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Free Databricks-Certified-Professional-Data-Engineer Exam Braindumps
Certified Data Engineer Professional Questions 153–160 (page: 21)

Updated On: 16-Apr-2026
Page 21 of 44
PDF with 339 Questions

The data engineering team is configuring environments for development, testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team wants to develop and test against data as similar to production data as possible.

A junior data engineer suggests that production data can be mounted to the development and testing environments, allowing pre-production code to execute against production data. Because all users have admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.

Which statement captures best practices for this situation?

  1. All development, testing, and production code and data should exist in a single, unified workspace; creating separate environments for testing and development complicates administrative overhead.
  2. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.
  3. Because access to production data will always be verified using passthrough credentials, it is safe to mount data to any Databricks development environment.
  4. Because Delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data; as such, it is generally safe to mount production data anywhere.

Answer(s): B


Reference:



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 the workspace is being configured, make sure that external cloud object storage has been mounted.
  3. Whenever a table is being created, make sure that the LOCATION keyword is used.
  4. When tables are created, make sure that the UNMANAGED keyword is used in the CREATE TABLE statement.

Answer(s): C



The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing-specific fields have not been approved for the sales org.

Which of the following solutions addresses the situation while emphasizing simplicity?

  1. Create a view on the marketing table selecting only those fields approved for the sales team; alias the names of any fields that should be standardized to the sales naming conventions.
  2. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up
    changes committed to one table to the corresponding table.
  3. Use a CTAS statement to create a derivative table from the marketing table; configure a production job to propagate changes.
  4. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from the marketing table.

Answer(s): A



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 scan the parquet file footers to identify each row that meets the filter criteria.
  4. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.

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 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


Reference:



A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:



A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.

Which statement describes the outcome of this batch insert?

  1. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
  2. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
  3. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
  4. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.

Answer(s): B



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 constraints and multi-table inserts to validate records on write.

Which consideration will impact the decisions made by the engineer while migrating this workload?

  1. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
  2. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
  3. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
  4. All Delta Lake transactions are ACID compliant against a single table, and Databricks does not enforce foreign key constraints.

Answer(s): D


Reference:



A data architect has heard about Delta Lake's built-in versioning and time travel capabilities. For auditing purposes, they have a requirement to maintain a full record of all valid street addresses as they appear in the customers table.

The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.

Which piece of information is critical to this decision?

  1. Data corruption can occur if a query fails in a partially completed state because Type 2 tables require setting multiple fields in a single update.
  2. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
  3. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
  4. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

Answer(s): D



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