Latest Apr 08, 2026 Databricks-Certified-Data-Engineer-Associate Brain Dump: A Study Guide with Tips & Tricks for passing Exam
Databricks-Certified-Data-Engineer-Associate Question Bank: Free PDF Download Recently Updated Questions
The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Exam is a certification program designed to recognize the skills and expertise of data engineering professionals. Databricks-Certified-Data-Engineer-Associate exam is intended for individuals who work with big data, data engineering, and distributed systems. It is a challenging exam that tests the candidate’s knowledge of data engineering concepts and practices.
NEW QUESTION # 47
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 and click on the active run to review the processing notebook.
- B. There is no way to determine why a Job task is running slowly.
- C. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.
- D. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.
- E. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
Answer: E
Explanation:
The Tasks tab in the Jobs UI shows the list of tasks that are part of a job, and allows the user to view the details of each task, such as the notebook path, the cluster configuration, the run status, and the duration. By clicking on the active run of a task, the user can access the Spark UI, the notebook output, and the logs of the task. These can help the user to identify the performance bottlenecks and errors in the task. The Runs tab in the Jobs UI only shows the summary of the job runs, such as the start time, the end time, the trigger, and the status. It does not provide the details of the individual tasks within a job run. References: Jobs UI, Monitor running jobs with a Job Run dashboard, How to optimize jobs performance
NEW QUESTION # 48
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?
- A.
- B.
- C.
- D.
- E.
Answer: D
Explanation:
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/
NEW QUESTION # 49
Which of the following Git operations must be performed outside of Databricks Repos?
- A. Pull
- B. Push
- C. Merge
- D. Commit
- E. Clone
Answer: C
Explanation:
Explanation
For following tasks, work in your Git provider:
Create a pull request.
Resolve merge conflicts.
Merge or delete branches.
Rebase a branch.
https://docs.databricks.com/repos/index.html
NEW QUESTION # 50
A Python file is ready to go into production and the client wants to use the cheapest but most efficient type of cluster possible. The workload is quite small, only processing 10GBs of data with only simple joins and no complex aggregations or wide transformations.
Which cluster meets the requirement?
- A. Interactive cluster
- B. Job cluster with spot instances disabled
- C. Job cluster with spot instances enabled
- D. Job cluster with Photon enabled
Answer: C
NEW QUESTION # 51
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?
- A. trigger(once="5 seconds")
- B. trigger(continuous="5 seconds")
- C. trigger("5 seconds")
- D. trigger(processingTime="5 seconds")
Answer: D
NEW QUESTION # 52
A data engineer is developing an ETL process based on Spark SQL. The execution fails. The data engineer checks the Spark Ul and can see the ERRORS as follows:
Which two corrective actions should the data engineer perform to resolve this issue?
Choose 2 answers - (Q) Narrow the filters in order to collect less data in the query
- A. Upsize the driver node and deactivate autoshuffle partitions
- B. Fix the shuffle partitions to 50 to ensure the allocation
- C. Upsize the worker nodes and activate autoshuffle partitions
- D. Cache the dataset in order to boost the query performance
Answer: A,C
NEW QUESTION # 53
A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?
- A.
- B.
- C.
- D.
- E.
Answer: B
Explanation:
https://www.databricks.com/blog/2021/10/20/introducing-sql-user-defined-functions.html
NEW QUESTION # 54
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?
- A. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
- B. They can set up the dashboard's SQL endpoint to be serverless.
- C. They can reduce the cluster size of the SQL endpoint.
- D. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
- E. They can turn on the Auto Stop feature for the SQL endpoint.
Answer: E
Explanation:
The Auto Stop feature allows the SQL endpoint to automatically stop after a specified period of inactivity.
This can help reduce the cost and resource consumption of the SQL endpoint, as it will only run when it is needed to refresh the dashboard or execute queries. The data engineer can configure the Auto Stop setting for the SQL endpoint from the SQL Endpoints UI, by selecting the desired idle time from the Auto Stop dropdown menu. The default idle time is 120 minutes, but it can be set to as low as 15 minutes or as high as
240 minutes. Alternatively, the data engineer can also use the SQL Endpoints REST API to set the Auto Stop setting programmatically. References: SQL Endpoints UI, SQL Endpoints REST API, Refreshing SQL Dashboard
NEW QUESTION # 55
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. dbutils.sql
- B. spark.delta.sql
- C. spark.delta.table
- D. spark.sql
- E. spark.table
Answer: D
NEW QUESTION # 56
Which of the following 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?
- A. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.
- B. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.
- C. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.
- D. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.
- E. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.
Answer: D
Explanation:
Explanation
The CREATE STREAMING LIVE TABLE syntax is used when you want to create Delta Live Tables (DLT) tables that are designed for processing data incrementally. This is typically used when your data pipeline involves streaming or incremental data updates, and you want the table to stay up to date as new data arrives.
It allows you to define tables that can handle data changes incrementally without the need for full table refreshes.
NEW QUESTION # 57
A data engineering project involves processing large batches of data on a daily schedule using ETL. The jobs are resource-intensive and vary in size, requiring a scalable, cost-efficient compute solution that can automatically scale based on the workload.
Which compute approach will satisfy the needs described?
- A. Job Cluster
- B. Databricks SQL Serverless
- C. All-Purpose Cluster
- D. Dedicated Cluster
Answer: A
NEW QUESTION # 58
A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?
- A. Option B
- B. Option D
- C. Option C
- D. Option E
- E. Option A
Answer: D
Explanation:
Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks
NEW QUESTION # 59
A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when It is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
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?
- A. O They can set up the dashboard's SQL endpoint to be serverless.
- B. Q They can turn on the Auto Stop feature for the SQL endpoint.
- C. O They can reduce the cluster size of the SQL endpoint.
- D. 0 They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
Answer: B
Explanation:
To minimize the total running time of the SQL endpoint used in the refresh schedule of a dashboard in Databricks, the most effective approach is to utilize the Auto Stop feature. This feature allows the SQL endpoint to automatically stop after a period of inactivity, ensuring that it only runs when necessary, such as during the dashboard refresh or when actively queried. This minimizes resource usage and associated costs by ensuring the SQL endpoint is not running idle outside of these operations.
Reference:
Databricks documentation on SQL endpoints: SQL Endpoints in Databricks
NEW QUESTION # 60
Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?
- A. DROP
- B. INSERT
- C. MERGE
- D. IGNORE
- E. APPEND
Answer: C
Explanation:
Explanation
To write data into a Delta table while avoiding the writing of duplicate records, you can use the MERGE command. The MERGE command in Delta Lake allows you to combine the ability to insert new records and update existing records in a single atomic operation. The MERGE command compares the data being written with the existing data in the Delta table based on specified matching criteria, typically using a primary key or unique identifier. It then performs conditional actions, such as inserting new records or updating existing records, depending on the comparison results. By using the MERGE command, you can handle the prevention of duplicate records in a more controlled and efficient manner. It allows you to synchronize and reconcile data from different sources while avoiding duplication and ensuring data integrity.
NEW QUESTION # 61
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?
- A. Delta Table
- B. Database
- C. View
- D. Temporary view
- E. Spark SQL Table
Answer: D
Explanation:
A temporary view is a relational object that is defined in the metastore and points to an existing DataFrame. It does not copy or store any physical data, but only saves the query that defines the view. The lifetime of a temporary view is tied to the SparkSession that was used to create it, so it does not persist across different sessions or applications. A temporary view is useful for accessing the same data multiple times within the same notebook or session, without incurring additional storage costs. The other options are either materialized (A, E), persistent (B, C), or not relational objects. References: Databricks Documentation - Temporary View, Databricks Community - How do temp views actually work?, Databricks Community - What's the difference between a Global view and a Temp view?, Big Data Programmers - Temporary View in Databricks.
NEW QUESTION # 62
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?
- A. spark.table("sales")
- B. spark.delta.table("sales")
- C. There is no way to share data between PySpark and SQL.
- D. SELECT * FROM sales
- E. spark.sql("sales")
Answer: A
Explanation:
The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. References: 1: SparkSession | PySpark
3.2.0 documentation 2: Welcome to Delta Lake's Python documentation page - delta-spark 2.4.0 documentation
NEW QUESTION # 63
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?
- A. trigger(once="5 seconds")
- B. trigger(continuous="5 seconds")
- C. trigger("5 seconds")
- D. trigger(processingTime="5 seconds")
Answer: D
NEW QUESTION # 64
A Databricks workflow fails at the last stage due to an error in a notebook. This workflow runs daily. The data engineer fixes the mistake and wants to rerun the pipeline. This workflow is very costly and time- intensive to run.
Which action should the data engineer do in order to minimise downtime and cost?
- A. Repair run
- B. Switch to another cluster
- C. Re-run the entire workflow
- D. Restart the cluster
Answer: A
NEW QUESTION # 65
A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?
- A. Option E
- B. Option B
- C. Option D
- D. Option C
- E. Option A
Answer: E
Explanation:
Option A is the correct answer because it uses the FILTER higher-order function correctly to filter out employees with more than 5 years of experience from the array column "employees". It applies a lambda function i -> i.years_exp > 5 that checks if the years of experience of each employee in the array is greater than 5. If this condition is met, the employee is included in the new array column "exp_employees".
The use of higher-order functions like FILTER can be referenced from Databricks documentation on Higher- Order Functions.
NEW QUESTION # 66
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?
- A. dbfs:/user/hive/database/customer360
- B. dbfs:/user/hive/warehouse
- C. dbfs:/user/hive/customer360
- D. More information is needed to determine the correct response
Answer: B
Explanation:
Explanation
dbfs:/user/hive/warehouse - which is the default location
NEW QUESTION # 67
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
- A. The ability to support batch and streaming workloads
- B. The ability to manipulate the same data using a variety of languages
- C. The ability to collaborate in real time on a single notebook
- D. The ability to set up alerts for query failures
- E. The ability to distribute complex data operations
Answer: A
NEW QUESTION # 68
Which tool is used by Auto Loader to process data incrementally?
- A. Databricks SQL
- B. Spark Structured Streaming
- C. Checkpointing
- D. Unity Catalog
Answer: B
Explanation:
Auto Loader in Databricks utilizes Spark Structured Streaming for processing data incrementally. This allows Auto Loader to efficiently ingest streaming or batch data at scale and to recognize new data as it arrives in cloud storage. Spark Structured Streaming provides the underlying engine that supports various incremental data loading capabilities like schema inference and file notification mode, which are crucial for the dynamic nature of data lakes.
References:Databricks documentation on Auto Loader: Auto Loader Overview
NEW QUESTION # 69
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:
Which of the following lines of code fills in the above blank to successfully complete the task?
- A. sqlite
- B. org.apache.spark.sql.jdbc
- C. DELTA
- D. org.apache.spark.sql.sqlite
- E. autoloader
Answer: A
Explanation:
In the given command, a data engineer is trying to create a table in Databricks using data from an SQLite database. The correct option to fill in the blank is "sqlite" because it specifies the type of database being connected to in a JDBC connection string. The USING clause should be followed by the format of the data, and since we are connecting to an SQLite database, "sqlite" would be appropriate here. References:
* Create a table using JDBC
* JDBC connection string
* SQLite JDBC driver
NEW QUESTION # 70
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Databricks Certified Data Engineer Associate certification exam covers topics such as data engineering concepts, data ingestion, data processing, data storage, and data transformation using Apache Spark and Delta Lake. Candidates who pass Databricks-Certified-Data-Engineer-Associate exam will have a deep understanding of the Databricks platform and will be able to design, build, and maintain data pipelines that are scalable, reliable, and efficient. Databricks Certified Data Engineer Associate Exam certification is ideal for data engineers, data analysts, and data scientists who work with big data and want to enhance their skills and advance their careers.
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