Free Databricks-Certified-Professional-Data-Engineer Exam Braindumps – New 2026 Databricks Pratice Exam [Q65-Q86]

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Free Databricks-Certified-Professional-Data-Engineer Exam Braindumps – New 2026 Databricks Pratice Exam

Practice Test for Databricks-Certified-Professional-Data-Engineer Certification Real 2026 Mock Exam

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

Section	Weight	Objectives
Data Sharing and Federation	5%	– Unity Catalog data sharing – Cross-workspace and cross-cloud access
Monitoring and Alerting	10%	– Pipeline observability and logging – Setting up alerts and notifications – Performance and health monitoring
Cost & Performance Optimisation	13%	– Storage optimization (partitioning, Z-order, indexing) – Query optimization and caching – Cluster configuration and scaling
Ensuring Data Security and Compliance	10%	– Data encryption and masking – Access control and permissions – Compliance standards implementation
Developing Code for Data Processing using Python and SQL	22%	– Batch and incremental processing logic – Data transformation and aggregation – Integration with Databricks APIs and tools
Debugging and Deploying	10%	– CI/CD and DevOps practices – Troubleshooting pipelines and errors – Deployment using bundles, CLI, and APIs
Data Ingestion & Acquisition	7%	– Connecting to diverse data sources – Auto Loader and streaming ingestion – Schema inference and evolution
Data Transformation, Cleansing, and Quality	10%	– Handling missing or inconsistent data – Data validation and quality checks – Standardization and normalization
Data Governance	7%	– Unity Catalog management – Policy enforcement – Data lineage and metadata tracking
Data Modelling	6%	– Schema design and management – Medallion Architecture implementation – Delta Lake table design

Q65. A data engineering team has been using a Databricks SQL query to monitor the performance of an ELT job.
The ELT job is triggered by a specific number of input records being ready to process. The Databricks SQL
query returns the number of minutes since the job’s most recent runtime.
Which of the following approaches can enable the data engineering team to be notified if the ELT job has not
been run in an hour?

They can set up an Alert for the query to notify when the ELT job fails

They can set up an Alert for the accompanying dashboard to notify them if the returned value is greater
than 60

They can set up an Alert for the accompanying dashboard to notify when it has not re-freshed in 60
minutes

They can set up an Alert for the query to notify them if the returned value is greater than 60

This type of alerting is not possible in Databricks

Q66. Which one of the following is not a Databricks lakehouse object?

Tables

Views

Database/Schemas

Catalog

Functions

Stored Procedures

Q67. A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.

Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly- parallel writes.

Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake’s upsert functionality.

Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.

Q68. The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

Group members have full permissions on the prod database and can also assign permissions to other users or groups.

Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.

Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.

Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.

Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.

Q69. Review the following error traceback:

Which statement describes the error being raised?

The code executed was PvSoark but was executed in a Scala notebook.

There is no column in the table named heartrateheartrateheartrate

There is a type error because a column object cannot be multiplied.

There is a type error because a DataFrame object cannot be multiplied.

There is a syntax error because the heartrate column is not correctly identified as a column.

Q70. Which REST API call can be used to review the notebooks configured to run as tasks in a multi-task job?

/jobs/runs/list

/jobs/runs/get-output

/jobs/runs/get

/jobs/get

/jobs/list

Q71. Which of the following python statement can be used to replace the schema name and table name in the query statement?

1.table_name = “sales”
2.schema_name = “bronze”
3.query = f”select * from schema_name.table_name”

1.table_name = “sales”
2.schema_name = “bronze”
3.query = “select * from {schema_name}.{table_name}”

1.table_name = “sales”
2.schema_name = “bronze”
3.query = f”select * from { schema_name}.{table_name}”

1.table_name = “sales”
2.schema_name = “bronze”
3.query = f”select * from + schema_name +”.”+table_name”

Q72. Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

configure

jobs

libraries

workspace

Q73. A junior data engineer on your team has implemented the following code block.

The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table.
When this query is executed, what will happen with new records that have the same event_id as an existing record?

They are merged.

They are ignored.

They are updated.

They are inserted.

They are deleted.

Q74. What is the main difference between the silver layer and the gold layer in medalion architecture?

Silver may contain aggregated data

Gold may contain aggregated data

Data quality checks are applied in gold

Silver is a copy of bronze data

God is a copy of silver data

Q75. Which of the following statements are true about a lakehouse?

Lakehouse only supports Machine learning workloads and Data warehouses support BI workloads

Lakehouse only supports end-to-end streaming workloads and Data warehouses support Batch workloads

Lakehouse does not support ACID

Lakehouse do not support SQL

Lakehouse supports Transactions

Q76. A security analytics pipeline must enrich billions of raw connection logs with geolocation data. The join hinges on finding which IPv4 range each event’s address falls into.
Table 1: network_events (≈ 5 billion rows)
event_id ip_int
42 3232235777
Table 2: ip_ranges (≈ 2 million rows)
start_ip_int end_ip_int country
3232235520 3232236031 US
The query is currently very slow:
SELECT n.event_id, n.ip_int, r.country
FROM network_events n
JOIN ip_ranges r
ON n.ip_int BETWEEN r.start_ip_int AND r.end_ip_int;
Which change will most dramatically accelerate the query while preserving its logic?

Increase spark.sql.shuffle.partitions from 200 to 10000.

Add a range-join hint /*+ RANGE_JOIN(r, 65536) */.

Force a sort-merge join with /*+ MERGE(r) */.

Add a broadcast hint: /*+ BROADCAST(r) */ for ip_ranges.

Q77. A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

Integration

Unit

Manual

functional

Q78. A data engineering team is in the process of converting their existing data pipeline to utilize Auto Loader for
incremental processing in the ingestion of JSON files. One data engineer comes across the following code
block in the Auto Loader documentation:
1. (streaming_df = spark.readStream.format(“cloudFiles”)
2. .option(“cloudFiles.format”, “json”)
3. .option(“cloudFiles.schemaLocation”, schemaLocation)
4. .load(sourcePath))
Assuming that schemaLocation and sourcePath have been set correctly, which of the following changes does
the data engineer need to make to convert this code block to use Auto Loader to ingest the data?

There is no change required. The inclusion of format(“cloudFiles”) enables the use of Auto Loader

There is no change required. Databricks automatically uses Auto Loader for streaming reads

The data engineer needs to change the format(“cloudFiles”) line to format(“autoLoader”)

The data engineer needs to add the .autoLoader line before the .load(sourcePath) line

There is no change required. The data engineer needs to ask their administrator to turn on Auto Loader

Q79. A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint2.0/jobs/create.

Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?

Three new jobs named “Ingest new data” will be defined in the workspace, and they will each run once daily.

The logic defined in the referenced notebook will be executed three times on new clusters with the configurations of the provided cluster ID.

Three new jobs named “Ingest new data” will be defined in the workspace, but no jobs will be executed.

One new job named “Ingest new data” will be defined in the workspace, but it will not be executed.

The logic defined in the referenced notebook will be executed three times on the referenced existing all purpose cluster.

Q80. The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property”contains_pii” = true.
The following SQL DDL statement is executed to create a new table:

Which command allows manual confirmation that these three requirements have been met?

DESCRIBE EXTENDED dev.pii test

DESCRIBE DETAIL dev.pii test

SHOW TBLPROPERTIES dev.pii test

DESCRIBE HISTORY dev.pii test

SHOW TABLES dev

Q81. A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a “dev” target to be the default and override specific paths.
How should the team use databricks.yml to satisfy these requirements?

Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.

Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.

Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.

Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default: true and override workspace paths or artifacts under that target.

Q82. A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a “dev” target to be the default and override specific paths.
How should the team use databricks.yml to satisfy these requirements?

Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.

Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.

Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.

Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default: true and override workspace paths or artifacts under that target.

Q83. The marketing team is launching a new campaign to monitor the performance of the new campaign for the first two weeks, they would like to set up a dashboard with a refresh schedule to run every 5 minutes, which of the below steps can be taken to reduce of the cost of this refresh over time?

Reduce the size of the SQL Cluster size

Reduce the max size of auto scaling from 10 to 5

Setup the dashboard refresh schedule to end in two weeks

Change the spot instance policy from reliability optimized to cost optimized

Always use X-small cluster

Q84. You have accidentally deleted records from a table called transactions, what is the easiest way to restore the records deleted or the previous state of the table? Prior to deleting the version of the table is 3 and after delete the version of the table is 4.

RESTORE TABLE transactions FROM VERSION as of 4

RESTORE TABLE transactions TO VERSION as of 3
C .
1.INSERT INTO OVERWRITE transactions
2.SELECT * FROM transactions VERSION AS OF 3
3.MINUS
4.SELECT * FROM transactions

1.INSERT INTO OVERWRITE transactions
2.SELECT * FROM transactions VERSION AS OF 4
3.INTERSECT
4.SELECT * FROM transactions

COPY OVERWRITE transactions from VERSION as of 3

Q85. How does Lakehouse replace the dependency on using Data lakes and Data warehouses in a Data and Analytics solution?

Open, direct access to data stored in standard data formats.

Supports ACID transactions.

Supports BI and Machine learning workloads

Support for end-to-end streaming and batch workloads

All the above

Q86. A data organization has adopted Delta Sharing to securely distribute curated datasets from a Unity Catalog-enabled workspace. The data engineering team shares large Delta tables internally via Databricks-to-Databricks and externally via Open Sharing for aggregated reports. While testing, they encounter challenges related to access control, data update visibility, and shareable object types.
What is a limitation of the Delta Sharing protocol or implementation when used with Databricks-to-Databricks or Open Sharing?

With Open Sharing, recipients cannot access Volumes, Models, or notebooks – only static Delta tables are supported.

Delta Sharing does not support Unity Catalog-enabled tables; only legacy Hive Metastore tables are shareable.

With Databricks-to-Databricks sharing, Unity Catalog recipients must re-ingest data manually using COPY INTO or REST APIs.

Delta Sharing (both Databricks-to-Databricks and Open Sharing) allows recipients to modify the source data if they have select privileges.

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

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