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Databricks Databricks-Certified-Professional-Data-Engineer Exam Questions
Certified Data Engineer Professional (Page 6 )

Updated On: 25-Apr-2026
Page 6 of 44
PDF with 339 Questions

The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.

The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.

Which statement exemplifies best practices for implementing this system?

  1. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
  2. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
  3. Storing all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.
  4. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
  5. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.

Answer(s): A



The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.

Which approach will ensure that this requirement is met?

  1. Whenever a database is being created, make sure that the LOCATION keyword is used.
  2. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
  3. Whenever a table is being created, make sure that the LOCATION keyword is used.
  4. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
  5. When the workspace is being configured, make sure that external cloud object storage has been mounted.

Answer(s): C



To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.

The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by

numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.

Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

  1. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
  2. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
  3. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
  4. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
  5. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.

Answer(s): B



A Delta Lake table representing metadata about content posts from users has the following schema:

user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE

This table is partitioned by the date column. A query is run with the following filter:

longitude < 20 & longitude > -20

Which statement describes how data will be filtered?

  1. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
  2. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.
  3. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
  4. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
  5. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.

Answer(s): D



A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.

The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.

Assuming that all data governance considerations are accounted for, which statement accurately informs this

decision?

  1. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
  2. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
  3. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
  4. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.
  5. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.

Answer(s): C



The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.

A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:



A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.

Which statement explains the cause of this failure?

  1. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
  2. The activity_details table already exists; CHECK constraints can only be added during initial table creation.
  3. The activity_details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.
  4. The activity_details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
  5. The current table schema does not contain the field valid_coordinates; schema evolution will need to be enabled before altering the table to add a constraint.

Answer(s): C



Which of the following is true of Delta Lake and the Lakehouse?

  1. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
  2. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
  3. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
  4. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
  5. Z-order can only be applied to numeric values stored in Delta Lake tables.

Answer(s): B



The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.

The following logic is used to process these records.



Which statement describes this implementation?

  1. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
  2. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
  3. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
  4. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
  5. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.

Answer(s): B



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What the Databricks-Certified-Professional-Data-Engineer Exam Tests and How to Pass It

The Databricks-Certified-Professional-Data-Engineer certification is designed for experienced data engineers who are responsible for building, deploying, and maintaining complex data pipelines within the Databricks Data Intelligence Platform. This certification validates that a professional possesses the advanced technical skills required to manage the entire data lifecycle, from initial ingestion and acquisition to sophisticated transformation, modelling, and final delivery. Organizations that hire for this role are typically looking for individuals who can not only write efficient code but also architect scalable solutions that adhere to best practices in data governance, security, and cost management. Because this is a professional-level credential, it serves as a benchmark for senior-level proficiency, demonstrating that the candidate can handle the nuances of production-grade data environments where performance, reliability, and compliance are critical business requirements.

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Updated on: 27 April, 2026

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