Question 1

The data engineering team maintains the following code:

Databricks-Certified-Professional-Data-Engineer-page61-image9

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?

Options :

A :

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.

B :

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.

C :

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.

D :

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

E :

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.

Answer: B

Question 2

Which of the following establishes a Python file as a notebook in Databricks ?

Options :

A : The magic command %databricks on the first line of the file’s source code

B : The comment ‘# Databricks notebook source’ on the first line of the file’s source code

C : The import of the dbutils.notebook module in the file’s source code

D : The creation of a spark session using SparkSession.builder.getOrCreate() in the file’s source code

E : None of the above statements is correct. Notebooks are binary files with .pydb file extension.

Answer: B

Question 3

A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A. If task A fails during a scheduled run, which statement describes the results of this run?

Options :

A :

Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed

B :

Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

C :

Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.

D :

Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.

E : Tasks B and C will be skipped; task A will not commit any changes because of stage failure.

Answer: D

Question 4

A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance. How should the data engineer create the patient_encounters table?

Options :

A :

Create an external table in Unity Catalog, specifying an S3 location for the data files. Enable predictive optimization through table properties, and configure Unity Catalog permissions for access controls.

B :

Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.

C :

Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, schedule jobs to run OPTIMIZE and VACUUM commands daily to achieve best performance.

D :

Create a managed table in Hive Metastore. Configure Hive Metastore permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.

Answer: B

Question 5

A junior data engineer is using the following code to de-duplicate raw streaming data and insert them in a target Delta table

1. spark.readStream

2. .table("orders_raw")

3. .dropDuplicates(["order_id", "order_timestamp"])

4. .writeStream

5. .option("checkpointLocation", "dbfs:/checkpoints")

6. .table("orders_unique")

A senior data engineer pointed out that this approach is not enough for having distinct records in the target table when there are late-arriving, duplicate records.

Which of the following could explain the senior data engineer’s remark?

Options :

A : Watermarking is also needed to only track state information for a window of time in which we expect records could be delayed.

B : A ranking function is also needed to ensure processing only the most recent records

C : A window function is also needed to apply deduplication for each non-overlapping interval.

D : The new records need also to be deduplicated against previously inserted data into the table.

E : More information is needed to determine the correct response

Answer: D

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