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

Section	Weight	Objectives
Topic 1: Databricks Lakehouse Platform	24%	- Delta Lake - Data Management - Unity Catalog - Lakehouse Architecture
Topic 2: Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization
Topic 3: Data Processing	28%	- Data Transformation - ETL Pipelines - Structured Streaming - Spark SQL
Topic 4: Data Quality and Governance	12%	- Data Lineage - Data Quality - Governance
Topic 5: Data Modeling and Storage	20%	- File Formats - Storage Optimization - Data Modeling

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
B. When a database is being created, make sure that the LOCATION keyword is used.
C. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
D. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
E. When the workspace is being configured, make sure that external cloud object storage has been mounted.

Answer: C

Explanation:
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored.
For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION `/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the
`/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.

NEW QUESTION # 109
A Data engineer wants to run unit's tests using common Python testing frameworks on python functions defined across several Databricks notebooks currently used in production. How can the data engineer run unit tests against function that work with data in production?

A. Define and unit test functions using Files in Repos
B. Define units test and functions within the same notebook
C. Define and import unit test functions from a separate Databricks notebook
D. Run unit tests against non-production data that closely mirrors production

Answer: D

Explanation:
The best practice for running unit tests on functions that interact with data is to use a dataset that closely mirrors the production data. This approach allows data engineers to validate the logic of their functions without the risk of affecting the actual production data. It's important to have a representative sample of production data to catch edge cases and ensure the functions will work correctly when used in a production environment.

NEW QUESTION # 110
A Databricks SQL dashboard has been configured to monitor the total number of records present in a collection of Delta Lake tables using the following query pattern:
SELECT COUNT (*) FROM table
Which of the following describes how results are generated each time the dashboard is updated?

A. The total count of rows will be returned from cached results unless REFRESH is run
B. The total count of records is calculated from the Delta transaction logs
C. The total count of records is calculated from the Hive metastore
D. The total count of rows is calculated by scanning all data files
E. The total count of records is calculated from the parquet file metadata

Answer: B

Explanation:
Delta Lake maintains a transaction log that records details about every change made to a table.
When you execute a count operation on a Delta table, Delta Lake can use the information in the transaction log to calculate the total number of records without having to scan all the data files.
This is because the transaction log includes information about the number of records in each file, allowing for an efficient aggregation of these counts to get the total number of records in the table.

NEW QUESTION # 111
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

A. In the Storage Detail screen, by noting which RDDs are not stored on disk
B. In the Executor's log file, by gripping for "predicate push-down"
C. In the Query Detail screen, by interpreting the Physical Plan
D. In the Delta Lake transaction log. by noting the column statistics
E. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column

Answer: C

Explanation:
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator.

NEW QUESTION # 112
A job runs four independent tasks (X, Y, Z, W) in parallel to process regional sales data. The Data Engineering team recently updated its cluster policy to ban cost-prohibitive instance types. Task Y now fails due to the newly enforced cluster policy restricting the use of a specific instance type.
A data engineer needs to resolve the failure quickly without disrupting the other tasks. How should the data engineer resolve the failure of tasks?

A. Use "Repair run", override the cluster configuration for Task Y to use a permitted instance type, and let Databricks re-run only Task Y.
B. Edit the global cluster policy to allow the restricted instance type, then re-run the entire job.
C. Manually create a new cluster for Task Y, update the job configuration, and trigger a full re-run.
D. Delete the failed run, disable the cluster policy, and re-execute all tasks.

Answer: A

Explanation:
Repair run allows re-running only the failed task without affecting successfully completed tasks.
Overriding the cluster configuration for the specific task resolves the policy violation quickly while keeping the rest of the job intact and minimizing disruption.

NEW QUESTION # 113
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