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

2. A data engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays. How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?

A) It uses sequence_by to order events and retains tombstones for deleted rows until older sequences are processed.
B) It runs VACUUM on the target table to purge conflicting records.
C) It ignores deletions if they arrive after updates for the same key.
D) It manually sorts incoming events by timestamp before applying changes.

3. A table in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours.
Which approach would simplify the identification of these changed records?

A) Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
B) Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
C) Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.
D) Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
E) Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.

4. 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) SET TAGS FOR table_name AS ('key1' = 'value1', 'key2' = 'value2');
D) APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');

5. A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A) The Parquet file footers are scanned for min and max statistics for the latitude column
B) The Hive metastore is scanned for min and max statistics for the latitude column
C) All records are cached to attached storage and then the filter is applied
D) All records are cached to an operational database and then the filter is applied
E) The Delta log is scanned for min and max statistics for the latitude column

Solutions: