Databricks Certified Data Engineer Associate Dumps & Practice Exams

Prepare effectively for the Databricks Certified Data Engineer Associate exam with these updated dumps and practice exams. Covering all core concepts like data ingestion, Spark SQL, Delta Lake, ETL pipelines, and optimization techniques, this prep material aligns with the latest Databricks certification syllabus. Whether you're reviewing exam questions or taking a timed practice test, our resources are designed to simulate the real exam environment. These dumps help you identify weak areas, improve speed and accuracy, and gain the confidence needed to pass the certification on your first attempt. Ideal for aspiring data engineers working with Apache Spark and Databricks Lakehouse Platform.

Databricks Certified Data Engineer Associate Dumps

1. Which tool is used by Auto Loader to process data incrementally?

1. Spark Structured Streaming

2. Databricks SQL

3. Checkpointing

4. Unity Catalog

2. Which two components function in the DB platform architecture’s control plane? (Choose two.)

1. Virtual Machines

2. Compute Orchestration

3. Compute

4. Unity Catalog

5. Serverless Compute

3. How can Git operations must be performed outside of Databricks Repos?

1. Merge

2. Pull

3. Commit

4. Clone

4. 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?

1. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log

2. Records that violate the expectation cause the job to fail

3. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table

4. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log

5. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset

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?

1. Delta Lake

2. Delta Live Tables

3. Auto Loader

4. Unity Catalog

6. 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 approach 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?

1. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint

2. They can set the query’s refresh schedule to end after a certain number of refreshes

3. They can set the query’s refresh schedule to end on a certain date in the query scheduler

4. They can set a limit to the number of individuals that are able to manage the query’s refresh schedule

7. 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?

1. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing

2. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing

3. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated

4. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused

5. 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

8. 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 approach can the data engineer use to set up the new task?

1. They can create a new task in the existing Job and then add the original task as a dependency of the new task

2. They can create a new job from scratch and add both tasks to run concurrently

3. They can create a new task in the existing Job and then add it as a dependency of the original task

4. They can clone the existing task in the existing Job and update it to run the new notebook

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

1. UPDATE VALUES ('a1', 6, 9.4) my_table

2. UPDATE my_table VALUES ('a1', 6, 9.4)

3. INSERT INTO my_table VALUES ('a1', 6, 9.4)

4. INSERT VALUES ('a1', 6, 9.4) INTO my_table

10. A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start. Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?

1. They can configure the clusters to autoscale for larger data sizes

2. They can configure the clusters to be single-node

3. They can use jobs clusters instead of all-purpose clusters

4. They can use endpoints available in Databricks SQL

5. They can use clusters that are from a cluster pool

11. Which of the following commands will return the location of database customer360?

1. ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user'};

2. DESCRIBE LOCATION customer360;

3. DESCRIBE DATABASE customer360;

4. USE DATABASE customer360;

5. DROP DATABASE customer360;

12. What describes the relationship between Gold tables and Silver tables?

1. Gold tables are more likely to contain a less refined view of data than Silver tables

2. Gold tables are more likely to contain aggregations than Silver tables

3. Gold tables are more likely to contain truthful data than Silver tables

4. Gold tables are more likely to contain valuable data than Silver tables

13. 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?

1. They can create a new job from scratch and add both tasks to run concurrently

2. They can create a new task in the existing Job and then add the original task as a dependency of the new task

3. They can create a new task in the existing Job and then add it as a dependency of the original task

4. They can clone the existing task to a new Job and then edit it to run the new notebook

5. They can clone the existing task in the existing Job and update it to run the new notebook

14. A data engineer is managing a data pipeline in Databricks, where multiple Delta tables are used for various transformations. The team wants to track how data flows through the pipeline, including identifying dependencies between Delta tables, notebooks, jobs, and dashboards. The data engineer is utilizing the Unity Catalog lineage feature to monitor this process. How does Unity Catalog’s data lineage feature support the visualization of relationships between Delta tables, notebooks, jobs, and dashboards?

1. Unity Catalog lineage provides an interactive graph that tracks dependencies between tables and notebooks but excludes any job-related dependencies or dashboard visualizations

2. Unity Catalog lineage only supports visualizing relationships at the table level and does not extend to notebooks, jobs, or dashboards

3. Unity Catalog provides an interactive graph that visualizes the dependencies between Delta tables, notebooks, jobs, and dashboards, while also supporting column-level tracking of data transformations

4. Unity Catalog lineage visualizes dependencies between Delta tables, notebooks, and jobs, but does not provide column-level tracing or relationships with dashboards

15. Which of the following commands will return the number of null values in the member_id column?

1. SELECT count(member_id) FROM my_table;

2. SELECT null(member_id) FROM my_table;

3. SELECT count_if(member_id IS NULL) FROM my_table;

4. SELECT count(member_id) - count_null(member_id) FROM my_table;

16. A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository. Which Git operation does the data engineer need to run to accomplish this task?

1. Push

2. Merge

3. Pull

4. Clone

17. 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?

1. They can add %sql to the first line of the cell

2. They can change the default language of the notebook to SQL

3. They can simply write SQL syntax in the cell

4. It is not possible to use SQL in a Python notebook

5. They can attach the cell to a SQL endpoint rather than a Databricks cluster

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?

1. They can set a limit to the number of individuals that are able to manage the query’s refresh schedule

2. They can set the query’s refresh schedule to end on a certain date in the query scheduler

3. They can set the query’s refresh schedule to end after a certain number of refreshes

4. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint

5. They cannot ensure the query does not cost the organization money beyond the first week of the project’s release

19. A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data. Which of the following relational objects should the data engineer create?

1. Delta Table

2. View

3. Temporary view

4. Spark SQL Table

20. Identify how the count_if function and the count where x is null can be used Consider a table random_values with below data. What would be the output of below query? select count_if(col > 1) as count_a. count(*) as count_b.count(col1) as count_c from random_values col1 0 1 2 NULL - 2 3

1. 3 6 6

2. 4 6 5

3. 4 6 6

4. 3 6 5

21. What describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?

1. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally

2. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static

3. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static

4. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations

22. A data engineer only wants to execute the final block of a Python program if the Python variable day_of_week is equal to 1 and the Python variable review_period is True. Which of the following control flow statements should the data engineer use to begin this conditionally executed code block?

1. if day_of_week = 1 & review_period: = "True":

2. if day_of_week = 1 and review_period = "True":

3. if day_of_week = 1 and review_period:

4. if day_of_week == 1 and review_period:

23. Which two conditions are applicable for governance in Databricks Unity Catalog? (Choose two.)

1. Both catalog and schema must have a managed location in Unity Catalog provided metastore is not associated with a location

2. You can have more than 1 metastore within a databricks account console but only 1 per region

3. If metastore is not associated with location, it’s mandatory to associate catalog with managed locations

4. You can have multiple catalogs within metastore and 1 catalog can be associated with multiple metastore

5. If catalog is not associated with location, it’s mandatory to associate schema with managed locations

24. 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?

1. Function

2. Table

3. Database

4. View

5. Temporary view

25. A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary. Which approach can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

1. They can ensure the dashboard’s SQL endpoint is not one of the included query’s SQL endpoint

2. They can ensure the dashboard’s SQL endpoint matches each of the queries’ SQL endpoints

3. They can turn on the Auto Stop feature for the SQL endpoint

4. They can set up the dashboard’s SQL endpoint to be serverless

FAQs

1. What is the Databricks Certified Data Engineer Associate exam?

The Databricks Certified Data Engineer Associate exam validates your ability to build and manage data pipelines, use Databricks tools, and optimize data workflows on the Databricks Lakehouse Platform.

2. How do I become a Databricks Certified Data Engineer Associate?

You must pass the Databricks Certified Data Engineer Associate exam, which assesses your understanding of data ingestion, transformation, storage, and governance using Databricks.

3. What are the prerequisites for the Databricks Certified Data Engineer Associate exam?

There are no official prerequisites, but it’s recommended that you have basic knowledge of SQL, Python, and data engineering concepts.

4. How much does the Databricks Certified Data Engineer Associate certification cost?

The exam costs $200 USD, though the price may vary by region.

5. How many questions are in the Databricks Certified Data Engineer Associate exam?

The exam includes 45 multiple-choice and multiple-select questions to be completed within 90 minutes.

6. What topics are covered in the Databricks Certified Data Engineer Associate exam?

It covers Databricks workspace basics, Delta Lake, ETL processes, data transformation, and pipeline management.

7. How difficult is the Databricks Certified Data Engineer Associate exam?

It’s considered moderately challenging, requiring hands-on experience with Databricks and familiarity with data engineering practices.

8. How long does it take to prepare for the Databricks Certified Data Engineer Associate exam?

Most candidates prepare in 6–8 weeks, depending on prior experience with Databricks and data tools.

9. What jobs can I get after earning the Databricks Certified Data Engineer Associate certification?

You can work as a Data Engineer, ETL Developer, Big Data Engineer, or Cloud Data Specialist.

10. How much salary can I earn with a Databricks Certified Data Engineer Associate certification?

Certified professionals typically earn between $95,000–$130,000 per year, depending on their experience and job role.