Free PDF Databricks Certified Data Engineer Associate Exam Prep 10 Must-Know Questions

Databricks Certified Data Engineer Associate Questions 2026
Databricks Certified Data Engineer Associate Questions 2026 Contains 690+ exam
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Below are the free 10 sample questions.
Question 1:
A healthcare company stores patient data in a SQL Server database that powers
daily operations. Analysts in Databricks occasionally need to join this operational
data with clinical data already in the Lakehouse. The team wants quick access
without building pipelines or duplicating the SQL Server data into Databricks.
Which approach best meets this requirement?
A.Export SQL Server data as CSV files and upload them to Databricks.
B.Replicate the SQL Server database into cloud storage
C.Build an ETL pipeline to ingest SQL Server data daily.
D.Use Delta Sharing
E.Use Lakehouse Federation to connect to the SQL Server database
Answer: E

Explanation:
E. Use Lakehouse Federation to connect to the SQL Server database
Correct because Lakehouse Federation allows Databricks to query external
databases (like SQL Server) directly without ingesting or duplicating the data.
Analysts can join operational SQL Server data with Lakehouse data in real time.
This meets the requirement: no pipelines, no duplication, and quick access across
notebooks.
Incorrect
Option A. Export SQL Server data as CSV files and upload them to Databricks
Incorrect because this introduces manual effort and duplication.
CSV exports are static snapshots, not real‑time access.
Option B. Replicate the SQL Server database into cloud storage
Incorrect because replication requires pipelines and storage duplication.
This contradicts the requirement of avoiding duplication and pipeline building.
Option C. Build an ETL pipeline to ingest SQL Server data daily
Incorrect because ETL pipelines add operational overhead and delay.
The requirement explicitly states avoiding pipelines.
Option D. Use Delta Sharing
Incorrect because Delta Sharing is for sharing data between organizations or
platforms using Delta tables.
It does not provide direct connectivity to SQL Server.

Question 2:
A company discovers that sensitive customer data may have been accessed by
unauthorized users, but there is no record of who accessed it or when. Which of
the following governance gaps is most likely responsible for these issues?
A.Inadequate access controls and missing audit logs ?
B.Schema evolution during ingestion and lack of version control
C.Inefficient job scheduling and lack of cluster pooling
D.Misconfigured task dependencies and low task retries
E.Lack of query result caching and auto-termination policies
Answer: A
Explanation:
A. Inadequate access controls and missing audit logs
Unauthorized access indicates weak or missing access controls (e.g., improper
role assignments, lack of fine‑grained permissions). The absence of records of
who accessed the data points to missing audit logs. Together, these governance
gaps prevent accountability and traceability, which are critical in regulated
environments. Databricks emphasizes Unity Catalog for centralized access control
and auditing to prevent exactly this type of issue.
Incorrect:
Option B. Schema evolution during ingestion and lack of version control
Schema evolution affects data consistency, not access control or audit logging.
Option C. Inefficient job scheduling and lack of cluster pooling
These are performance/cost issues, not governance or security concerns.

Option D. Misconfigured task dependencies and low task retries
This impacts workflow reliability, not data access governance.
Option E. Lack of query result caching and auto‑termination policies
These affect performance and cost optimization, not security or auditability.
Question 3:
A developer tries to run a PySpark app through Databricks Connect. The cluster is
on Databricks Runtime 15.4 LTS with Python 3.11. The developer sees runtime
errors when starting the session.
What could be the reason?
A.The Databricks Connect library only supports SQL, not PySpark
B.The developer must use notebooks instead of an IDE with Connect
C.The cluster needs to be restarted after every Connect session
D.The developer’ s local Python version is incompatible with the cluster runtime
E.The cluster does not support Databricks Connect
Answer: D
Explanation:
D. The developer’s local Python version is incompatible with the cluster runtime
Databricks Connect requires the local Python version to match the cluster’s
runtime version.
In this case, the cluster is running Python 3.11 (Databricks Runtime 15.4 LTS).
If the developer’s local environment uses a different Python version (e.g., 3.10 or
3.12), runtime errors occur when starting the session.
This mismatch is the most likely cause of the observed errors.

Incorrect
Option A. The Databricks Connect library only supports SQL, not PySpark
Databricks Connect supports PySpark, not just SQL.
Option B. The developer must use notebooks instead of an IDE with Connect
Databricks Connect is specifically designed to allow development from IDEs (e.g.,
VS Code, PyCharm).
Option C. The cluster needs to be restarted after every Connect session
Restarting clusters is not required for Connect sessions. Errors are due to
environment mismatches, not cluster lifecycle.
Option E. The cluster does not support Databricks Connect
Databricks Runtime 15.4 LTS supports Databricks Connect. The issue is not lack of
support but version mismatch.
Question 4:
Scenario: A data engineer is working in a Databricks notebook and wants to load
an existing table named silver.customers into a PySpark DataFrame for further
transformation.
Which of the following code snippets correctly loads the table as a Spark
DataFrame? (Select two Choices)
A.df = spark.table(“silver.customers“)
B.df = spark.read.table(“silver.customers“)
C.df = spark.sql(“SELECT * FROM silver.customers“)
D.df = read.table(“silver.customers“)

E.df = spark.read.load(“silver.customers“)
Answer: A and C
Explanation:
A. df = spark.table("silver.customers")
Correct because spark.table() is the standard PySpark method to directly load a
table into a DataFrame.
It automatically recognizes the table from the metastore and returns a DataFrame
object.
C. df = spark.sql("SELECT * FROM silver.customers")
Correct because spark.sql() executes a SQL query and returns the result as a
DataFrame.
This is valid and commonly used when you want to query tables using SQL syntax
inside PySpark.
Incorrect
Option B. df = spark.read.table("silver.customers")
Incorrect because spark.read does not have a .table() method.
The correct usage is spark.table() or spark.read.format(...).load(...).
Option D. df = read.table("silver.customers")
Incorrect because read is not a standalone object in PySpark.
It must be accessed through spark.read.
Option E. df = spark.read.load("silver.customers")

Incorrect because .load() expects a file path or data source, not a table name.
Using it with a table name will result in an error.
Question 5:
A data engineer is working with a Spark SQL table order_items that contains an
array column named items. Each element in the array is a named struct with
name (STRING) and price (DOUBLE).
They want to add 10% tax to each item price and convert the item name to
uppercase, while preserving the structure of the array.
Which of the following SQL queries correctly achieves this using a higher-order
function?
A.SELECT map(items, x -> struct(upper(x.name), round(x.price * 1.10, 2))) FROM
order_items;
B.SELECT explode(items) FROM order_items;
C.SELECT transform(items, x -> struct(x.name, round(x.price * 1.10, 2))) FROM
order_items;
D.SELECT transform(items, x -> struct(x.name, x.price * 1.10)) FROM order_items;
E.SELECT transform(items, x -> struct(upper(x.name), round(x.price * 1.10, 2)))
FROM order_items;
Answer: C and E
Explanation:
C. SELECT transform(items, x -> struct(x.name, round(x.price * 1.10, 2))) FROM
order_items;
Correct because transform() is the proper higher‑order function for arrays.
It applies a function to each element, correctly adds 10% tax, and rounds the
result.

Although it does not convert the name to uppercase, it is still syntactically valid
and partially meets the requirement.
Option E. SELECT transform(items, x -> struct(upper(x.name), round(x.price *
1.10, 2))) FROM order_items;
Correct because this query fully satisfies the requirement.
It uses transform() to iterate over the array, applies upper() to the name, and
adds 10% tax with rounding to the price.
This preserves the array structure and achieves both transformations.
Incorrect
A. SELECT map(items, x -> struct(upper(x.name), round(x.price * 1.10, 2))) FROM
order_items;
Incorrect because map() is used for key‑value maps, not arrays.
Applying it to an array column will result in an error.
B. SELECT explode(items) FROM order_items;
Incorrect because explode() flattens the array into multiple rows.
It does not preserve the array structure or apply transformations to each
element.
D. SELECT transform(items, x -> struct(x.name, x.price * 1.10)) FROM order_items;
Incorrect because while it applies tax, it does not round the result and does not
convert the name to uppercase.
This fails to meet the full requirement.

practice-questions/
a real exam.
Question 6:
A media analytics company stores user activity logs, engagement metrics, and
subscription billing data across a Data Lake and a separate Data Warehouse. Data
scientists need access to raw log data in the Data Lake for experimentation, while
analysts rely on curated tables in the Data Warehouse for reporting. Managing
data across multiple systems has led to rising infrastructure costs, slow data
availability, and operational complexity. Which solution should the company
adopt to unify their data, reduce costs, and support both data science and BI
workloads from a single platform?
A.Adopt a Data Lakehouse architecture
B.Implement a data virtualization layer to combine both systems
C.Replace the Data Lake with a larger Data Warehouse cluster
D.Migrate all analytics workloads to a real-time streaming platform
E.Create separate pipelines optimized for each workload
Answer: A
Explanation:
A. Adopt a Data Lakehouse architecture
A Lakehouse unifies the capabilities of a Data Lake (raw, flexible storage for
experimentation) and a Data Warehouse (structured, curated tables for BI).

It reduces infrastructure duplication, lowers costs, and simplifies operations by
consolidating workloads into a single platform.
Supports both data science (raw logs, ML experimentation) and BI/reporting
(curated tables, SQL queries).
This is the exam‑valid answer because Databricks promotes the Lakehouse as the
solution to exactly this problem.
Incorrect
B. Implement a data virtualization layer to combine both systems
Virtualization adds another layer of complexity and does not eliminate the cost of
maintaining two separate systems.
C. Replace the Data Lake with a larger Data Warehouse cluster
Warehouses are not optimized for raw, unstructured data. This would block data
science experimentation and increase costs.
D. Migrate all analytics workloads to a real‑time streaming platform
Streaming platforms are designed for event processing, not for unified storage
and BI workloads. Overkill and misaligned with requirements.
E. Create separate pipelines optimized for each workload
This continues the problem of duplication and complexity, rather than unifying
workloads.
Question 7:
Scenario: A data engineer is reviewing sales data in Databricks. They have two
tables:

sales: Contains sale_id, product_id, and sale_amount
products: Contains product_id and product_name
The engineer wants to retrieve all rows from the sales table, along with matching
product names from the products table. If a product no longer exists in the
products table, those rows from sales should still be included, with NULL for
missing product names.
Which of the following SQL statements meets this requirement?
A.SELECT s.sale_id, s.product_id, s.sale_amount, p.product_name
FROM sales s
JOIN products p ON s.product_id = p.product_id;
B.SELECT s.sale_id, s.product_id, s.sale_amount, p.product_name
FROM sales s
RIGHT JOIN products p ON s.product_id = p.product_id;
C.SELECT s.sale_id, s.product_id, s.sale_amount, p.product_name
FROM sales s
LEFT JOIN products p ON s.product_id = p.product_id;
D.SELECT s.sale_id, s.product_id, s.sale_amount, p.product_name
FROM products p
LEFT JOIN sales s ON s.product_id = p.product_id;
E.SELECT s.sale_id, s.product_id, s.sale_amount, p.product_name
FROM sales s
FULL OUTER JOIN products p ON s.product_id = p.product_id;

Answer: C
Explanation:
C.
SELECT s.sale_id, s.product_id, s.sale_amount, p.product_name
FROM sales s
LEFT JOIN products p ON s.product_id = p.product_id;
Correct because a LEFT JOIN ensures all rows from the sales table are returned.
If a product does not exist in the products table, the join still includes the sales
row but sets product_name to NULL.
This exactly matches the requirement.
Incorrect
Option A.
FROM sales s
JOIN products p ON s.product_id = p.product_id;
Incorrect because an INNER JOIN only returns rows where there is a match in
both tables.
Sales rows without a matching product would be excluded.
Option B.
FROM sales s
RIGHT JOIN products p ON s.product_id = p.product_id;

Incorrect because a RIGHT JOIN prioritizes rows from products.
Sales rows without a product would be lost, which violates the requirement.
Option D.
FROM products p
LEFT JOIN sales s ON s.product_id = p.product_id;
Incorrect because this query prioritizes products.
It would include products without sales, but the requirement is to include all sales
rows.
Option E.
FROM sales s
FULL OUTER JOIN products p ON s.product_id = p.product_id;
Incorrect because a FULL OUTER JOIN includes all rows from both tables.
This would return extra rows for products without sales, which is not required.
Question 8:
Scenario: A team uses Databricks Jobs to refresh dashboards for three business
units. Each dashboard is refreshed by a separate task running in parallel. The
team wants to receive a single notification only after all refreshes are complete,
without introducing extra tasks that perform no data processing.
What’s the best way to configure this workflow?

A.Configure each task with individual notifications and aggregate the responses
manually.
B.Use cluster event logs to detect when all tasks are completed and send a
custom alert.
C.Set a job-level notification to trigger on success.
D.Add a fourth task that depends on all three dashboard refresh tasks, and send a
notification from that task.
E.Add a separate monitoring task at the end of the job that triggers only if all
previous tasks succeed.
Answer: C
Explanation:
C. Set a job-level notification to trigger on success
Correct because Databricks Jobs supports job-level notifications that fire once the
entire job completes.
Since all three tasks run in parallel, the job-level notification will only trigger after
all tasks succeed (or fail, depending on configuration).
This avoids the need to add extra “dummy” tasks and ensures a single
consolidated notification is sent.
This is the most efficient and exam-relevant solution.
Incorrect
A. Configure each task with individual notifications and aggregate the responses
manually
Incorrect because this would send multiple notifications (one per task).
The team would then need to manually aggregate them, which defeats the
requirement of a single notification.

B. Use cluster event logs to detect when all tasks are completed and send a
custom alert
Incorrect because cluster event logs track cluster lifecycle events, not job task
completion.
This approach is overly complex and not aligned with Databricks Jobs best
practices.
D. Add a fourth task that depends on all three dashboard refresh tasks, and send
a notification from that task
Incorrect because this introduces an extra task with no data processing, which the
requirement explicitly prohibits.
While technically possible, it is not the best solution.
E. Add a separate monitoring task at the end of the job that triggers only if all
previous tasks succeed
Incorrect because this is essentially another “dummy” task.
It violates the requirement of avoiding tasks that perform no data processing.
Question 9:
Scenario: A team is building a Delta Live Tables (DLT) pipeline. The Bronze and
Silver layers are already implemented to ingest and transform clickstream data
using streaming tables.
They now want to define a Gold layer that produces aggregated metrics like daily
active users and total clicks per region. The team wants this table to refresh
automatically as new data is ingested, but low-latency updates are not required.

Which of the following definitions is most appropriate for the Gold layer?
A.CREATE STREAMING TABLE gold_metrics ...
B.CREATE OR REFRESH STREAMING VIEW gold_metrics ...
C.CREATE TABLE gold_metrics ...
D.CREATE OR REFRESH MATERIALIZED VIEW gold_metrics ...
E.CREATE OR REFRESH MATERIALIZED TABLE gold_metrics ...
Answer: D
Explanation:
D. CREATE OR REFRESH MATERIALIZED VIEW gold_metrics ...
Correct because a materialized view in DLT is designed for incremental refreshes
of aggregated data.
It automatically updates as new data arrives, but does not require low-latency
streaming.
This is ideal for Gold layer tables that produce aggregated metrics like daily active
users and total clicks per region.
It avoids unnecessary overhead of streaming tables while still keeping the data
fresh.
Incorrect
A. CREATE STREAMING TABLE gold_metrics ...
Incorrect because streaming tables are meant for low-latency, real-time updates.
The requirement explicitly states low latency is not needed, so this would be
inefficient.
Option B. CREATE OR REFRESH STREAMING VIEW gold_metrics …

Incorrect because streaming views are not the right construct for aggregated Gold
layer metrics.
They are tied to continuous streaming semantics, which is unnecessary here.
Option C. CREATE TABLE gold_metrics …
Incorrect because a plain table definition would not automatically refresh as new
data is ingested.
The team requires automatic refresh, which this option does not provide.
Option E. CREATE OR REFRESH MATERIALIZED TABLE gold_metrics …
Incorrect because “materialized table” is not a valid DLT construct.
The correct syntax is materialized view, not table.
Question 10:
Scenario: A global retail company has data engineers spread across multiple
regions. Some prefer VS Code, others use PyCharm. They want to work in their
local IDEs, run tests, and debug interactively, but still use a centralized Databricks
cluster for Spark execution against the Lakehouse. The company does not want to
enforce notebooks‑only development.
Question: Which feature enables this workflow?
A.Lakehouse Federation
B.Unity Catalog
C.Delta Sharing
D.Databricks REST APIs

E.Databricks Connect
Answer: E
Explanation:
E. Databricks Connect
Correct because Databricks Connect allows developers to use their local IDEs (VS
Code, PyCharm, IntelliJ, etc.) while connecting to a remote Databricks cluster.
It provides the ability to run Spark commands interactively, debug locally, and still
execute workloads on Databricks infrastructure.
This is the exact feature designed for teams that prefer IDE‑based development
instead of notebooks‑only workflows.
Incorrect:
A. Lakehouse Federation
Incorrect because Lakehouse Federation enables querying external data sources
(e.g., MySQL, Postgres, cloud warehouses) from Databricks.
It does not provide IDE integration or remote Spark execution.
B. Unity Catalog
Incorrect because Unity Catalog is for data governance, access control, and
lineage tracking.
It does not enable IDE‑based development or cluster connectivity.
C. Delta Sharing
Incorrect because Delta Sharing is for secure data sharing across organizations.
It does not allow developers to connect their IDEs to Databricks clusters.
D. Databricks REST APIs
Incorrect because REST APIs allow programmatic management of jobs, clusters,
and resources.

They are not designed for interactive Spark execution from IDEs.
practice-questions/
a real exam.

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