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

Section	Objectives
Topic 1: Data Modeling and Transformation	- Performance optimization techniques - Spark SQL transformations - Dimensional modeling concepts
Topic 2: Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics)
Topic 3: Delta Lake and Data Management	- Time travel and versioning - Delta Lake transactions and ACID properties - Schema evolution and enforcement
Topic 4: Data Ingestion and Processing	- Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns - Structured Streaming fundamentals
Topic 5: Production Pipelines and Orchestration	- Error handling and recovery strategies - Job scheduling and monitoring - Databricks Workflows

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q170-Q175):

NEW QUESTION # 170
A data engineer is implementing a job to download multiple PDF files from a third-party provided REST API endpoint by specifying different report types. The REST API is time-consuming and encounters intermittent errors, so the engineer wants to track each download activity to know when it fails and to retry partially, while providing scalable throughput. The engineer needs to download ten report types, and the list can be changed over time. How should the data engineer achieve this?

A. Define a list variable within a Notebook to loop through the report types to download them, and print the download results. Execute it as a Notebook tasks.
B. Use a foreach task with a list of report types as its inputs.
C. Define ten Notebook tasks to clearly track which report download failed.
D. Use a Delta Lake table to track each report download status as 10 rows, and use it as a source table to execute the download function as a Pandas UDF.

Answer: B

Explanation:
A foreach task allows the job to dynamically iterate over a configurable list of report types, execute downloads in parallel, and track the success or failure of each item independently. This enables scalable throughput, partial retries for failed downloads, and easy updates when the list of report types changes, without hardcoding tasks or introducing unnecessary complexity.

NEW QUESTION # 171
What is the first of a Databricks Python notebook when viewed in a text editor?

A. -- Databricks notebook source
B. %python
C. // Databricks notebook source
D. # MAGIC %python
E. # Databricks notebook source

Answer: E

Explanation:
https://docs.databricks.com/en/notebooks/notebook-export-import.html#import-a-file-and-convert- it-to-a-notebook

NEW QUESTION # 172
A departing platform owner currently holds ownership of multiple catalogs and controls storage credentials and external locations. A data engineer has been asked to ensure continuity: transfer catalog ownership to the platform team group, delegate ongoing privilege management, and retain the ability to receive and share data via Delta Sharing. Which role must be in place to perform these actions across the metastore?

A. Metastore Admin, because metastore admins can transfer ownership and manage privileges across all metastore objects, including shares and recipients.
B. Workspace Admin, because workspace admins can transfer ownership of any Unity Catalog object.
C. Account Admin, because account admins can only create metastores but cannot change ownership of catalogs.
D. Catalog Owner, because catalog owners can transfer any object in any catalog in the metastore.

Answer: A

Explanation:
Metastore Admins have the highest administrative privileges within a Unity Catalog metastore.
They can transfer ownership of any Unity Catalog object, including catalogs, schemas, tables, storage credentials, and external locations. Metastore Admins are also required to manage Delta Sharing configurations such as creating or transferring shares and recipients.
Account Admins, by contrast, only create metastores and cannot change ownership or manage Delta Sharing objects. Workspace Admins have privileges limited to workspace-level management, not cross-metastore access.

NEW QUESTION # 173
A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?

A. Use the "Validate" option in the notebook to check for syntax errors.
B. Open the web terminal from the notebook and run a shell command to validate the pipeline code.
C. Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
D. Switch to a workspace file instead of a notebook to access validation and diagnostics tools.

Answer: A

Explanation:
Databricks provides a "Validate" option within the Lakeflow Declarative Pipeline development interface that checks pipeline configurations, transformations, and syntax errors before actual execution.
This feature parses and validates the pipeline logic defined in notebooks or workspace files to ensure correctness and consistency of table dependencies, DLT (Delta Live Table) syntax, and schema references.
The validation process does not process or move any data, making it ideal for testing new configurations before deployment.
Using the shell terminal (B) or workspace files (D) does not perform integrated pipeline-level validation, while reconnecting to compute clusters (C) is unrelated to syntax checks. Therefore, the verified and correct approach is A.

NEW QUESTION # 174
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. 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.
B. Add a parallel table write to the current production pipeline, updating a new sales table that varies as required from marketing table.
C. Instruct the marketing team to download results as a CSV and email them to the sales organization.
D. Use a CTAS statement to create a derivative table from the marketing table configure a production jon to propagation changes.
E. 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.

Answer: E

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 # 175
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