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

Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A) Perform an insert-only merge with a matching condition on a unique key.
B) Rely on Delta Lake schema enforcement to prevent duplicate records.
C) VACUUM the Delta table after each batch completes.
D) Set the configuration delta.deduplicate = true.
E) Perform a full outer join on a unique key and overwrite existing data.

2. What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

A) Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.
B) Dynamic data masking is applied only at rest, so it does not affect query performance.
C) PII masking is only required for direct identifiers.
D) Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.

3. In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

A)

B)

C)

D)

E)

4. A data engineer is reviewing the PySpark code to copy a part of the production dataset to the sandbox environment, and needs to be sure that no PII(Personally Identifiable Information) data is being copied. After checking the sales table, the data engineer notices that it has user emails as the only PII data included as well as being the only column to identify the user.
from pyspark.sql import functions as F

Which anonymised code should be used to achieve the required outcome?

A) df.withColumn ("user_email", F.sha2 ("user_email"))
B) df.withColumn ("user_email", F.regexp_replace ("user_eamail", "@*", "@anonymized.com"))
C) df.withColumn ("hashed_email", sha2 ("user_email"))
D) df.withColumn ("user_emai", F.expr("uuid()"))

5. A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A) Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
B) Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
C) Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
D) Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.

Solutions: