2026 Realistic Verified Free Databricks Databricks-Certified-Data-Engineer-Associate Exam Questions [Q64-Q89]

2026 Realistic Verified Free Databricks Databricks-Certified-Data-Engineer-Associate Exam Questions

Databricks-Certified-Data-Engineer-Associate Real Exam Questions and Answers FREE

NEW QUESTION # 64
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. my_table UNION VALUES ('a1', 6, 9.4)
• B. INSERT INTO my_table VALUES ('a1', 6, 9.4)
• C. UPDATE VALUES ('a1', 6, 9.4) my_table
• D. UPDATE my_table VALUES ('a1', 6, 9.4)
• E. INSERT VALUES ( 'a1' , 6, 9.4) INTO my_table

Answer: B

Explanation:
To append a new record to an existing Delta table, you can use the INSERT INTO statement with the VALUES clause. This statement will insert one or more rows into the table with the specified values.
Option A is the only code block that follows this syntax correctly. Option B is incorrect, as it uses the UNION operator, which will return a new table that is the union of two tables, not append to an existing table. Option C is incorrect, as it uses the INSERT VALUES statement, which is not a valid SQL syntax.
Option D is incorrect, as it uses the UPDATE statement, which will modify existing rows in the table, not append new rows. Option E is incorrect, as it uses the UPDATE VALUES statement, which is also not a valid SQL syntax. References: Insert data into a table using SQL | Databricks on AWS, Insert data into a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

NEW QUESTION # 65
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 # 66
A data engineer has written a function in a Databricks Notebook to calculate the population of bacteria in a given medium.

Analysts use this function in the notebook and sometimes provide input arguments of the wrong data type, which can cause errors during execution.
Which Databricks feature will help the data engineer quickly identify if an incorrect data type has been provided as input?

• A. The Data Engineer should add print statements to find out what the variable is.
• B. The Databricks debugger enables the use of a variable explorer to see at a glance the value of the variables.
• C. The Databricks debugger enables breakpoints that will raise an error if the wrong data type is submitted
• D. The Spark User interface has a debug tab that contains the variables that are used in this session.

Answer: B

NEW QUESTION # 67
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.
What is the expected outcome after clicking Start to update the pipeline assuming previously unprocessed data exists and all definitions are valid?

• A. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
• B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
• C. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.
• D. 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.

Answer: D

Explanation:
In Delta Live Tables (DLT), when configured to run in Continuous Pipeline Mode, particularly in a production environment, the system is designed to continuously process and update data as it becomes available. This mode keeps the compute resources active to handle ongoing data processing and automatically updates all datasets defined in the pipeline at predefined intervals. Once the pipeline is manually stopped, the compute resources are terminated to conserve resources and reduce costs. This mode is suitable for production environments where datasets need to be kept up-to-date with the latest data.
Reference:
Databricks documentation on Delta Live Tables: Delta Live Tables Guide

NEW QUESTION # 68
Which of the following commands will return the location of database customer360?

• A. USE DATABASE customer360;
• B. DROP DATABASE customer360;
• C. DESCRIBE DATABASE customer360;
• D. DESCRIBE LOCATION customer360;
• E. ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user'};

Answer: C

NEW QUESTION # 69
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 source is not a Delta table
• C. When the source table can be deleted
• D. When the target table is an external table
• E. When the target table cannot contain duplicate records

Answer: E

Explanation:
The MERGE INTO command is used to perform upserts, which are a combination of insertions and updates, based on a source table into a target Delta table1. The MERGE INTO command can handle scenarios where the target table cannot contain duplicate records, such as when there is a primary key or a unique constraint on the target table. The MERGE INTO command can match the source and target rows based on a merge condition and perform different actions depending on whether the rows are matched or not. For example, the MERGE INTO command can update the existing target rows with the new source values, insert the new source rows that do not exist in the target table, or delete the target rows that do not exist in the source table1.
The INSERT INTO command is used to append new rows to an existing table or create a new table from a query result2. The INSERT INTO command does not perform any updates or deletions on the existing target table rows. The INSERT INTO command can handle scenarios where the location of the data needs to be changed, such as when the data needs to be moved from one table to another, or when the data needs to be partitioned by a certain column2. The INSERT INTO command can also handle scenarios where the target table is an external table, such as when the data is stored in an external storage system like Amazon S3 or Azure Blob Storage3. The INSERT INTO command can also handle scenarios where the source table can be deleted, such as when the source table is a temporary table or a view4. The INSERT INTO command can also handle scenarios where the source is not a Delta table, such as when the source is a Parquet, CSV, JSON, or Avro file5.
References:
* 1: MERGE INTO | Databricks on AWS
* 2: [INSERT INTO | Databricks on AWS]
* 3: [External tables | Databricks on AWS]
* 4: [Temporary views | Databricks on AWS]
* 5: [Data sources | Databricks on AWS]

NEW QUESTION # 70
A data engineer wants to delegate day-to-day permission management for the schema main.marketing to the mkt-admins group, without making them workspace admins. They should be able to grant and revoke privileges for other users on objects within that schema.
Which approach aligns with Unity Catalog's ownership and privilege model?

• A. Grant MANAGE permissions on the metastore to mkt-admins, which allows managing privileges for all schemas and tables globally.
• B. Transfer ownership of the schema main.marketing to mkt-admins; owners can manage privileges on the schema and its contained objects.
• C. Grant USE SCHEMA on main.marketing, and MODIFY on all tables to mkt-admins, which enables the management of grants within the schema.
• D. Make mkt-admins a workspace-level admins group, then assign SELECT on main.marketing to allow privilege delegation.

Answer: B

Explanation:
In Unity Catalog, ownership is the primary mechanism for delegating full administrative control of a securable object. The owner of a schema can grant and revoke privileges on that schema and on all objects contained within it (such as tables and views), without needing to be a workspace admin or metastore admin.
Transferring ownership of main.marketing to the mkt-admins group therefore aligns precisely with the requirement to delegate day-to-day permission management at the schema scope. Granting MANAGE at the metastore level (option B) would be overly permissive, enabling global administration across all schemas and objects, which violates the principle of least privilege. Simply granting USE SCHEMA and MODIFY (option C) does not confer the ability to manage grants for other users. Making the group workspace admins (option D) unnecessarily elevates privileges beyond Unity Catalog's data governance model. Unity Catalog documentation emphasizes using ownership transfer to delegate administrative responsibilities at the appropriate scope while maintaining centralized governance in Databricks.
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NEW QUESTION # 71
A new data engineering team has been assigned to work on a project. The team will need access to database customers in order to see what tables already exist. The team has its own group team.
Which of the following commands can be used to grant the necessary permission on the entire database to the new team?

• A. GRANT VIEW ON CATALOG customers TO team;
• B. GRANT CREATE ON DATABASE team TO customers;
• C. GRANT USAGE ON DATABASE customers TO team;
• D. GRANT USAGE ON CATALOG team TO customers;
• E. GRANT CREATE ON DATABASE customers TO team;

Answer: C

Explanation:
The correct command to grant the necessary permission on the entire database to the new team is to use the GRANT USAGE command. The GRANT USAGE command grants the principal the ability to access the securable object, such as a database, schema, or table. In this case, the securable object is the database customers, and the principal is the group team. By granting usage on the database, the team will be able to see what tables already exist in the database. Option E is the only option that uses the correct syntax and the correct privilege type for this scenario. Option A uses the wrong privilege type (VIEW) and the wrong securable object (CATALOG). Option B uses the wrong privilege type (CREATE), which would allow the team to create new tables in the database, but not necessarily see the existing ones. Option C uses the wrong securable object (CATALOG) and the wrong principal (customers). Option D uses the wrong securable object (team) and the wrong principal (customers). References: GRANT, Privilege types, Securable objects, Principals

NEW QUESTION # 72
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. DELETE FROM my_table WHERE age > 25;
• C. UPDATE my_table WHERE age <= 25;
• D. DELETE FROM my_table WHERE age <= 25;
• E. UPDATE my_table WHERE age > 25;

Answer: B

Explanation:
1: The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table. This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. Reference: Table deletes, updates, and merges - Delta Lake Documentation

NEW QUESTION # 73
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

• A. USING CSV
• B. USING DELTA
• C. FROM CSV
• D. FROM "path/to/csv"
• E. None of these lines of code are needed to successfully complete the task

Answer: D

Explanation:
A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. Reference: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics

NEW QUESTION # 74
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

• A. spark.delta.table
• B. spark.sql
• C. SELECT * FROM sales
• D. There is no way to share data between PySpark and SQL.
• E. spark.table

Answer: B

Explanation:
Explanation
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
df = spark.sql("SELECT * FROM sales")
print(df.count())

NEW QUESTION # 75
A data analyst has a series of queries in a SQL program. The data analyst wants this program to run every day. They only want the final query in the program to run on Sundays. They ask for help from the data engineering team to complete this task.
Which of the following approaches could be used by the data engineering team to complete this task?

• A. They could redesign the data model to separate the data used in the final query into a new table.
• B. They could wrap the queries using PySpark and use Python's control flow system to determine when to run the final query.
• C. They could only run the entire program on Sundays.
• D. They could submit a feature request with Databricks to add this functionality.
• E. They could automatically restrict access to the source table in the final query so that it is only accessible on Sundays.

Answer: B

Explanation:
This approach would allow the data engineering team to use the existing SQL program and add some logic to control the execution of the final query based on the day of the week. They could use the datetime module in Python to get the current date and check if it is a Sunday. If so, they could run the final query, otherwise they could skip it. This way, they could schedule the program to run every day without changing the data model or the source table. References: PySpark SQL Module, Python datetime Module, Databricks Jobs

NEW QUESTION # 76
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

• A. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
• B. Replace spark.read with spark.readStream
• C. Replace predict with a stream-friendly prediction function
• D. Replace format("delta") with format("stream")
• E. Replace "transactions" with the path to the location of the Delta table

Answer: B

Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html

NEW QUESTION # 77
Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

• A. APPEND
• B. MERGE
• C. DROP
• D. INSERT
• E. IGNORE

Answer: B

NEW QUESTION # 78
Which of the following describes the storage organization of a Delta table?

• A. Delta tables are stored in a single file that contains only the data stored within the table.
• B. Delta tables store their data in a single file and all metadata in a collection of files in a separate location.
• C. Delta tables are stored in a collection of files that contain data, history, metadata, and other attributes.
• D. Delta tables are stored in a collection of files that contain only the data stored within the table.
• E. Delta tables are stored in a single file that contains data, history, metadata, and other attributes.

Answer: C

Explanation:
Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling1. Delta Lake stores its data and metadata in a collection of files in a directory on a cloud storage system, such as AWS S3 or Azure Data Lake Storage2. Each Delta table has a transaction log that records the history of operations performed on the table, such as insert, update, delete, merge, etc. The transaction log also stores the schema and partitioning information of the table2. The transaction log enables Delta Lake to provide ACID guarantees, time travel, schema enforcement, and other features1. Reference:
What is Delta Lake? | Databricks on AWS
Quickstart - Delta Lake Documentation

NEW QUESTION # 79
Which of the following commands will return the location of database customer360?

• A. USE DATABASE customer360;
• B. DROP DATABASE customer360;
• C. DESCRIBE DATABASE customer360;
• D. DESCRIBE LOCATION customer360;
• E. ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user'};

Answer: C

Explanation:
Explanation
To retrieve the location of a database named "customer360" in a database management system like Hive or Databricks, you can use the DESCRIBE DATABASE command followed by the database name. This command will provide information about the database, including its location.

NEW QUESTION # 80
A data engineer is working in a Python notebook on Databricks to process data, but notices that the output is not as expected. The data engineer wants to investigate the issue by stepping through the code and checking the values of certain variables during execution.
Which tool should the data engineer use to inspect the code execution and variables in real-time?

• A. Job Execution Dashboard
• B. Python Notebook Interactive Debugger
• C. Cluster Logs
• D. SQL Analytics

Answer: B

NEW QUESTION # 81
A data engineer is maintaining an ETL pipeline code with a GitHub repository linked to their Databricks account. The data engineer wants to deploy the ETL pipeline to production as a databricks workflow.
Which approach should the data engineer use?

• A. Maintain workflow_config.j son and deploy it using Databricks CLI
• B. Manually create and manage the workflow in Ul
• C. Maintain workflow_conf ig. json and deploy it using Terraform
• D. Databricks Asset Bundles (DAB) + GitHub Integration

Answer: D

NEW QUESTION # 82
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 change will need to be made to the pipeline when migrating to Delta Live Tables?

• A. The pipeline will need to be written entirely in Python.
• B. The pipeline will need to be written entirely in SQL.
• C. The pipeline can have different notebook sources in SQL & Python.
• D. The pipeline will need to use a batch source in place of a streaming source.

Answer: C

Explanation:
When migrating to Delta Live Tables (DLT) with a data pipeline that involves different programming languages across various data layers, the migration does not require unifying the pipeline into a single language. Delta Live Tables support multi-language pipelines, allowing data engineers and data analysts to work in their preferred languages, such as Python for data engineering tasks (raw, bronze, and silver layers) and SQL for data analytics tasks (gold layer). This capability is particularly beneficial in collaborative settings and leverages the strengths of each language for different stages of data processing.
References:Databricks documentation on Delta Live Tables: Delta Live Tables Guide

NEW QUESTION # 83
Which of the following commands can be used to write data into a Delta table while avoiding the writing of duplicate records?

• A. APPEND
• B. MERGE
• C. DROP
• D. INSERT
• E. IGNORE

Answer: B

Explanation:
The MERGE command can be used to upsert data from a source table, view, or DataFrame into a target Delta table. It allows you to specify conditions for matching and updating existing records, and inserting new records when no match is found. This way, you can avoid writing duplicate records into a Delta table1. The other commands (DROP, IGNORE, APPEND, INSERT) do not have this functionality and may result in duplicate records or data loss234. Reference: 1: Upsert into a Delta Lake table using merge | Databricks on AWS 2: SQL DELETE | Databricks on AWS 3: SQL INSERT INTO | Databricks on AWS 4: SQL UPDATE | Databricks on AWS

NEW QUESTION # 84
Which of the following is hosted completely in the control plane of the classic Databricks architecture?

• A. Worker node
• B. Databricks web application
• C. Databricks Filesystem
• D. JDBC data source
• E. Driver node

Answer: B

Explanation:
The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network. References: Databricks architecture overview, Security and Trust Center

NEW QUESTION # 85
Which of the following is a benefit of the Databricks Lakehouse Platform embracing open source technologies?

• A. Cloud-specific integrations
• B. Ability to scale storage
• C. Simplified governance
• D. Avoiding vendor lock-in
• E. Ability to scale workloads

Answer: D

Explanation:
One of the benefits of the Databricks Lakehouse Platform embracing open source technologies is that it avoids vendor lock-in. This means that customers can use the same open source tools and frameworks across different cloud providers, and migrate their data and workloads without being tied to a specific vendor. The Databricks Lakehouse Platform is built on open source projects such as Apache Spark™, Delta Lake, MLflow, and Redash, which are widely used and trusted by millions of developers. By supporting these open source technologies, the DatabricksLakehouse Platform enables customers to leverage the innovation and community of the open source ecosystem, and avoid the risk of being locked into proprietary or closed solutions. The other options are either not related to open source technologies (A, B, C, D), or not benefits of the Databricks Lakehouse Platform (A, B). References: Databricks Documentation - Built on open source, Databricks Documentation - What is the Lakehouse Platform?, Databricks Blog - Introducing the Databricks Lakehouse Platform.

NEW QUESTION # 86
A data engineer is attempting to drop a Spark SQL table my_table. The data engineer wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?

• A. The table was managed
• B. The table did not have a location
• C. The table's data was larger than 10 GB
• D. The table's data was smaller than 10 GB
• E. The table was external

Answer: E

Explanation:
An external table is a table that is defined in the metastore and points to an existing location in the storage system. When you drop an external table, only the metadata is deleted from the metastore, but the data files are not deleted from the storage system. This is because external tables are meant to be shared by multiple applications and users, and dropping them should not affect the data availability. On the other hand, a managed table is a table that is defined in the metastore and also managed by the metastore. When you drop a managed table, both the metadata and the data files are deleted from the metastore and the storage system, respectively. This is because managed tables are meant to be exclusive to the application or user that created them, and dropping them should free up the storage space. Therefore, the correct answer is C, because the table was external and only the metadata was deleted when the table was dropped. References: Databricks Documentation - Managed and External Tables, Databricks Documentation - Drop Table

NEW QUESTION # 87
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. Q They can turn on the Auto Stop feature for the SQL endpoint.
• B. O They can reduce the cluster size of the SQL endpoint.
• C. O They can set up the dashboard's SQL endpoint to be serverless.
• D. 0 They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Answer: A

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 # 88
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 without notifications.
• B. They can set up an Alert with one-time notifications.
• C. They can set up an Alert with a new webhook alert destination.
• D. They can set up an Alert with a custom template.
• E. They can set up an Alert with a new email alert destination.

Answer: C

Explanation:
Explanation
To achieve this, the data engineer can set up an Alert in the Databricks workspace that triggers when the query results exceed the threshold of 100 NULL values. They can create a new webhook alert destination in the Alert's configuration settings and provide the necessary messaging webhook URL to receive notifications.
When the Alert is triggered, it will send a message to the configured webhook URL, which will then notify the entire team of the issue.

NEW QUESTION # 89
......

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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