention period is 168 hours (7 days), ensuring that data files are not accidentally deleted.

  • A. Incorrect.

    This is incorrect because the VACUUM command only removes files that are no longer referenced by Delta table commits. It does not delete uncommitted data.

  • B. Correct.

    This is correct because the VACUUM command ensures that only files not referenced by any active Delta table version (commits) are permanently deleted.

  • C. Incorrect.

    This is incorrect because the VACUUM command is used to permanently delete unused data files, not to recover them.

  • D. Incorrect.

    This is incorrect because the minimum retention period for the VACUUM command is 168 hours by default (7 days), unless explicitly overridden, but it cannot be set to 0 hours.

Databricks Data Engineer Associate Question 302

Single answer

You are working on a Delta Lake table in Databricks that has undergone multiple delete operations. To reclaim storage space, you decide to run the VACUUM command. What happens when you run VACUUM on the table?

  1. A

    VACUUM removes files that are no longer referenced by the Delta table's transaction log.

  2. B

    VACUUM deletes active data files that are currently in use by the Delta table.

  3. C

    VACUUM commits the removal of deleted files only after the retention period has expired.

  4. D

    VACUUM forces an immediate physical deletion of all historical data files, regardless of the retention period.

Show answer and explanation

Correct answer: A

Explanation

The VACUUM command in Delta Lake removes files that are no longer referenced by the transaction log, reclaiming storage. These files are typically old versions of data or data files that were deleted in previous operations. VACUUM ensures that only unreferenced files beyond the retention threshold are physically deleted.

  • A. Correct.

    Correct. VACUUM in Delta Lake reclaims storage by removing files that are no longer referenced by the transaction log, ensuring they are not needed for future table operations.

  • B. Incorrect.

    Incorrect. VACUUM does not delete active data files. It only removes files that have been marked as no longer needed by the Delta table's transaction log.

  • C. Incorrect.

    Incorrect. VACUUM does not involve a retention period for committing deletes. It removes files that are already unreferenced after the specified retention period has passed.

  • D. Incorrect.

    Incorrect. VACUUM respects the default or user-specified retention period and does not immediately delete all historical data files.

Databricks Data Engineer Associate Question 303

Single answer

When using Delta Lake's OPTIMIZE command, which type of files does it compact?

  1. A

    Small files created by frequent writes or updates

  2. B

    Large files that exceed a predefined size threshold

  3. C

    Corrupted files in the Delta Lake table

  4. D

    Files that are not part of the Delta log

Show answer and explanation

Correct answer: A

Explanation

Delta Lake's OPTIMIZE command is used to improve query performance by compacting small files into fewer, larger files. This process addresses the problem of small files generated during frequent writes or updates, which can otherwise degrade performance due to higher metadata overhead.

  • A. Correct.

    Correct. The OPTIMIZE command in Delta Lake is specifically designed to compact small files into larger ones to improve query performance and reduce metadata overhead.

  • B. Incorrect.

    Incorrect. Large files exceeding the size threshold are not compacted by OPTIMIZE. The focus is on merging small files, not splitting large ones.

  • C. Incorrect.

    Incorrect. OPTIMIZE does not handle corrupted files. Handling corrupted files usually involves manual intervention or specific tools for data recovery.

  • D. Incorrect.

    Incorrect. OPTIMIZE only works on files that are part of the Delta log. Files outside the Delta log are ignored.

Databricks Data Engineer Associate Question 304

Select 2

In a Databricks Delta table, which type of files are compacted by the OPTIMIZE command?

  1. A

    Small data files generated due to frequent small writes

  2. B

    Large data files that exceed the maximum file size threshold

  3. C

    Parquet files that are fragmented across multiple partitions

  4. D

    Checkpoint files used for Delta Lake transaction logs

  5. E

    Files stored in external storage systems like S3 or ADLS

Show answer and explanation

Correct answers: A, C

Explanation

The OPTIMIZE command in Databricks Delta Lake is designed to compact small data files and fragmented files within a Delta table into larger, more efficient Parquet files. This results in improved query performance and storage efficiency. It does not target large files, checkpoint files, or non-Delta table files stored in external storage directly.

  • A. Correct.

    Small data files generated by frequent small writes are compacted by the OPTIMIZE command to improve read performance by reducing the overhead of reading many small files.

  • B. Incorrect.

    Large data files that exceed the maximum size threshold are not compacted by the OPTIMIZE command since it focuses on merging small files to meet the configured target file size.

  • C. Correct.

    Parquet files fragmented across multiple partitions are compacted by the OPTIMIZE command to ensure better organization and efficiency in querying.

  • D. Incorrect.

    Checkpoint files used for Delta Lake transaction logs are not compacted by the OPTIMIZE command, as they serve a different purpose related to transaction consistency.

  • E. Incorrect.

    Files stored in external storage systems like S3 or ADLS are not specifically compacted by the OPTIMIZE command unless they are part of a Delta table.

Databricks Data Engineer Associate Question 305

Select 2

In Databricks, which kinds of files does the OPTIMIZE command compact when used on a Delta table?

  1. A

    Small files to create fewer larger files

  2. B

    Corrupted files that fail to load into the Delta table

  3. C

    Large files to split them into smaller files

  4. D

    Files within a Delta table that belong to the same partition

Show answer and explanation

Correct answers: A, D

Explanation

The OPTIMIZE command in Databricks is primarily used to compact small files within a Delta table into larger files, which minimizes the overhead of managing a large number of small files and improves query performance. Additionally, OPTIMIZE operates within the same partition of a Delta table to ensure data locality and efficient storage organization. It does not handle corrupted files or split large files into smaller ones.

  • A. Correct.

    Correct: OPTIMIZE is used to compact small files into larger files, improving query performance by reducing the number of files read during execution.

  • B. Incorrect.

    Incorrect: OPTIMIZE does not repair or handle corrupted files. Such files need to be addressed separately through data repair or cleaning processes.

  • C. Incorrect.

    Incorrect: OPTIMIZE does not split large files into smaller ones. Instead, its purpose is to consolidate small files into fewer, larger files.

  • D. Correct.

    Correct: OPTIMIZE works on files within the same partition of a Delta table to compact them, ensuring efficient data organization and retrieval.

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Databricks Data Engineer Associate practice questions 301 to 400 of 531

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.You are managing a Delta Lake table in Databricks, and you have recently deleted a large number of records...
  2. 302.You are working on a Delta Lake table in Databricks that has undergone multiple delete operations. To reclaim...
  3. 303.When using Delta Lake's OPTIMIZE command, which type of files does it compact?
  4. 304.In a Databricks Delta table, which type of files are compacted by the OPTIMIZE command?
  5. 305.In Databricks, which kinds of files does the OPTIMIZE command compact when used on a Delta table?
  6. 306.When using the OPTIMIZE command in Databricks on a Delta table, which type of files does it compact?
  7. 307.Which of the following types of files does the OPTIMIZE command in Databricks primarily compact?
  8. 308.A data engineering team is tasked with creating a new table in Databricks that contains aggregated sales data...
  9. 309.A data engineering team is tasked with creating a new table in Databricks from the results of a complex...
  10. 310.A data engineering team is building an ETL pipeline in Databricks to create a new table that consolidates...
  11. 311.A data engineering team is tasked with creating a new table from an existing one while simultaneously...
  12. 312.A Databricks engineer is tasked with creating a new table in a Delta Lake to store aggregated sales data. The...
  13. 313.You are working with a Delta table named salesdata in Databricks. The table contains the columns productid,...
  14. 314.A data engineer is working on a Delta table in Databricks containing transaction data. The table includes the...
  15. 315.You are tasked with creating a Delta table in Databricks to store customer orders. The table should include a...
  16. 316.You are tasked with creating a Delta table to store sales data in Databricks. The table must include a...
  17. 317.You are working with a Delta table in Databricks and need to add a generated column that calculates the total...
  18. 318.You are working in Databricks and need to add a comment to an existing Delta table named 'salesdata' within...
  19. 319.You are working with a Delta table named 'salesdata' in your Databricks environment. You want to add a...
  20. 320.You are working on a Databricks Lakehouse project and need to add a comment to an existing Delta table to...
  21. 321.You are working on a Databricks Lakehouse project where you need to document the purpose of a table called...
  22. 322.You are a data engineer managing a Delta table named salesdata in your Databricks workspace. Your team has...
  23. 323.A data engineering team is working on a Delta table named 'salesdata' stored in the Databricks Lakehouse....
  24. 324.You are working with a Databricks SQL table named 'salesdata' and want to update it with new data from a...
  25. 325.You are working on a Databricks project where you need to create a table named 'salesdata' in the 'analytics'...
  26. 326.You are working on a Databricks table named 'salesdata' in the 'analytics' database. The table already...
  27. 327.You are working on a Databricks project where you need to update a table named 'salesdata' with the latest...
  28. 328.A data engineering team is working on a Delta Lake table in Databricks. They need to update the contents of...
  29. 329.You are working on a Databricks project where you need to update a table with new data. The table is...
  30. 330.You are working on a Delta Lake table in Databricks. The table is used to store daily sales data, and you...
  31. 331.You are working on a Databricks project where you need to update an existing table with the latest data. The...
  32. 332.You are working on a Databricks project where you need to refresh a Delta table with new data from a source...
  33. 333.You are working with a Delta Lake table that stores customer data, and you receive a daily feed containing...
  34. 334.A retail company maintains a Delta Lake table to track inventory levels for all its products. The table...
  35. 335.You are working with a Delta table in Databricks that stores user account information. A new dataset is...
  36. 336.You are working with a Delta Lake table named salesdata that contains daily sales records. A new dataset is...
  37. 337.You are working with a Delta table named 'customertransactions' that tracks customer purchases. Your team...
  38. 338.You are working with a Delta table in Databricks that tracks customer orders. The table occasionally receives...
  39. 339.You are working with a Delta Lake table named customerdata, which may contain duplicate records due to...
  40. 340.You are working with a Delta Lake table named 'customers' that contains duplicate data due to multiple...
  41. 341.A data engineering team is tasked with maintaining a Delta Lake table containing customer transaction data....
  42. 342.A data engineering team is tasked with deduplicating data in a Delta Lake table during an ETL pipeline...
  43. 343.A data engineering team is tasked with updating a Delta Lake table that contains customer records. They need...
  44. 344.A data engineering team is tasked with updating a Delta table that tracks customer transactions. The table...
  45. 345.A data engineering team is tasked with maintaining a Delta Lake table that stores customer transaction data....
  46. 346.A data engineering team is tasked with maintaining a Delta table that holds customer information. The table...
  47. 347.A data engineering team is tasked with maintaining a Delta Lake table that tracks customer transactions. They...
  48. 348.A data engineer is using the COPY INTO statement to load data from a cloud storage location into a Delta...
  49. 349.A data engineer is using the COPY INTO command to load data from a Delta table into another Delta table....
  50. 350.A Data Engineer executes a COPY INTO statement to load data into a Delta table. However, upon running the...
  51. 351.You are using a COPY INTO statement to ingest data from a cloud storage location into a Delta Lake table in...
  52. 352.A data engineer is using the COPY INTO statement to load data from a cloud storage location into a Delta...
  53. 353.You are working on a Databricks project where new data files are being uploaded hourly into an Azure Data...
  54. 354.You are working on a data pipeline that ingests daily incrementally updated CSV files from an external source...
  55. 355.You are working as a data engineer for a company that ingests daily transactional data from an external...
  56. 356.You are working on a Databricks Lakehouse platform and need to load incremental data from a directory in an...
  57. 357.You are working on a Databricks project where you need to incrementally load new data files from an external...
  58. 358.You are working on a Databricks notebook and need to load data from a cloud storage location into a Delta...
  59. 359.You are tasked with loading new JSON data from a cloud storage location into an existing Delta table in...
  60. 360.A data engineer is tasked with loading data from a cloud storage location into a Delta table using the COPY...
  61. 361.You are working on a Databricks notebook and need to incrementally load new data from an external S3 bucket...
  62. 362.You are working on a Databricks pipeline to process and load CSV files into a Delta table named salesdata....
  63. 363.You are tasked with creating a new Delta Live Tables (DLT) pipeline in Databricks. Which of the following...
  64. 364.You are tasked with setting up a new Delta Live Tables (DLT) pipeline to process streaming data in...
  65. 365.You are tasked with creating a new Delta Live Tables (DLT) pipeline in Databricks to transform raw data into...
  66. 366.You are tasked with creating a new Databricks Delta Live Tables (DLT) pipeline. Which of the following...
  67. 367.You are tasked with creating a new Databricks Delta Live Tables (DLT) pipeline for your organization's data...
  68. 368.You are designing a data pipeline in Databricks using Delta Live Tables (DLT). As part of the pipeline...
  69. 369.You are designing a Delta Live Tables (DLT) pipeline to process data for a real-time analytics application....
  70. 370.You are building a data pipeline in Databricks using Delta Live Tables (DLT). As part of the configuration,...
  71. 371.You are designing a data pipeline in Databricks using Delta Live Tables (DLT). While configuring the...
  72. 372.You are designing a data pipeline in Databricks using a notebook. You want to configure the pipeline to use...
  73. 373.A data engineering team is tasked with designing a Delta Live Table (DLT) pipeline for processing real-time...
  74. 374.You are designing a data pipeline in Databricks, where the data must be processed with minimal latency to...
  75. 375.You are designing a data pipeline in Databricks to process streaming data from IoT devices. The pipeline...
  76. 376.You are designing a data pipeline in Databricks for a streaming application. The pipeline must minimize...
  77. 377.A data engineering team is deciding between using a triggered pipeline and a continuous pipeline in...
  78. 378.You are tasked with identifying which source location in your Databricks workspace is utilizing Auto Loader....
  79. 379.A data engineering team is working on a Databricks project and is using Auto Loader to ingest data from...
  80. 380.You are working on a Databricks project where multiple source locations, such as cloud storage buckets, are...
  81. 381.You are tasked with analyzing a Databricks workspace to identify which source location is utilizing Auto...
  82. 382.You are tasked with identifying which source location in a Databricks workspace is utilizing Auto Loader for...
  83. 383.A company wants to continuously ingest JSON files containing customer transaction data from a cloud storage...
  84. 384.You are tasked with setting up a data pipeline to process files arriving in a cloud storage location. The...
  85. 385.You are working on a data pipeline that ingests real-time log files generated by various applications into a...
  86. 386.A data engineering team is tasked with ingesting data from a cloud storage location where new files are...
  87. 387.You are working on a data engineering project where a large volume of JSON files is continuously being...
  88. 388.A data engineer is using Auto Loader to ingest data from a JSON source. However, all the inferred column...
  89. 389.You are using Auto Loader to ingest JSON files into a Delta table. After inspecting the schema inferred by...
  90. 390.A data engineer is using Databricks Auto Loader to ingest JSON data into a Delta table. However, all the...
  91. 391.You are using Auto Loader to ingest JSON files into a Delta Lake table. However, you notice that all the...
  92. 392.You are using Auto Loader to ingest data from a JSON source into a Delta table, but you notice that all the...
  93. 393.You have created a Delta table with a NOT NULL constraint on one of its columns. During a batch write...
  94. 394.You are working with a Delta table in Databricks that enforces a NOT NULL constraint on a column. You attempt...
  95. 395.You are working with a Delta Lake table in Databricks that enforces a NOT NULL constraint on a specific...
  96. 396.While working with Delta Lake on Databricks, a data engineer attempts to write data into a table that has a...
  97. 397.You are working with a Delta Lake table in Databricks that has a NOT NULL constraint on a column. During an...
  98. 398.You are working with a Delta table in Databricks and have enforced a unique constraint on a column. You are...
  99. 399.A Delta table in your Databricks environment has a CHECK constraint to ensure that the 'quantity' column must...
  100. 400.You are a data engineer managing a Delta table with a primary key constraint. During an upsert operation...

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