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Databricks Certified Data Engineer Professional Sample Questions:

1. An organization processes customer data from web and mobile applications. Data includes names, emails, phone numbers, and location history. Data arrives both as batch files (from SFTP daily) and streaming JSON events (from Kafka in real-time).
To comply with data privacy policies, the following requirements must be met:
- Personally Identifiable Information (PII) such as email, phone
number, and IP address must be masked or anonymized before storage.
- Both batch and streaming pipelines must apply consistent PII
handling.
- Masking logic must be auditable and reproducible.
- The masked data must remain usable for downstream analytics.
How should the data engineer design a compliant data pipeline on Databricks that supports both batch and streaming modes, applies data masking to PII, and maintains traceability for audits?

A) Ingest both batch and streaming data using Lakeflow Declarative Pipelines, and apply masking via Unity Catalog column masks at read time to avoid modifying the data during ingestion.
B) Use Lakeflow Declarative Pipelines for batch and streaming ingestion, define a PII masking function, and apply it during Bronze ingestion before writing to Delta Lake.
C) Load batch data with notebooks and ingest streaming data with SQL Warehouses; use Unity Catalog column masks on Silver tables to redact fields after storage.
D) Allow PII to be stored unmasked in Bronze for lineage tracking, then apply masking logic in Gold tables used for reporting.

2. A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

A) Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
B) Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
C) Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
D) Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.

3. Which method can be used to determine the total wall-clock time it took to execute a query?

A) In the Spark UI, take the job duration of the longest-running job associated with that query.
B) Open the Query Profiler associated with that query and use the Aggregated task time metric.
C) In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
D) Open the Query Profiler associated with that query and use the Total wall-clock duration metric.

4. A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A) Task queueing resulting from improper thread pool assignment.
B) Credential validation errors while pulling data from an external system.
C) Spill resulting from attached volume storage being too small.
D) Network latency due to some cluster nodes being in different regions from the source data
E) Skew caused by more data being assigned to a subset of spark-partitions.

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

Solutions: