Databricks Databricks-Certified-Data-Engineer-Associate Exam Dumps

Databricks Certified Data Engineer Associate Exam

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Exam Code	Databricks-Certified-Data-Engineer-Associate
Exam Name	Databricks Certified Data Engineer Associate Exam
Questions	230 Questions Answers With Explanation
Update Date	August 03, 2026
Price	Was : $81 Today : $45 Was : $99 Today : $55 Was : $117 Today : $65

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Demo Questions

Question # 1

Which of the following is stored in the Databricks customer's cloud account? 

A. Databricks web application 
B. Cluster management metadata 
C. Repos 
D. Data 
E. Notebooks

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Question # 2

A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task. Which of the following approaches can the data engineer use to set up the new task?

A. They can clone the existing task in the existing Job and update it to run the new notebook. 
B. They can create a new task in the existing Job and then add it as a dependency of the original task. 
C. They can create a new task in the existing Job and then add the original task as a dependency of the new task. 
D. They can create a new job from scratch and add both tasks to run concurrently. 
E. They can clone the existing task to a new Job and then edit it to run the new notebook

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Question # 3

A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values. Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

A. There was a type mismatch between the specific schema and the inferred schema 
B. JSON data is a text-based format 
C. Auto Loader only works with string data 
D. All of the fields had at least one null value 
E. Auto Loader cannot infer the schema of ingested data 

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Question # 4

A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location. Which of the following data entities should the data engineer create?

A. Database 
B. Function 
C. View 
D. Temporary view E. Table 

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Question # 5

A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level. Which of the following tools can the data engineer use to solve this problem?

A. Unity Catalog 
B. Data Explorer 
C. Delta Lake
 D. Delta Live Tables 
E. Auto Loader 

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Question # 6

Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table? 

A. SELECT * FROM my_table WHERE age > 25; 
B. UPDATE my_table WHERE age > 25; 
. DELETE FROM my_table WHERE age > 25; 
D. UPDATE my_table WHERE age <= 25; E. DELETE FROM my_table WHERE age <= 25; 

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Question # 7

In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?

A. When the location of the data needs to be changed 
B. When the target table is an external table
 C. When the source table can be deleted 
D. When the target table cannot contain duplicate records 
E. When the source is not a Delta table 

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Question # 8

An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results. Which of the following approaches can the manager use to ensure the results of the query are updated each day?

A. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL. 
B. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
 C. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL. 
D. They can schedule the query to run every 1 day from the Jobs UI. E. They can schedule the query to run every 12 hours from the Jobs UI. 

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Question # 9

A dataset has been defined using Delta Live Tables and includes an expectations clause: CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log. 
B. Records that violate the expectation cause the job to fail. 
C. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table. 
D. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log. 
E. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset. 

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Question # 10

A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE. The table is configured to run in Development mode using the Continuous Pipeline Mode. Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated. 
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down. 
C. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused. 
D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing. 
E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing. 

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Question # 11

Which of the following data workloads will utilize a Gold table as its source? 

A. A job that enriches data by parsing its timestamps into a human-readable format 
B. A job that aggregates uncleaned data to create standard summary statistics 
C. A job that cleans data by removing malformatted records 
D. A job that queries aggregated data designed to feed into a dashboard 
E. A job that ingests raw data from a streaming source into the Lakehouse 

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Question # 12

A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables. Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

A. None of these changes will need to be made 
B. The pipeline will need to stop using the medallion-based multi-hop architecture 
C. The pipeline will need to be written entirely in SQL 
D. The pipeline will need to use a batch source in place of a streaming source 
E. The pipeline will need to be written entirely in Python 

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Question # 13

A data engineer has left the organization. The data team needs to transfer ownership of the data engineer’s Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team. Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?

A. Databricks account representative 
B. This transfer is not possible 
C. Workspace administrator 
D. New lead data engineer 
E. Original data engineer

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Question # 14

A data engineer has been given a new record of data: id STRING = 'a1' rank INTEGER = 6 rating FLOAT = 9.4 Which of the following SQL commands can be used to append the new record to an existing Delta table my_table? 

A. INSERT INTO my_table VALUES ('a1', 6, 9.4) 
B. my_table UNION VALUES ('a1', 6, 9.4) 
C. INSERT VALUES ( 'a1' , 6, 9.4) INTO my_table 
D. UPDATE my_table VALUES ('a1', 6, 9.4) 
E. UPDATE VALUES ('a1', 6, 9.4) my_table 

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Question # 15

A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job’s current run. The data engineer asks a tech lead for help in identifying why this might be the case. Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?

A. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook. 
B. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.  immediately review the processing notebook. 
C. They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook. 
D. There is no way to determine why a Job task is running slowly.
 E. They can navigate to the Tasks tab in the Jobs UI to

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Question # 16

A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE. The table is configured to run in Production mode using the Continuous Pipeline Mode. Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing. 
B. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused. 
C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped. 
D. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated. 
E. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing. 

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Question # 17

A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs. Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?

A. pyspark.sql.types.DateType 
B. datetime 
C. pyspark.sql.types.TimestampType 
D. Cron syntax 
E. There is no way to represent and submit this information programmatically

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Question # 18

An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project’s release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project’s release. Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project’s release?

A. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint. efresh schedule to end on a certain date in the query scheduler. 
B. They can set the query’s refresh schedule to end after a certain number of refreshes. 
C. They cannot ensure the query does not cost the organization money beyond the first week of the project’s release. 
D. They can set a limit to the number of individuals that are able to manage the query’s refresh schedule. 
E. They can set the query’s refresh schedule to end on a certain date in the query scheduler.

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Question # 19

A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name. They have the following incomplete code block: ____(f"SELECT customer_id, spend FROM {table_name}") Which of the following can be used to fill in the blank to successfully complete the task?

A. spark.delta.sql 
B. spark.delta.table 
C. spark.table 
D. dbutils.sql 
E. spark.sql 

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Question # 20

Which of the following describes a scenario in which a data team will want to utilize cluster pools?

A. An automated report needs to be refreshed as quickly as possible. 
B. An automated report needs to be made reproducible. 
C. An automated report needs to be tested to identify errors. 
D. An automated report needs to be version-controlled across multiple collaborators. 
E. An automated report needs to be runnable by all stakeholders. 

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Question # 21

A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted. Which of the following explains why the data files are no longer present?

A. The VACUUM command was run on the table 
B. The TIME TRAVEL command was run on the table 
C. The DELETE HISTORY command was run on the table 
D. The OPTIMIZE command was nun on the table E. The HISTORY command was run on the table 

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Question # 22

A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos. Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

A. Databricks Repos automatically saves development progress  
B. Databricks Repos supports the use of multiple branches  
C. Databricks Repos allows users to revert to previous versions of a notebook  
D. Databricks Repos provides the ability to comment on specific changes  
E. Databricks Repos is wholly housed within the Databricks Lakehouse Platform  

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Question # 23

Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?

A. When they are working interactively with a small amount of data  
B. When they are running automated reports to be refreshed as quickly as possible  
C. When they are working with SQL within Databricks SQL  
D. When they are concerned about the ability to automatically scale with larger data  
E. When they are manually running reports with a large amount of data  

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Question # 24

A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?  

A. They can set up an Alert with a custom template.  
B. They can set up an Alert with a new email alert destination.  
C. They can set up an Alert with a new webhook alert destination.  
D. They can set up an Alert with one-time notifications.  
E. They can set up an Alert without notifications.  

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Question # 25

A data engineer has a Python notebook in Databricks, but they need to use SQL to accomplish a specific task within a cell. They still want all of the other cells to use Python without making any changes to those cells.Which of the following describes how the data engineer can use SQL within a cell of their Python notebook?  

A. It is not possible to use SQL in a Python notebook  
B. They can attach the cell to a SQL endpoint rather than a Databricks cluster  
C. They can simply write SQL syntax in the cell  
D. They can add %sql to the first line of the cell  
E. They can change the default language of the notebook to SQL  

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