實用Databricks-Certified-Professional-Data-Engineer權威考題和資格考試中的領先材料提供者＆頂尖的Databricks Databricks Certified Professional Data Engineer Exam

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

Section	Objectives
Security, Governance, Monitoring, and Optimization	- Monitor and optimize Spark workloads - Cost optimization and performance tuning - Implement Unity Catalog governance and access control
Data Modeling and Storage	- Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies - Delta Lake table design and optimization
Production Pipelines and Orchestration	- Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs - Automate ETL pipelines and scheduling
Data Ingestion and Transformation	- Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks - Handle batch and streaming data pipelines

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最新的 Databricks Certification Databricks-Certified-Professional-Data-Engineer 免費考試真題 (Q145-Q150):

問題 #145
What steps need to be taken to set up a DELTA LIVE PIPELINE as a job using the workspace UI?

A. DELTA LIVE TABLES do not support job cluster
B. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook
C. Use Pipeline creation UI, select a new pipeline and job cluster
D. Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the pipeline JSON file

答案：B

解題說明：
Explanation
The answer is,
Select Workflows UI and Delta live tables tab, under task type select Delta live tables pipeline and select the notebook.
Create a pipeline
To create a new pipeline using the Delta Live Tables notebook:
1.Click Workflows in the sidebar, click the Delta Live Tables tab, and click Create Pipeline.
2.Give the pipeline a name and click to select a notebook.
3.Optionally enter a storage location for output data from the pipeline. The system uses a de-fault location if you leave Storage Location empty.
4.Select Triggered for Pipeline Mode.
5.Click Create.
The system displays the Pipeline Details page after you click Create. You can also access your pipeline by clicking the pipeline name in the Delta Live Tables tab.

問題 #146
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source table has been de- duplicated and validated, which statement describes what will occur when this code is executed?

A. An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.
B. The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
C. A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
D. An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
E. The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.

答案：E

解題說明：
This code is using the pyspark.sql.functions library to group the silver_customer_sales table by customer_id and then aggregate the data using the minimum sale date, maximum sale total, and sum of distinct order ids.
The resulting aggregated data is then written to the gold_customer_lifetime_sales_summary table, overwriting any existing data in that table. This is a batch job that does not use any incremental or streaming logic, and does not perform any merge or update operations. Therefore, the code will overwrite the gold table with the aggregated values from the silver table every time it is executed. References:
* https://docs.databricks.com/spark/latest/dataframes-datasets/introduction-to-dataframes-python.html
* https://docs.databricks.com/spark/latest/dataframes-datasets/transforming-data-with-dataframes.html
* https://docs.databricks.com/spark/latest/dataframes-datasets/aggregating-data-with-dataframes.html

問題 #147
Which is a key benefit of an end-to-end test?

A. It closely simulates real world usage of your application.
B. It provides testing coverage for all code paths and branches.
C. It makes it easier to automate your test suite
D. It pinpoint errors in the building blocks of your application.

答案：A

解題說明：
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real-world, user behavior, ensuring that the system as a whole operates correctly.
Reference:
Software Testing: End-to-End Testing

問題 #148
You have written a notebook to generate a summary data set for reporting, Notebook was scheduled using the job cluster, but you realized it takes 8 minutes to start the cluster, what feature can be used to start the cluster in a timely fashion so your job can run immediatley?

A. Disable auto termination so the cluster is always running
B. Use Databricks Premium edition instead of Databricks standard edition
C. Setup an additional job to run ahead of the actual job so the cluster is running second job starts
D. Use the Databricks cluster pools feature to reduce the startup time
E. Pin the cluster in the cluster UI page so it is always available to the jobs

答案：D

解題說明：
Explanation
Cluster pools allow us to reserve VM's ahead of time, when a new job cluster is created VM are grabbed from the pool. Note: when the VM's are waiting to be used by the cluster only cost incurred is Azure. Databricks run time cost is only billed once VM is allocated to a cluster.
Here is a demo of how to setup a pool and follow some best practices,
Graphical user interface, text Description automatically generated

問題 #149
Which of the following data workloads will utilize a silver table as its source?

A. A job that cleans data by removing malformatted records
B. A job that queries aggregated data that already feeds into a dashboard
C. A job that aggregates cleaned data to create standard summary statistics
D. A job that enriches data by parsing its timestamps into a human-readable format
E. A job that ingests raw data from a streaming source into the Lakehouse

答案：C

解題說明：
Explanation
The answer is, A job that aggregates cleaned data to create standard summary statistics Silver zone maintains the grain of the original data, in this scenario a job is taking data from the silver zone as the source and aggregating and storing them in the gold zone.
Medallion Architecture - Databricks
Silver Layer:
1. Reduces data storage complexity, latency, and redundency
2. Optimizes ETL throughput and analytic query performance
3. Preserves grain of original data (without aggregation)
4. Eliminates duplicate records
5. production schema enforced
6. Data quality checks, quarantine corrupt data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
Purpose of each layer in medallion architecture

問題 #150
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Databricks-Certified-Professional-Data-Engineer熱門考古題: https://www.vcesoft.com/Databricks-Certified-Professional-Data-Engineer-pdf.html

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