Nov 04, 2026 Updated Databricks-Certified-Data-Engineer-Associate Dumps Questions For Databricks Exam [Q96-Q120]

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Nov 04, 2026 Updated Databricks-Certified-Data-Engineer-Associate Dumps Questions For Databricks Exam

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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Objectives
Topic 1: Data Ingestion and ELT Development	– ETL patterns and transformations – Handling structured and semi-structured data – Data ingestion using Spark SQL and PySpark
Topic 2: Databricks Lakehouse Platform Fundamentals	– Clusters, notebooks, and basic Databricks environment usage – Workspace, architecture, and core platform concepts
Topic 3: Data Governance and Quality	– Data quality concepts and management – Unity Catalog basics – Data access control and governance
Topic 4: Data Processing and Transformations	– Apache Spark SQL operations (joins, aggregations, filtering) – User-defined functions (UDFs) – PySpark DataFrame transformations – Delta Lake fundamentals (tables, transactions, optimization)
Topic 5: Productionizing Data Pipelines	– Scheduling and monitoring jobs – Pipeline deployment and operationalization – Databricks Workflows / Jobs orchestration

QUESTION 96
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

The table was managed

The table’s data was smaller than 10 GB

The table’s data was larger than 10 GB

The table was external

The table did not have a location

The reason why all of the data files and metadata files were deleted from the file system after dropping the table is that the table was managed. A managed table is a table that is created and managed by Spark SQL. It stores both the data and the metadata in the default location specified by the spark.sql.warehouse.
dir configuration property. When a managed table is dropped, both the data and the metadata are deleted from the file system.
Option B is not correct, as the size of the table’s data does not affect the behavior of dropping the table.
Whether the table’s data is smaller or larger than 10 GB, the data files and metadata files will be deleted if the table is managed, and will be preserved if the table is external.
Option C is not correct, for the same reason as option B.
Option D is not correct, as an external table is a table that is created and managed by the user. It stores the data in a user-specified location, and only stores the metadata in the Spark SQL catalog. When an external table is dropped, only the metadata is deleted from the catalog, but the data files are preserved in the file system.
Option E is not correct, as a table must have a location to store the data. If the location is not specified by the user, it will use the default location for managed tables. Therefore, a table without a location is a managed table, and dropping it will delete both the data and the metadata.
Managing Tables
[Databricks Data Engineer Professional Exam Guide]

QUESTION 97
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?

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

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

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

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.

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.
References:Databricks documentation on Delta Live Tables: Delta Live Tables Guide

QUESTION 98
A data engineer has been provided a PySpark DataFrame named dfwith columns product and revenue. The data engineer needs to compute complex aggregations to determine each product’s total revenue, average revenue, and transaction count. Which code snippet should the data engineer use?

The correct code uses PySpark functions (sum, avg, count) with aliases inside .agg() after grouping by product. This produces total revenue, average revenue, and transaction count for each product.

QUESTION 99
Which of the following data workloads will utilize a Gold table as its source?

A job that enriches data by parsing its timestamps into a human-readable format

A job that aggregates uncleaned data to create standard summary statistics

A job that cleans data by removing malformatted records

A job that queries aggregated data designed to feed into a dashboard

A job that ingests raw data from a streaming source into the Lakehouse

A Gold table is a table that contains highly refined and aggregated data that powers analytics, machine learning, and production applications. It represents data that has been transformed into knowledge, rather than just information. A Gold table is typically the final output of a medallion lakehouse architecture, where data flows from Bronze to Silver to Gold tables, with each layer improving the structure and quality of data. A job that queries aggregated data designed to feed into a dashboard is an example of a data workload that will utilize a Gold table as its source, as it requires data that is ready for consumption and analysis. The other options are either data workloads that will use a Bronze or Silver table as their source, or data workloads that will produce a Gold table as their output. Reference: Databricks Documentation – What is the medallion lakehouse architecture?, Databricks Documentation – What is a Medallion Architecture?, K21Academy – Delta Lake Architecture & Azure Databricks Workspace.

QUESTION 100
A company is collaborating with a partner that does not use Databricks but needs access to a large historical dataset stored in Delta format. The data engineer needs to ensure that the partner can access the data securely, without the need for them to set up an account, and with read-only access.
How should the data be shared?

Share the dataset using Delta Sharing, which allows your partner to access the data using a secure, read- only URL without requiring a Databricks account, ensuring that they cannot modify the data.

Share the dataset using Unity Catalog, ensuring that both teams have full write access to the data within the same organization.

Share the dataset by exporting it to a CSV file and manually transferring the file to the partner ‘ s system.

Grant your partner access to your Databricks workspace and assign them full write permissions to the Delta table, enabling them to modify the dataset.

Delta Sharing is an open protocol developed by Databricks that enables secure, real-time sharing of data across organizations without requiring recipients to have a Databricks account. It is specifically designed for scenarios where external partners need read-only access to data stored in Delta format. With Delta Sharing, data providers create a share and grant access to recipients via a secure mechanism (such as a profile file or URL-based access), ensuring that the data cannot be modified by the consumer. This approach eliminates the need for data duplication or manual file transfers, maintaining data freshness and governance. Option B is incorrect because Unity Catalog governs internal access within an organization, not external sharing without accounts. Option C is inefficient and breaks data lineage and freshness. Option D violates security best practices by granting excessive permissions. Delta Sharing ensures fine- grained access control, auditability, and secure external collaboration , making it the correct solution for this requirement.
=========

QUESTION 101
In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task?

When another task needs to be replaced by the new task

When another task needs to fail before the new task begins

When another task has the same dependency libraries as the new task

When another task needs to use as little compute resources as possible

When another task needs to successfully complete before the new task begins

A data engineer can create a multi-task job in Databricks that consists of multiple tasks that run in a specific order. Each task can have one or more dependencies, which are other tasks that must run before the current task. The Depends On field of a new Databricks Job Task allows the data engineer to specify the dependencies of the task. The data engineer should select a task in the Depends On field when they want the new task to run only after the selected task has successfully completed. This can help the data engineer to create a logical sequence of tasks that depend on each other’s outputs or results. For example, a data engineer can create a multi-task job that consists of the following tasks:
Task A: Ingest data from a source using Auto Loader
Task B: Transform the data using Spark SQL
Task C: Write the data to a Delta Lake table
Task D: Analyze the data using Spark ML
Task E: Visualize the data using Databricks SQL
In this case, the data engineer can set the dependencies of each task as follows:
Task A: No dependencies
Task B: Depends on Task A
Task C: Depends on Task B
Task D: Depends on Task C
Task E: Depends on Task D
This way, the data engineer can ensure that each task runs only after the previous task has successfully completed, and the data flows smoothly from ingestion to visualization.
The other options are incorrect because they do not describe valid scenarios for selecting a task in the Depends On field. The Depends On field does not affect the following aspects of a task:
Whether the task needs to be replaced by another task
Whether the task needs to fail before another task begins
Whether the task has the same dependency libraries as another task
Whether the task needs to use as little compute resources as possible Reference: Create a multi-task job, Run tasks conditionally in a Databricks job, Databricks Jobs.

QUESTION 102
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 of the following describes why the statement might not have copied any new records into the table?

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

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

The previous day’s file has already been copied into the table.

The PARQUET file format does not support COPY INTO.

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

QUESTION 103
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?

COMMENT “Contains PIT

511

“COMMENT PII”

TBLPROPERTIES PII

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.
References:Databricks documentation on Delta Lake: Delta Lake on Databricks

QUESTION 104
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

Databricks Repos automatically saves development progress

Databricks Repos supports the use of multiple branches

Databricks Repos allows users to revert to previous versions of a notebook

Databricks Repos provides the ability to comment on specific changes

Databricks Repos is wholly housed within the Databricks Lakehouse Platform

Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. Reference: Git integration with Databricks Repos – Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available – New ‘Files’ Feature in …, Databricks Repos – What it is and how we can use it | Adatis.

QUESTION 105
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?

Option A

Option B

Option C

Option D

Option E

Create a table using SQL | Databricks on AWS, Create a table using SQL – Azure Databricks, Delta Lake Quickstart – Azure Databricks

QUESTION 106
In which of the following file formats is data from Delta Lake tables primarily stored?

Delta

CSV

Parquet

JSON

A proprietary, optimized format specific to Databricks

Explanation
https://docs.delta.io/latest/delta-faq.html

QUESTION 107
What Databricks feature can be used to check the data sources and tables used in a workspace?

Do not use the lineage feature as it only tracks activity from the last 3 months and will not provide full details on dependencies.

Use the lineage feature to visualize a graph that highlights where the table is used only in notebooks,

Use the lineage feature to visualize a graph that highlights where the table is used only in reports.

Use the lineage feature to visualize a graph that shows all dependencies, including where the table is used in notebooks, other tables, and reports.

QUESTION 108
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?

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

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

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

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.

Unity Catalog lineage provides an interactive graph that shows how data flows across Delta tables, notebooks, jobs, and dashboards, with column-level lineage to trace transformations precisely for governance and auditing.

QUESTION 109
Which type of workloads are compatible with Auto Loader?

Streaming workloads

Machine learning workloads

Serverless workloads

Batch workloads

QUESTION 110
A data engineer is creating a Spark DataFrame transformation pipeline. The pipeline consists of multiple transformations such as filter, select, and join operations before writing results to a Delta table. Which Spark characteristic allows these transformations to be optimized before execution?

Lazy evaluation

Parallel processing

Checkpointing

Broadcasting

QUESTION 111
Which of the following describes the relationship between Bronze tables and raw data?

Bronze tables contain less data than raw data files.

Bronze tables contain more truthful data than raw data.

Bronze tables contain aggregates while raw data is unaggregated.

Bronze tables contain a less refined view of data than raw data.

Bronze tables contain raw data with a schema applied.

Bronze tables are the first layer of a medallion architecture, which is a data design pattern used to organize data in a lakehouse. Bronze tables contain raw data ingested from various sources, such as RDBMS data, JSON files, IoT data, etc. The table structures in this layer correspond to the source system table structures
“as-is”, along with any additional metadata columns that capture the load date/time, process ID, etc. The only transformation applied to the raw data in this layer is to apply a schema, which defines the column names and data types of the table. The schema can be inferred from the data source or specified explicitly. Applying a schema to the raw data enables the use of SQL and other structured query languages to access and analyze the data. Therefore, option E is the correct answer. References: What is a Medallion Architecture?, Raw Data Ingestion into Delta Lake Bronze tables using Azure Synapse Mapping Data Flow, Apache Spark + Delta Lake concepts, Delta Lake Architecture & Azure Databricks Workspace.

QUESTION 112
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?

trigger(“5 seconds”)

trigger(continuous=”5 seconds”)

trigger(once=”5 seconds”)

trigger(processingTime=”5 seconds”)

QUESTION 113
A data engineer is developing an ETL process based on Spark SQL. The execution fails. The data engineer checks the Spark UI and can see the ERRORS as follows:
“java.lang.OutofMemoryError: Java heap space”
Which two corrective actions should the data engineer perform to resolve this issue? (Choose two.)

Narrow the filters in order to collect less data in the query

Upsize the worker nodes and activate autoshuffle partitions

Upsize the driver node and deactivate autoshuffle partitions

Cache the dataset in order to boost the query performance

Fix the shuffle partitions to 50 to ensure the allocation

Reducing input with narrower filters lowers memory pressure, and increasing executor resources (upsize worker nodes) plus enabling adaptive/auto shuffle partitioning provides more memory and better partitioning to avoid Java heap OOM during Spark SQL execution.

QUESTION 114
A data engineer is writing Spark code to group sales data by region and calculate total revenue for each region. Which Spark DataFrame transformation performs grouping operations?

groupBy

filter

select

orderBy

QUESTION 115
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?

They can use endpoints available in Databricks SQL

They can use jobs clusters instead of all-purpose clusters

They can configure the clusters to be single-node

They can use clusters that are from a cluster pool

They can configure the clusters to autoscale for larger data sizes

The best action that the data engineer can perform to improve the start up time for the clusters used for the Job is to use clusters that are from a cluster pool. A cluster pool is a set of idle clusters that can be used by jobs or interactive sessions. By using a cluster pool, the data engineer can avoid the cluster creation time and reduce the latency of the tasks. Cluster pools also offer cost savings and resource efficiency, as they can be shared by multiple users and jobs.
Option A is not relevant, as endpoints available in Databricks SQL are used for creating and managing SQL analytics workloads, not for improving cluster start up time.
Option B is not correct, as jobs clusters and all-purpose clusters have similar start up times. Jobs clusters are clusters that are dedicated to run a single job and are terminated when the job is completed. All-purpose clusters are clusters that can be used for multiple purposes, such as interactive sessions, notebooks, or multiple jobs. Both types of clusters can benefit from using a cluster pool.
Option C is not advisable, as configuring the clusters to be single-node will reduce the parallelism and performance of the tasks. Single-node clusters are clusters that have only one worker node and are typically used for testing or development purposes. They are not suitable for running production jobs that require high scalability and fault tolerance.
Option E is not helpful, as configuring the clusters to autoscale for larger data sizes will not affect the start up time of the clusters. Autoscaling is a feature that allows clusters to dynamically adjust the number of worker nodes based on the workload. It can help optimize the resource utilization and cost efficiency of the clusters, but it does not speed up the cluster creation process.
Reference:
Cluster Pools
Jobs
Clusters
[Databricks Data Engineer Professional Exam Guide]

QUESTION 116
Which of the following describes the relationship between Bronze tables and raw data?

Bronze tables contain less data than raw data files.

Bronze tables contain more truthful data than raw data.

Bronze tables contain aggregates while raw data is unaggregated.

Bronze tables contain a less refined view of data than raw data.

Bronze tables contain raw data with a schema applied.

QUESTION 117
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?

trigger(“5 seconds”)

trigger(continuous=”5 seconds”)

trigger(once=”5 seconds”)

trigger(processingTime=”5 seconds”)

QUESTION 118
A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

The table was managed

The table’s data was smaller than 10 GB

The table’s data was larger than 10 GB

The table was external

The table did not have a location

QUESTION 119
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?

When they are working interactively with a small amount of data

When they are running automated reports to be refreshed as quickly as possible

When they are working with SQL within Databricks SQL

When they are concerned about the ability to automatically scale with larger data

When they are manually running reports with a large amount of data

The scenario in which a data engineer will want to use a single-node cluster is when they are working interactively with a small amount of data. A single-node cluster is a cluster consisting of an Apache Spark driver and no Spark workers1. A single-node cluster supports Spark jobs and all Spark data sources, including Delta Lake1. A single-node cluster is helpful for single-node machine learning workloads that use Spark to load and save data, and for lightweight exploratory data analysis1. A single-node cluster can run Spark locally, spawn one executor thread per logical core in the cluster, and save all log output in the driver log1. A single-node cluster can be created by selecting the Single Node button when configuring a cluster1.
The other options are not suitable for using a single-node cluster. When running automated reports to be refreshed as quickly as possible, a data engineer will want to use a multi-node cluster that can scale up and down automatically based on the workload demand2. When working with SQL within Databricks SQL, a data engineer will want to use a SQL Endpoint that can execute SQL queries on a serverless pool or an existing cluster3. When concerned about the ability to automatically scale with larger data, a data engineer will want to use a multi-node cluster that can leverage the Databricks Lakehouse Platform and the Delta Engine to handle large-scale data processing efficiently and reliably4. When manually running reports with a large amount of data, a data engineer will want to use a multi-node cluster that can distribute the computation across multiple workers and leverage the Spark UI to monitor the performance and troubleshoot the issues.
Reference:
1: Single Node clusters | Databricks on AWS
2: Autoscaling | Databricks on AWS
3: SQL Endpoints | Databricks on AWS
4: Databricks Lakehouse Platform | Databricks on AWS
5: [Spark UI | Databricks on AWS]

QUESTION 120
Which of the following describes the relationship between Gold tables and Silver tables?

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

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

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

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

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

According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer. They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables. Reference:
What is the medallion lakehouse architecture?
What is a Medallion Architecture?

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