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Databricks is a cloud-based data analytics platform that provides solutions for data engineering, machine learning, and analytics. The Databricks-Certified-Professional-Data-Engineer (Databricks Certified Professional Data Engineer) Exam is a certification program designed to validate the skills and knowledge of data engineers working with the Databricks platform. Databricks-Certified-Professional-Data-Engineer Exam is designed to assess the candidate's ability to design, implement, and manage data pipelines using Databricks.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q38-Q43):

NEW QUESTION # 38
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion. However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
B. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
C. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
D. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.
E. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.

Answer: E

Explanation:
Partitioning the data by the topic field allows the company to apply different access control policies and retention policies for different topics. For example, the company can use the Table Access Control feature to grant or revoke permissions to the registration topic based on user roles or groups. The company can also use the DELETE command to remove records from the registration topic that are older than 14 days, while keeping the records from other topics indefinitely. Partitioning by the topic field also improves the performance of queries that filter by the topic field, as they can skip reading irrelevant partitions. Reference:
Table Access Control: https://docs.databricks.com/security/access-control/table-acls/index.html DELETE: https://docs.databricks.com/delta/delta-update.html#delete-from-a-table

NEW QUESTION # 39
A data engineer is using a Databricks SQL query to monitor the performance of an ELT job. The ELT job is triggered by a specific number of input records being ready to process. The Databricks SQL query returns the number of minutes since the job's most recent runtime. Which of the following approaches can enable the data engineering team to be notified if the ELT job has not been run in an hour?

A. They can set up an Alert for the query to notify when the ELT job fails.
B. They can set up an Alert for the accompanying dashboard to notify when it has not re-freshed in 60 minutes.
C. They can set up an Alert for the query to notify them if the returned value is greater than 60.
D. They can set up an Alert for the accompanying dashboard to notify them if the returned value is greater than 60.
E. This type of alert is not possible in Databricks

Answer: C

Explanation:
Explanation
The answer is, They can set up an Alert for the query to notify them if the returned value is greater than 60.
The important thing to note here is that alert can only be setup on query not on the dashboard, query can return a value, which is used if alert can be triggered.

NEW QUESTION # 40
If you create a database sample_db with the statement CREATE DATABASE sample_db what will be the default location of the database in DBFS?

A. Default location, DBFS:/user/
B. Default Storage account
C. Default location, /user/db/
D. Default Location, dbfs:/user/hive/warehouse
E. Statement fails "Unable to create database without location"

Answer: D

Explanation:
Explanation
The Answer is dbfs:/user/hive/warehouse this is the default location where spark stores user data-bases, the default can be changed using spark.sql.warehouse.dir a parameter. You can also provide a custom location using the LOCATION keyword.
Here is how this works,
Graphical user interface, text, application, email Description automatically generated

Default location

FYI, This can be changed used using cluster spark config or session config.
Modify spark.sql.warehouse.dir location to change the default location
Graphical user interface, text, application Description automatically generated

NEW QUESTION # 41
Which statement describes Delta Lake Auto Compaction?

A. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
B. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
C. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
D. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.

Answer: E

Explanation:
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB. Auto Compaction only compacts files that have not been compacted previously. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Compaction for Delta Lake on Databricks" section.
"Auto compaction occurs after a write to a table has succeeded and runs synchronously on the cluster that has performed the write. Auto compaction only compacts files that haven't been compacted previously."
https://learn.microsoft.com/en-us/azure/databricks/delta/tune-file-size

NEW QUESTION # 42
A data engineer wants to reflector the following DLT code, which includes multiple definition with very similar code:

In an attempt to programmatically create these tables using a parameterized table definition, the data engineer writes the following code.

The pipeline runs an update with this refactored code, but generates a different DAG showing incorrect configuration values for tables.
How can the data engineer fix this?

A. Wrap the loop inside another table definition, using generalized names and properties to replace with those from the inner table
B. Load the configuration values for these tables from a separate file, located at a path provided by a pipeline parameter.
C. Convert the list of configuration values to a dictionary of table settings, using table names as keys.
D. Convert the list of configuration values to a dictionary of table settings, using different input the for loop.

Answer: C

Explanation:
The issue with the refactored code is that it tries to use string interpolation to dynamically create table names within thedlc.tabledecorator, which will not correctly interpret the table names. Instead, by using a dictionary with table names as keys and their configurations as values, the data engineer can iterate over the dictionary items and use the keys (table names) to properly configure the table settings. This way, the decorator can correctly recognize each table name, and the corresponding configuration settings can be applied appropriately.

NEW QUESTION # 43
......

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