Databricks Certified Data Engineer Professional Sample Questions:

1. The data engineering team maintains the following code:

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

A) A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
B) The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.
C) The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
D) An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.
E) An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.

2. The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
MERGE INTO customers
USING (
SELECT updates.customer_id as merge_ey, updates .*
FROM updates
UNION ALL
SELECT NULL as merge_key, updates .*
FROM updates JOIN customers
ON updates.customer_id = customers.customer_id
WHERE customers.current = true AND updates.address <> customers.address ) staged_updates ON customers.customer_id = mergekey WHEN MATCHED AND customers. current = true AND customers.address <> staged_updates.address THEN UPDATE SET current = false, end_date = staged_updates.effective_date WHEN NOT MATCHED THEN INSERT (customer_id, address, current, effective_date, end_date) VALUES (staged_updates.customer_id, staged_updates.address, true, staged_updates.effective_date, null) Which statement describes this implementation?

A) The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
B) The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
C) The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
D) The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

3. Which statement describes Delta Lake optimized writes?

A) Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
B) Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
C) A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
D) An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.

4. A data engineer wants to automate job monitoring and recovery in Databricks using the Jobs API.
They need to list all jobs, identify a failed job, and rerun it. Which sequence of API actions should the data engineer perform?

A) Use the jobs/list endpoint to list jobs, then use the jobs/create endpoint to create a new job, and run the new job using jobs/run-now.
B) Use the jobs/list endpoint to list jobs, check job run statuses with jobs/runs/list, and rerun a failed job using jobs/run-now.
C) Use the jobs/cancel endpoint to remove failed jobs, then recreate them with jobs/create and run the new ones.
D) Use the jobs/get endpoint to retrieve job details, then use jobs/update to rerun failed jobs.

5. A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bpm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A) Set the SkipChangeCommits flag to true raw_lot
B) Set the pipelines, reset, allowed property to false on raw_iot
C) Set the pipelines, reset, allowed property to false on bpm_stats
D) Set the skipChangeCommits flag to true on bpm_stats

Solutions: