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

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

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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q128-Q133):

質問 # 128
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
B. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
C. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
D. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
E. Define a view against the products_per_order table and define the dashboard against this view.

正解：C

質問 # 129
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A. The connection to the external table will succeed; the string value of password will be printed in plain text.
B. The connection to the external table will fail; the string "redacted" will be printed.
C. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
D. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
E. The connection to the external table will succeed; the string "redacted" will be printed.

正解：E

解説：
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks.

質問 # 130
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
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Which command allows manual confirmation that these three requirements have been met?

A. DESCRIBE DETAIL dev.pii test
B. SHOW TBLPROPERTIES dev.pii test
C. DESCRIBE EXTENDED dev.pii test
D. DESCRIBE HISTORY dev.pii test
E. SHOW TABLES dev

正解：C

解説：
This is the correct answer because it allows manual confirmation that these three requirements have been met. The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property "contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement.

質問 # 131
A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

A. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
B. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
C. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.
D. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.

正解：A

解説：
Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs.
Conversely, streaming tables are designed for real-time data ingestion and processing, enabling event-driven analytics and low-latency use cases.
Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C, where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.

質問 # 132
A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

A. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
B. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
C. Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.
D. All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.

正解：A

質問 # 133
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