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Free Databricks Certified Data Engineer Associate Exam Dumps August 2026

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Question No: 1

MultipleChoice

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?

Options

ASELECT * FROM sales

BThere is no way to share data between PySpark and SQL.

Cspark.sql('sales')

Dspark.delta.table('sales')

Espark.table('sales')

Answer
EExplanation

The data engineering team can use thespark.tablemethod to access the Delta tablesalesin PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing.Thespark.tablemethod 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 theDeltaTable.forPathmethod to load the Delta table from its path2.Reference:1:SparkSession | PySpark 3.2.0 documentation2:Welcome to Delta Lake's Python documentation page --- delta-spark 2.4.0 documentation

Question No: 2

MultipleChoice

A data engineer needs access to a table new_uable, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.

Which approach can be used to identify the owner of new_table?

Options

AThere is no way to identify the owner of the table

BReview the Owner field in the table's page in the cloud storage solution

CReview the Permissions tab in the table's page in Data Explorer

DReview the Owner field in the table's page in Data Explorer

Answer
DExplanation

To find the owner of a table in Databricks, one can utilize the Data Explorer feature. The Data Explorer provides detailed information about various data objects, including tables. By navigating to the specific table's page in Data Explorer, a data engineer can review the Owner field, which identifies the individual or role that owns the table. This information is crucial for obtaining the necessary permissions or for any administrative actions related to the table.

Reference: Databricks documentation on Data Explorer: Using Data Explorer in Databricks

Question No: 3

MultipleChoice

Which option best SQL keywords can be used to convert a table from a long format to a wide format?

Options

APIVOT

BCONVERT

CWHERE

DTRANSFORM

ESUM

Answer
AExplanation

The SQL keyword that can be used to convert a table from a long format to a wide format isPIVOT.The PIVOT clause is used to rotate the rows of a table into columns of a new table1.The PIVOT clause can aggregate the values of a column based on the distinct values of another column, and use those values as the column names of the new table1.The PIVOT clause can be useful for transforming data from a long format, where each row represents an observation with multiple attributes, to a wide format, where each row represents an observation with a single attribute and multiple values2.For example, the PIVOT clause can be used to convert a table that contains the sales of different products by different regions into a table that contains the sales of each product by each region as separate columns1.

The other options are not suitable for converting a table from a long format to a wide format.CONVERT is a function that can be used to change the data type of an expression3.WHERE is a clause that can be used to filter the rows of a table based on a condition4.TRANSFORM is a keyword that can be used to apply a user-defined function to a group of rows in a table5. SUM is a function that can be used to calculate the total of a numeric column.

1:PIVOT | Databricks on AWS

2:Reshaping Data - Long vs Wide Format | Databricks on AWS

3:CONVERT | Databricks on AWS

4:WHERE | Databricks on AWS

5:TRANSFORM | Databricks on AWS

: [SUM | Databricks on AWS]

Question No: 4

MultipleChoice

A dataset has been defined using Delta Live Tables and includes an expectations clause:

CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW

What is the expected behavior when a batch of data containing data that violates these constraints is processed?

Options

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

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

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

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

ERecords that violate the expectation cause the job to fail.

Answer
CExplanation

Delta Live Tables expectations are optional clauses that apply data quality checks on each record passing through a query. An expectation consists of a description, a boolean statement, and an action to take when a record fails the expectation. The ON VIOLATION clause specifies the action to take, which can be one of the following: warn, drop, or fail. The drop action means that invalid records are dropped from the target dataset before data is written to the target. The failure is reported as a metric for the dataset, which can be viewed by querying the Delta Live Tables event log. The event log contains information such as the number of records that violate an expectation, the number of records dropped, and the number of records written to the target dataset.Reference:

Manage data quality with Delta Live Tables

Monitor Delta Live Tables pipelines

Delta Live Tables SQL language reference

Question No: 5

MultipleChoice

A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.

Which command can be used to grant full permissions on the database to the new data engineering team?

Options

Agrant all privileges on table sales TO team;

BGRANT SELECT ON TABLE sales TO team;

CGRANT SELECT CREATE MODIFY ON TABLE sales TO team;

DGRANT ALL PRIVILEGES ON TABLE team TO sales;

Answer
AExplanation

To grant full privileges on a table such as 'sales' to a group like 'team', the correct SQL command in Databricks is:

GRANT ALL PRIVILEGES ON TABLE sales TO team;

This command assigns all available privileges, including SELECT, INSERT, UPDATE, DELETE, and any other data manipulation or definition actions, to the specified team. This is typically necessary when a team needs full control over a table to manage and manipulate it as part of a project or ongoing maintenance.

Reference: Databricks documentation on SQL permissions: SQL Permissions in Databricks

Question No: 6

MultipleChoice

A data engineer runs a statement every day to copy the previous day's sales into the table transactions. Each day's sales are in their own file in the location "/transactions/raw".

Today, the data engineer runs the following command to complete this task:

After running the command today, the data engineer notices that the number of records in table transactions has not changed.

Which option best describes why the statement might not have copied any new records into the table?

Options

AThe format of the files to be copied were not included with the FORMAT_OPTIONS keyword.

BThe names of the files to be copied were not included with the FILES keyword.

CThe previous day's file has already been copied into the table.

DThe PARQUET file format does not support COPY INTO.

EThe COPY INTO statement requires the table to be refreshed to view the copied rows.

Answer
CExplanation

The COPY INTO statement is an idempotent operation, which means that it will skip any files that have already been loaded into the target table1. This ensures that the data is not duplicated or corrupted by multiple attempts to load the same file. Therefore, if the data engineer runs the same command every day without specifying the names of the files to be copied with the FILES keyword or a glob pattern with the PATTERN keyword, the statement will only copy the first file that matches the source location and ignore the rest.To avoid this problem, the data engineer should either use the FILES or PATTERN keywords to filter the files to be copied based on the date or some other criteria, or delete the files from the source location after they are copied into the table2.Reference:1:COPY INTO | Databricks on AWS2:Get started using COPY INTO to load data | Databricks on AWS

Question No: 7

MultipleChoice

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?

Options

AO They can reduce the cluster size of the SQL endpoint.

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

CO They can set up the dashboard's SQL endpoint to be serverless.

D0 They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

Answer
BExplanation

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

Question No: 8

MultipleChoice

A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.

Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

Options

ACREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions;

BCREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions;

CCREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions;

DCREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions;

ECREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions;

Answer
BExplanation

The correct command to create a new table that contains all records from two tables without duplicate records is to use theUNIONoperator. The UNION operator combines the results of two queries and removes any duplicate rows. The INNER JOIN, OUTER JOIN, and MERGE operators do not remove duplicate rows, and the INTERSECT operator only returns the rows that are common to both tables. Therefore, option B is the only correct answer.Reference:Databricks SQL Reference - UNION,Databricks SQL Reference - JOIN,Databricks SQL Reference - MERGE, [Databricks SQL Reference - INTERSECT]

Question No: 9

MultipleChoice

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?

Options

ASELECT * FROM sales

Bspark.delta.table

Cspark.sql

DThere is no way to share data between PySpark and SQL.

Espark.table

Answer
CExplanation

The spark.sql operation allows the data engineering team to run a SQL query and return the result as a PySpark DataFrame. This way, the data engineering team can use the same query that the data analyst has developed and operate with the results in PySpark. For example, the data engineering team can use spark.sql(''SELECT * FROM sales'') to get a DataFrame of all the records from the sales Delta table, and then apply various tests or transformations using PySpark APIs. The other options are either not valid operations (A, D), not suitable for running a SQL query (B, E), or not returning a DataFrame (A).Reference:Databricks Documentation - Run SQL queries,Databricks Documentation - Spark SQL and DataFrames.

Question No: 10

MultipleChoice

Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?