Jun-2026 Databricks-Certified-Data-Engineer-Associate Study Material, Preparation Guide and PDF Download [Q14-Q38]

Jun-2026 Databricks-Certified-Data-Engineer-Associate Study Material, Preparation Guide and PDF Download

Free Databricks-Certified-Data-Engineer-Associate Certification Sample Questions with Online Practice Test

Databricks Certified Data Engineer Associate certification is a highly sought-after certification in the data engineering industry. Databricks Certified Data Engineer Associate Exam certification demonstrates that a candidate has the knowledge and skills required to design and build data pipelines using Databricks. Databricks Certified Data Engineer Associate Exam certification is recognized globally and is highly valued by employers in various industries.

 

NEW QUESTION # 14
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

• A. COMMENT "Contains PII"
• B. TBLPROPERTIES PII
• C. PII
• D. There is no way to indicate whether a table contains PII.
• E. "COMMENT PII"

Answer: A

Explanation:
In Databricks, when creating a table, you can add a comment to columns or the entire table to provide more information about the data it contains. In this case, since it's organization policy to indicate that the new table includes personally identifiable information (PII), option D is correct. The line of code would be added after defining the table structure and before closing with a semicolon. References: Data Engineer Associate Exam Guide, CREATE TABLE USING (Databricks SQL)

NEW QUESTION # 15
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 "transactions" with the path to the location of the Delta table
• B. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
• C. Replace spark.read with spark.readStream
• D. Replace format("delta") with format("stream")
• E. Replace predict with a stream-friendly prediction function

Answer: C

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. References: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 16
A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.
Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

• A. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
• B. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
• C. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
• D. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;

Answer: C

Explanation:
To create a table within a schema, a principal must have CREATE TABLE on the schema, USE SCHEMA on that schema, and USE CATALOG on the parent catalog. This combination ensures the group has just enough privileges to create objects in that schema without excessive permissions like CREATE SCHEMA or CREATE CATALOG.
Reference Source: Databricks Unity Catalog Privilege Model - "Privileges Required to Create a Table."

NEW QUESTION # 17
Which of the following benefits is provided by the array functions from Spark SQL?

• A. An ability to work with complex, nested data ingested from JSON files
• B. An ability to work with data in a variety of types at once
• C. An ability to work with an array of tables for procedural automation
• D. An ability to work with time-related data in specified intervals
• E. An ability to work with data within certain partitions and windows

Answer: A

Explanation:
The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. Reference: 1: Spark SQL, Built-in Functions - Apache Spark 2: Spark SQL Array Functions Complete List - Spark By Examples 3: Spark SQL Array Functions - Syntax and Examples - DWgeek.com 4: Spark SQL, Built-in Functions - Apache Spark : Spark SQL, Built-in Functions - Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks - The Databricks Blog]

NEW QUESTION # 18
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 cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

• A. trigger(once="5 seconds")
• B. trigger()
• C. trigger(continuous="5 seconds")
• D. trigger("5 seconds")
• E. trigger(processingTime="5 seconds")

Answer: E

Explanation:
The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). References: Databricks Documentation - Configure Structured Streaming trigger intervals, Databricks Documentation - Trigger.

NEW QUESTION # 19
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 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 reduce the cluster size of the SQL endpoint.
• B. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
• C. They can turn on the Auto Stop feature for the SQL endpoint.
• D. They can set up the dashboard's SQL endpoint to be serverless.
• E. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Answer: C

NEW QUESTION # 20
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?

• A. They can turn on the Auto Stop feature for the SQL endpoint.
• B. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
  "Reliability Optimized."
• C. They can turn on the Serverless feature for the SQL endpoint.
• D. They can increase the maximum bound of the SQL endpoint's scaling range
• E. They can increase the cluster size of the SQL endpoint.

Answer: E

Explanation:
Explanation
https://www.databricks.com/blog/2022/03/10/top-5-databricks-performance-tips.html

NEW QUESTION # 21
A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

• A. TBLPROPERTIES PII
• B. COMMENT "Contains PII"
• C. PII
• D. There is no way to indicate whether a table contains PII.
• E. "COMMENT PII"

Answer: A

NEW QUESTION # 22
A data engineer needs to combine sales data from an on-premises PostgreSQL database with customer data in Azure Synapse for a comprehensive report. The goal is to avoid data duplication and ensure up-to-date information How should the data engineer achieve this using Databricks?

• A. Manually synchronize data from both sources into a single database
• B. Export data from both sources to CSV files and upload them to Databricks
• C. Use Lakehouse Federation to query both data sources directly
• D. Develop custom ETL pipelines to ingest data into Databricks

Answer: C

NEW QUESTION # 23
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 E
• B. Option A
• C. Option D
• D. Option C
• E. Option B

Answer: A

Explanation:
References: Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

NEW QUESTION # 24
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. Delta Live Tables
• B. Data Explorer
• C. Delta Lake
• D. Unity Catalog
• E. Auto Loader

Answer: A

NEW QUESTION # 25
What is the structure of an Asset Bundle?

• A. A single plain text file enumerating the names of assets to be migrated to a new workspace.
• B. A Docker image containing runtime environments and the source code of the assets
• C. A YAML configuration file that specifies the artifacts, resources, and configurations for the project.
• D. A compressed archive (ZIP) that solely contains workspace assets without any accompanying metadata.

Answer: C

NEW QUESTION # 26
Which query is performing a streaming hop from raw data to a Bronze table?

• A.
• B.
• C.
• D.

Answer: C

Explanation:
The query performing a streaming hop from raw data to a Bronze table is identified by using the Spark streaming read capability and then writing to a Bronze table. Let's analyze the options:
* Option A: Utilizes .writeStream but performs a complete aggregation which is more characteristic of a roll-up into a summarized table rather than a hop into a Bronze table.
* Option B: Also uses .writeStream but calculates an average, which again does not typically represent the raw to Bronze transformation, which usually involves minimal transformations.
* Option C: This uses a basic .write with .mode("append") which is not a streaming operation, and hence not suitable for real-time streaming data transformation to a Bronze table.
* Option D: It employs spark.readStream.load() to ingest raw data as a stream and then writes it out with .writeStream, which is a typical pattern for streaming data into a Bronze table where raw data is captured in real-time and minimal transformation is applied. This approach aligns with the concept of a Bronze table in a modern data architecture, where raw data is ingested continuously and stored in a more accessible format.
References:Databricks documentation on Structured Streaming: Structured Streaming in Databricks

NEW QUESTION # 27
A data engineer needs to process SQL queries on a large dataset with fluctuating workloads. The workload requires automatic scaling based on the volume of queries, without the need to manage or provision infrastructure. The solution should be cost-efficient and charge only for the compute resources used during query execution.
Which compute option should the data engineer use?

• A. Databricks Jobs
• B. Databricks Runtime for ML
• C. Serverless SQL Warehouse
• D. Databricks SQL Analytics

Answer: C

NEW QUESTION # 28
Which of the following commands will return the number of null values in the member_id column?

• A. SELECT count(member_id) FROM my_table;
• B. SELECT null(member_id) FROM my_table;
• C. SELECT count_null(member_id) FROM my_table;
• D. SELECT count(member_id) - count_null(member_id) FROM my_table;
• E. SELECT count_if(member_id IS NULL) FROM my_table;

Answer: E

Explanation:
To return the number of null values in the member_id column, the best option is to use the count_if function, which counts the number of rows that satisfy a given condition. In this case, the condition is that the member_id column is null. The other options are either incorrect or not supported by Spark SQL. Option A will return the number of non-null values in the member_id column. Option B will not work because there is no count_null function in Spark SQL. Option D will not work because there is no null function in Spark SQL. Option E will not work because there is no count_null function in Spark SQL. Reference:
Built-in Functions - Spark SQL, Built-in Functions
count_if - Spark SQL, Built-in Functions

NEW QUESTION # 29
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?

• A. Databricks SQL
• B. Data Explorer
• C. Delta Lake
• D. Unity Catalog
• E. Auto Loader

Answer: E

NEW QUESTION # 30
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 reduce the cluster size of the SQL endpoint.
• B. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
• C. They can turn on the Auto Stop feature for the SQL endpoint.
• D. They can set up the dashboard's SQL endpoint to be serverless.
• E. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Answer: C

NEW QUESTION # 31
A data engineer has realized that the data files associated with a Delta table are incredibly small. They want to compact the small files to form larger files to improve performance.
Which of the following keywords can be used to compact the small files?

• A. OPTIMIZE
• B. COMPACTION
• C. REPARTITION
• D. REDUCE
• E. VACUUM

Answer: A

Explanation:
The keyword that can be used to compact the small files associated with a Delta table is OPTIMIZE. The OPTIMIZE command performs file compaction on a Delta table by rewriting a set of small files into a set of larger files1. This can improve the performance of queries that scan the table by reducing the number of files that need to be read and the amount of metadata that needs to be processed1. The OPTIMIZE command can also optionally sort the data within each file by a given set of columns, which can further improve the query performance by enabling data skipping and predicate pushdown1. The OPTIMIZE command can be applied to the whole table or to a specific partition of the table1.
The other keywords are not suitable for compacting the small files associated with a Delta table. REDUCE is a keyword used in the SQL syntax for aggregating data using a user-defined function2. COMPACTION is not a valid keyword in SQL or Python. REPARTITION is a keyword used in the Python syntax for changing the number of partitions of a DataFrame or an RDD3. VACUUM is a keyword used to remove files that are no longer referenced by a Delta table and are older than a retention threshold4.
:
1: OPTIMIZE | Databricks on AWS
2: REDUCE | Databricks on AWS
3: repartition | Databricks on AWS
4: VACUUM | Databricks on AWS

NEW QUESTION # 32
Which tool is used by Auto Loader to process data incrementally?

• A. Spark Structured Streaming
• B. Databricks SQL
• C. Unity Catalog
• D. Checkpointing

Answer: A

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 # 33
Which of the following Git operations must be performed outside of Databricks Repos?

• A. Push
• B. Clone
• C. Commit
• D. Merge
• E. Pull

Answer: B

NEW QUESTION # 34
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: C

Explanation:
Explanation
https://www.w3schools.com/python/python_functions.asp

NEW QUESTION # 35
A global retail company sells products across multiple categories (e.g.. Electronics, Clothing) and regions (e.
g.. North. South, East. West). The sales team has provided the data engineer with a PySpark dataframe named sales_df as below and the team wants the data engineer to analyze the sales data to help them make strategic decisions.

• A. Category_sales = sales_df.sum("3ales_amount"). g-1- upBy("categcryn).alias("toLal_sales_amount))
• B. Category_sales = sales_df.groupBy("reqion"). agq(sum("sales_amountn).alias(ntotal_sales_amount''))
• C. Category_sale: .es df -agg (sum ("sales amount") .-;r*i:rRy ("category") .alias ("total sa.en amount"))
• D. Category_sales = sales df.groupBy("category").agg(sum("sales amount") .alias ("total sales amount"))

Answer: D

NEW QUESTION # 36
A data engineer wants to create a new table containing the names of customers who live in France.
They have written the following command:
CREATE TABLE customersInFrance
_____ AS
SELECT id,
firstName,
lastName
FROM customerLocations
WHERE country = 'FRANCE';
A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (Pll).
Which line of code fills in the above blank to successfully complete the task?

• A. COMMENT "Contains PIT
• B. 0
• C. TBLPROPERTIES PII
• D. "COMMENT PII"

Answer: C

Explanation:
To include a property indicating that a table contains personally identifiable information (PII), the TBLPROPERTIES keyword is used in SQL to add metadata to a table. The correct syntax to define a table property for PII is as follows:
CREATE TABLE customersInFrance
USING DELTA
TBLPROPERTIES ('PII' = 'true')
AS
SELECT id,
firstName,
lastName
FROM customerLocations
WHERE country = 'FRANCE';
The TBLPROPERTIES ('PII' = 'true') line correctly sets a table property that tags the table as containing personally identifiable information. This is in accordance with organizational policies for handling sensitive information.
Reference:
Databricks documentation on Delta Lake: Delta Lake on Databricks

NEW QUESTION # 37
Which file format is used for storing Delta Lake Table?

• A. Delta
• B. JSON
• C. Parquet
• D. SV

Answer: C

NEW QUESTION # 38
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