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Databricks Data Engineer Associate exam dumps: questions 101 to 200 of 531

Page 2 of the free Databricks Data Engineer Associate question bank for the Databricks Certified Data Engineer Associate exam. Questions 101 to 200 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 101 to 105 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 101

Single answer

A data engineer is working with a Databricks environment and has created a table using the following SQL command: CREATE TABLE external_table USING PARQUET OPTIONS ('path' '/mnt/data/external-source'). They want to confirm if this table is a Delta Lake table. Which of the following statements is correct?

  1. A

    The table is a Delta Lake table because Delta Lake is the default format for all tables in Databricks.

  2. B

    The table is not a Delta Lake table because the USING PARQUET clause was used instead of USING DELTA.

  3. C

    The table is a Delta Lake table because it is stored on the Databricks File System (DBFS).

  4. D

    The table is not a Delta Lake table because it was created with an external data source.

Show answer and explanation

Correct answer: B

Explanation

To determine whether a table is a Delta Lake table, the table creation command must specify the Delta format explicitly with USING DELTA. In this scenario, the USING PARQUET clause indicates that the table is a Parquet table, not a Delta Lake table. The storage location or the use of external data sources does not inherently determine a table's format.

  • A. Incorrect.

    Delta Lake is not the default format in Databricks; the format must be explicitly specified using USING DELTA or through configurations.

  • B. Correct.

    This is correct because the USING PARQUET clause explicitly specifies that the table uses the Parquet format, not Delta Lake.

  • C. Incorrect.

    The storage location (e.g., DBFS) does not determine whether a table is a Delta Lake table; the format specified during table creation does.

  • D. Incorrect.

    Using an external data source does not prevent a table from being a Delta Lake table if USING DELTA is specified.

Databricks Data Engineer Associate Question 102

Single answer

You are tasked with analyzing a table in Databricks, and you need to determine whether it is a Delta Lake table or not. Which of the following indicates that the table is NOT a Delta Lake table?

  1. A

    The table's storage format is 'csv'.

  2. B

    The table contains a '_delta_log' directory in its storage path.

  3. C

    The table's metadata includes 'delta' as the format.

  4. D

    The table supports ACID transactions.

Show answer and explanation

Correct answer: A

Explanation

Delta Lake tables have specific characteristics, such as the presence of a '_delta_log' directory, support for ACID transactions, and metadata indicating the 'delta' format. External sources like CSV, JSON, or Parquet files are not Delta Lake tables unless explicitly converted. Identifying the storage format as 'csv' confirms that the table is not a Delta Lake table.

  • A. Correct.

    Correct: A table stored in 'csv' format is not a Delta Lake table since Delta Lake uses its own format and metadata structure.

  • B. Incorrect.

    Incorrect: The presence of a '_delta_log' directory indicates that the table is a Delta Lake table because this directory is exclusive to Delta Lake.

  • C. Incorrect.

    Incorrect: If the table's metadata specifies 'delta' as the format, it confirms that the table is a Delta Lake table.

  • D. Incorrect.

    Incorrect: Delta Lake tables support ACID transactions, which is one of their key features and differentiators.

Databricks Data Engineer Associate Question 103

Select 3

You are working on a Databricks project where multiple tables are stored in different formats. You need to determine which tables are not Delta Lake tables to avoid using Delta-specific features on them. Given the following scenario, which of the following statements correctly help identify a non-Delta table?

  1. A

    The table's storage format is listed as 'parquet' when using the DESCRIBE DETAIL command.

  2. B

    The table's metadata includes a '_delta_log' directory in its storage location.

  3. C

    The table was created using the SQL statement 'CREATE TABLE ... USING DELTA'.

  4. D

    The table's storage format is listed as 'csv' when using the DESCRIBE DETAIL command.

  5. E

    The table does not support ACID transactions or time travel operations.

Show answer and explanation

Correct answers: A, D, E

Explanation

Delta Lake tables use the 'delta' format and have unique features like ACID compliance and time travel. Non-Delta tables will lack these features and may be stored in formats like 'parquet' or 'csv'. By examining the table format and associated features, you can distinguish Delta tables from non-Delta tables.

  • A. Correct.

    Correct. A table stored in 'parquet' format is not a Delta table. Delta tables use the 'delta' format.

  • B. Incorrect.

    Incorrect. The presence of a '_delta_log' directory indicates that the table is a Delta table, as this directory is used to track changes.

  • C. Incorrect.

    Incorrect. If the table was created using 'USING DELTA', it is explicitly a Delta table.

  • D. Correct.

    Correct. A table stored in 'csv' format is not a Delta table, as Delta tables do not use the 'csv' format.

  • E. Correct.

    Correct. Delta tables support ACID transactions and time travel, so a table lacking these features is not a Delta table.

Databricks Data Engineer Associate Question 104

Single answer

You have a table created from an external source in Databricks, and you want to confirm whether it is a Delta Lake table. After running the command DESCRIBE DETAIL table_name, you notice that the format field in the output shows parquet. What does this indicate about the table?

  1. A

    The table is a Delta Lake table because Delta uses Parquet as its storage format.

  2. B

    The table is not a Delta Lake table because Delta tables always show the format as 'delta'.

  3. C

    The table is a Delta Lake table because the DESCRIBE DETAIL command only works with Delta tables.

  4. D

    The table is not a Delta Lake table because the DESCRIBE DETAIL command cannot verify table types.

Show answer and explanation

Correct answer: B

Explanation

Delta Lake tables explicitly store metadata about their format and can be distinguished by the format field in the output of the DESCRIBE DETAIL command. If the field shows 'delta', it is a Delta Lake table. If it shows another format, such as 'parquet', the table is not a Delta Lake table.

  • A. Incorrect.

    This is incorrect. While Delta Lake uses Parquet as its underlying storage format, Delta tables specifically show their format as 'delta' in the DESCRIBE DETAIL output.

  • B. Correct.

    This is correct. Delta tables explicitly show their format as 'delta' in the DESCRIBE DETAIL output. If the format field shows 'parquet', then the table is not a Delta Lake table.

  • C. Incorrect.

    This is incorrect. The DESCRIBE DETAIL command works with both Delta and non-Delta tables, so seeing the output does not guarantee the table is a Delta Lake table.

  • D. Incorrect.

    This is incorrect. The DESCRIBE DETAIL command can verify table types, but in this case, the format field clearly indicates the table type.

Databricks Data Engineer Associate Question 105

Select 3

A data engineering team is working with a new set of tables sourced from an external data warehouse. They want to confirm whether these tables are Delta Lake tables before performing Delta-specific operations. Which of the following methods can be used to identify that the tables are NOT Delta Lake tables?

  1. A

    Query the table properties and check if 'delta' is listed as the table provider.

  2. B

    Inspect the storage location and verify the presence of '_delta_log' folder.

  3. C

    Query the table schema and check for a 'delta_version' column.

  4. D

    Run DESCRIBE DETAIL on the table and check if the format is listed as 'delta'.

  5. E

    Attempt to perform a MERGE operation on the table.

Show answer and explanation

Correct answers: A, B, D

Explanation

Delta Lake tables have specific characteristics, such as 'delta' as the table provider, the presence of a '_delta_log' folder in the storage location, and the format being listed as 'delta' in DESCRIBE DETAIL output. These are reliable methods to verify whether a table is a Delta Lake table or not. Schema inspection or testing specific operations like MERGE are not reliable methods for this purpose.

  • A. Correct.

    Correct: Querying the table properties and checking if 'delta' is listed as the table provider is a valid method to determine if the table is a Delta Lake table. If the table is NOT Delta, the provider will not be 'delta'.

  • B. Correct.

    Correct: Inspecting the storage location for the presence of the '_delta_log' folder is a reliable way to check if the table is a Delta Lake table. If the '_delta_log' folder is absent, the table is not a Delta Lake table.

  • C. Incorrect.

    Incorrect: The presence of a 'delta_version' column in the schema is not a valid method to identify Delta tables. Delta Lake tables do not have a special 'delta_version' column by default.

  • D. Correct.

    Correct: Running DESCRIBE DETAIL on the table and checking the format field is a valid method to confirm if the table uses Delta Lake. If the format is not listed as 'delta', the table is not a Delta Lake table.

  • E. Incorrect.

    Incorrect: Attempting a MERGE operation is not a recommended method to identify Delta tables. While a failure may indicate the table is not Delta, the error could also be due to other reasons such as unsupported operations on the table.

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Databricks Data Engineer Associate practice questions 101 to 200 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. 101.A data engineer is working with a Databricks environment and has created a table using the following SQL...
  2. 102.You are tasked with analyzing a table in Databricks, and you need to determine whether it is a Delta Lake...
  3. 103.You are working on a Databricks project where multiple tables are stored in different formats. You need to...
  4. 104.You have a table created from an external source in Databricks, and you want to confirm whether it is a Delta...
  5. 105.A data engineering team is working with a new set of tables sourced from an external data warehouse. They...
  6. 106.You are tasked with creating two tables in Databricks: one from a JDBC connection to a MySQL database and...
  7. 107.You are tasked with creating a Delta table in Databricks using data from two different sources: a relational...
  8. 108.You are tasked with creating a table in Databricks using data from two sources: a relational database...
  9. 109.You are tasked with creating tables in Databricks for a data engineering project. The first table needs to be...
  10. 110.You are tasked with creating a Delta table in Databricks. The data comes from two sources: a remote MySQL...
  11. 111.You are analyzing a sales dataset in a Delta table to identify rows with missing values in the 'discount'...
  12. 112.You are tasked with analyzing a table named 'salesdata' in Databricks that contains the columns 'orderid',...
  13. 113.A data engineer is tasked with analyzing a dataset containing customer transactions. The engineer needs to...
  14. 114.You are working with a DataFrame in Databricks containing customer data. The DataFrame includes a column...
  15. 115.You are working with a Delta table named 'sales' in Databricks that contains thousands of sales transactions....
  16. 116.You are working on a Databricks notebook to analyze customer data stored in a Delta table. The table contains...
  17. 117.You are working on a Databricks notebook and have a DataFrame named salesdata with the following schema: id...
  18. 118.You are tasked with analyzing a dataset in Databricks that contains a column 'sales'. This column includes...
  19. 119.You are working with a Spark DataFrame salesdata that contains the following columns: id (unique identifier),...
  20. 120.A data engineer is analyzing a dataset in a Databricks notebook. The dataset contains a column named...
  21. 121.You are working with a Delta Lake table named 'salesdata' which contains duplicate rows. You need to...
  22. 122.You are tasked with deduplicating rows in a Delta Lake table named 'salesdata' based on the 'transactionid'...
  23. 123.You are working with a Delta Lake table named 'salesdata' that contains duplicate rows. You need to...
  24. 124.You have a Delta Lake table named 'salesdata' that contains duplicate rows. You want to deduplicate the table...
  25. 125.You are working with a Databricks Delta table named 'salesdata' and are tasked with creating a new table...
  26. 126.You are working on a Databricks notebook and need to create a new table cleanedcustomers from an existing...
  27. 127.You are working on a Databricks notebook and need to create a new table called distinctusers from an existing...
  28. 128.You are working on a Databricks notebook and have an existing table named salesdata that contains duplicate...
  29. 129.You are working on a Databricks notebook and want to create a new table named distinctsales by removing...
  30. 130.You are working on a Databricks notebook, and you are tasked with deduplicating rows in a DataFrame named...
  31. 131.You are working with a Delta table named 'salesdata' that contains multiple rows with duplicate data based on...
  32. 132.You are tasked with removing duplicate rows in a Delta table based on specific columns userid and eventtime....
  33. 133.You are working with a Delta table named 'salesdata' and need to remove duplicate rows based on the...
  34. 134.You are working with a DataFrame in Databricks that contains duplicate rows based on a combination of the...
  35. 135.You are working with a Delta Lake table named customers, which contains the columns customerid, name, and...
  36. 136.You are a data engineer tasked with ensuring the uniqueness of a primary key column, orderid, in a Delta...
  37. 137.You are working on a Delta Lake table in Databricks that contains customer data. Each customer is expected to...
  38. 138.You are working with a large dataset stored in a Delta table in Databricks. The table is expected to have a...
  39. 139.You are working on a Databricks project where you need to validate that the customerid column in a Delta...
  40. 140.You are working with a Databricks Delta table named orders that contains the columns orderid and customerid....
  41. 141.You are working with a Databricks Delta table named 'salesdata' that contains the columns 'customerid' and...
  42. 142.You are working with a Delta table in Databricks containing transaction data. The table has two columns:...
  43. 143.You are working with a Delta Lake table named 'transactions' in Databricks. The table has two columns:...
  44. 144.You are working with a dataset in a Delta table that contains customer transaction records. The table has two...
  45. 145.You are working on a Databricks Delta table named 'salesdata' that contains millions of records. You want to...
  46. 146.You are tasked with validating that a value is NOT present in the 'status' column of a Delta table named...
  47. 147.You are tasked with ensuring that a specific column in a Delta table does not contain a forbidden value...
  48. 148.You are working with a Delta Lake table that contains customer transaction data. The table has a column named...
  49. 149.A data engineering team is tasked with validating that a specific value, 'null', is not present in the...
  50. 150.You are working with a Delta table in Databricks that contains a column named eventtime as a string in the...
  51. 151.You are working with a DataFrame in Databricks that contains a column named eventtime with values stored as...
  52. 152.You are working on a Databricks notebook and have a DataFrame df with a column eventdate that is currently...
  53. 153.You are working with a Delta table named events, which contains a column eventtime stored as a string in the...
  54. 154.You are working with a Delta table in Databricks that contains a column named 'eventdate' stored as a string...
  55. 155.You are working with a DataFrame in Databricks containing a column named eventtime with timestamp values. You...
  56. 156.You are working with a timestamp column named eventtime in a Delta table. You want to extract the year and...
  57. 157.You are working with a timestamp column named eventtime in a Delta table. You need to extract the day of the...
  58. 158.You are working with a DataFrame in Databricks that contains a column named 'eventtime' with timestamp...
  59. 159.You are working on a Databricks notebook and need to extract the year, month, and day from a timestamp column...
  60. 160.You are working with a DataFrame in Databricks that contains a column named 'logdata' with entries like...
  61. 161.You are working with a Delta table containing a column named emailaddress, which stores email addresses in...
  62. 162.You are working with a DataFrame in Databricks that contains a column named email. You need to extract the...
  63. 163.You are working with a Delta table in Databricks that contains customer data. One of the columns,...
  64. 164.You are working with a DataFrame in Databricks that contains a column named logdata with strings in the...
  65. 165.You are working with a DataFrame df in Databricks that contains a nested column user with the fields id and...
  66. 166.You are working with a nested JSON dataset in Databricks that contains the following structure: { "user": {...
  67. 167.You are working with a DataFrame in Databricks that contains a nested JSON column named userinfo with the...
  68. 168.You are working with a DataFrame in Databricks that contains nested JSON data about customer orders. The...
  69. 169.You are working with a JSON dataset in a Databricks notebook. The dataset contains the following nested...
  70. 170.You are working with a dataset in Databricks that contains a column called activities storing an array of...
  71. 171.A data engineer is working with a column in a Delta table that contains arrays of integers. The engineer...
  72. 172.You are working with a dataset in Databricks containing a column named tags, which stores an array of strings...
  73. 173.A data engineering team is working with a JSON dataset containing nested arrays of user activity logs. They...
  74. 174.A data engineer is working with a column of arrays in a Delta table containing customer purchase history. The...
  75. 175.You are working with a Delta table containing a column named rawjson that stores JSON strings. You need to...
  76. 176.You are working with a DataFrame in Databricks that contains a column 'jsondata' with JSON strings, and you...
  77. 177.You are working with a Delta table in Databricks containing a column named eventdata that stores JSON...
  78. 178.You are working with a JSON dataset in Databricks where each record contains a 'user' field as a JSON string....
  79. 179.You are working with a Databricks notebook and have a column named jsondata in a DataFrame that contains JSON...
  80. 180.You have two DataFrames in Databricks: users and orders. The users DataFrame contains columns userid and...
  81. 181.You are working with two tables in Databricks: customers and orders. The customers table contains customer...
  82. 182.You are working with two datasets in Databricks: employees and departments. The employees table contains...
  83. 183.You are working on a Databricks SQL query to analyze sales data. You have two tables: customers and orders....
  84. 184.You are working with two datasets in Databricks: orders and customers. The orders dataset has columns...
  85. 185.You are working with a DataFrame in Databricks that contains a column named nestedarray, which holds deeply...
  86. 186.You are working with a dataset in a Databricks notebook that contains a column 'items' with an array of...
  87. 187.You are working with a dataset in Databricks that contains a column named 'orders', where each record is a...
  88. 188.You are working with a nested JSON dataset in Databricks that contains an array of objects under a column...
  89. 189.You are working with a dataset in Databricks containing sales transactions in the following long format:...
  90. 190.You are working with a dataset in Databricks that tracks monthly sales for different products in a long...
  91. 191.A retail company stores its sales data in a table named 'sales', which includes columns 'storeid', 'month',...
  92. 192.You have a table named salesdata with the following schema: productid region sales...
  93. 193.You are working on a Databricks SQL project and need to define a SQL User-Defined Function (UDF) that takes a...
  94. 194.You are working on a Databricks SQL notebook and need to create a SQL User-Defined Function (UDF) to...
  95. 195.You are working on a Databricks SQL workflow and need to create a SQL User-Defined Function (UDF) that...
  96. 196.You are working with a Databricks SQL environment and need to define a user-defined function (UDF) that...
  97. 197.You are working on a Databricks SQL project and need to create a User-Defined Function (UDF) to calculate the...
  98. 198.You are working with a Databricks notebook and have defined a custom Python function named processdata to...
  99. 199.A data engineering team is working on a Databricks notebook that uses a custom-defined function named...
  100. 200.You are working on a Databricks notebook and need to reuse a function named processdata. The function has...

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