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

Section	Weight	Objectives
Topic 1: Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Topic 2: CI/CD, Testing, and Deployment	~6%	- Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API
Topic 3: Developing Code for Data Processing using Python and SQL	~22%	- Implement scalable Python/SQL code and project structures - Manage dependencies, libraries, and UDFs - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader
Topic 4: Cost and Performance Optimization	~13%	- Leverage system tables and observability tools - Optimize queries, clusters, and storage
Topic 5: Security and Governance	~10%	- Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance
Topic 6: Data Modeling	~10%	- Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering
Topic 7: Monitoring, Logging, and Troubleshooting	~8%	- Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables
Topic 8: Streaming Workloads and Change Data Capture	~11%	- Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics
Topic 9: Data Transformation, Cleansing, and Quality	~12%	- Apply advanced Spark transformations - Enforce data quality and quarantine bad data

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Databricks Certified Data Engineer Professional Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q154-Q159):

154. Frage
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor
B. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
C. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
D. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
E. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor

Antwort: A

155. Frage
What is the first line of a Databricks Python notebook when viewed in a text editor?

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

Antwort: E

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

156. Frage
To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A. Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.
B. Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
C. Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
D. Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
E. Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.

Antwort: D

Begründung:
This is the correct answer because it addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed. The situation is that an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added, due to new requirements from a customer-facing application. By configuring a new table with all the requisite fields and new names and using this as the source for the customer-facing application, the data engineering team can meet the new requirements without affecting other teams that rely on the existing table schema and name. By creating a view that maintains the original data schema and table name by aliasing select fields from the new table, the data engineering team can also avoid duplicating data or creating additional tables that need to be managed.

157. Frage
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.
B. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
C. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
D. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
E. The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.

Antwort: B

Begründung:
This is the correct answer because it accurately presents information about Delta Lake and Databricks that may impact the decision-making process of a junior data engineer who is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Delta Lake and Databricks support schema inference and evolution, which means that they can automatically infer the schema of a table from the source data and allow adding new columns or changing column types without affecting existing queries or pipelines. However, schema inference and evolution may not always be desirable or reliable, especially when dealing with complex or nested data structures or when enforcing data quality and consistency across different systems. Therefore, setting types manually can provide greater assurance of data quality enforcement and avoid potential errors or conflicts due to incompatible or unexpected data types.

158. Frage
Which distribution does Databricks support for installing custom Python code packages?

A. jars
B. CRAN
C. sbt
D. nom
E. CRAM
F. Wheels

Antwort: D

Begründung:
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in-workflows

159. Frage
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