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

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

2. 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) Allow PII to be stored unmasked in Bronze for lineage tracking, then apply masking logic in Gold tables used for reporting.
D) 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.

3. A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?

A) ALTER TABLE table_name SET TAGS ('key1' = 'value1', 'key2' = 'value2');
B) COMMENT ON TABLE table_name TAGS ('key1' = 'value1', 'key2' = 'value2');
C) APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');
D) SET TAGS FOR table_name AS ('key1' = 'value1', 'key2' = 'value2');

4. A table is registered with the following code:

Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A) The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
B) All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.
C) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D) Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
E) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.

5. A security analytics pipeline must enrich billions of raw connection logs with geolocation data.
The join hinges on finding which IPv4 range each event's address falls into.
Table 1: network_events ( 5 billion rows)
event_id ip_int
42 3232235777
Table 2: ip_ranges ( 2 million rows)
start_ip_int end_ip_int country
3232235520 3232236031 US
The query is currently very slow:
SELECT n.event_id, n.ip_int, r.country
FROM network_events n
JOIN ip_ranges r
ON n.ip_int BETWEEN r.start_ip_int AND r.end_ip_int;
Which change will most dramatically accelerate the query while preserving its logic?

A) Force a sort-merge join with /*+ MERGE(r) */.
B) Add a broadcast hint: /*+ BROADCAST(r) */ for ip_ranges.
C) Add a range-join hint /*+ RANGE_JOIN(r, 65536) */.
D) Increase spark.sql.shuffle.partitions from 200 to 10000.

Solutions: