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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

A) At some point, the upstream data provider switched everything to multi-line JSON.
B) The badRecordsPath location is accumulating many small files.
C) The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
D) The source data is valid JSON but does not conform to the defined schema in some way.

2. An analytics team wants to run a short-term experiment in Databricks SQL on the customer transactions Delta table (about 20 billion records) created by the data engineering team. Which strategy should the data engineering team use to ensure minimal downtime and no impact on the ongoing ETL processes?

A) Deep clone the table for the analytics team.
B) Give the analytics team direct access to the production table.
C) Shallow clone the table for the analytics team.
D) Create a new table for the analytics team using a CTAS statement.

3. The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source tables has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A) An incremental job will leverage information in the state store to identify unjoined rows in the source tables and write these rows to the enriched_iteinized_orders_by_account table.
B) A batch job will update the enriched_itemized_orders_by_account table, replacing only those rows that have different values than the current version of the table, using accountID as the primary key.
C) No computation will occur until enriched_itemized_orders_by_account is queried; upon query materialization, results will be calculated using the current valid version of data in each of the three tables referenced in the join logic.
D) The enriched_itemized_orders_by_account table will be overwritten using the current valid version of data in each of the three tables referenced in the join logic.
E) An incremental job will detect if new rows have been written to any of the source tables; if new rows are detected, all results will be recalculated and used to overwrite the enriched_itemized_orders_by_account table.

4. Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

A) They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.
B) They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.
C) They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
D) They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.

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

Solutions: