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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q134-Q139):

NEW QUESTION # 134
A Delta Lake table in the Lakehouse named customer_parsams 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.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?

A. Parse the Delta Lake transaction log to identify all newly written data files.
B. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
C. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
D. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.

Answer: D

Explanation:
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.

NEW QUESTION # 135
Which statement describes integration testing?

A. Validates interactions between subsystems of your application
B. Requires manual intervention
C. Requires an automated testing framework
D. Validates behavior of individual elements of your application
E. Validates an application use case

Answer: A

Explanation:
Integration testing is a type of software testing where components of the software are gradually integrated and then tested as a unified group.

NEW QUESTION # 136
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame named preds with the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A. preds.write.mode("append").saveAsTable("churn_preds")
B.
C. preds.write.format("delta").save("/preds/churn_preds")
D.
E.

Answer: A

NEW QUESTION # 137
The marketing team is looking to share data in an aggregate table with the sales organization, but the field names used by the teams do not match, and a number of marketing specific fields have not been approval for the sales org.
Which of the following solutions addresses the situation while emphasizing simplicity?

A. Instruct the marketing team to download results as a CSV and email them to the sales organization.
B. Create a new table with the required schema and use Delta Lake's DEEP CLONE functionality to sync up changes committed to one table to the corresponding table.
C. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
D. Create a view on the marketing table selecting only these fields approved for the sales team alias the names of any fields that should be standardized to the sales naming conventions.
E. Add a parallel table write to the current production pipeline, updating a new sales table that varies Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from as required from marketing table.

Answer: D

Explanation:
Creating a view is a straightforward solution that can address the need for field name standardization and selective field sharing between departments. A view allows for presenting a transformed version of the underlying data without duplicating it. In this scenario, the view would only include the approved fields for the sales team and rename any fields as per their naming conventions.

NEW QUESTION # 138
Review the following error traceback:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

Which statement describes the error being raised?

A. The code executed was PvSoark but was executed in a Scala notebook.
B. There is a type error because a column object cannot be multiplied.
C. There is no column in the table named heartrateheartrateheartrate
D. There is a type error because a DataFrame object cannot be multiplied.
E. There is a syntax error because the heartrate column is not correctly identified as a column.

Answer: C

Explanation:
https://sparkbyexamples.com/spark/spark-cannot-resolve-given-input-columns/

NEW QUESTION # 139
......

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