最優質的Databricks-Certified-Professional-Data-Engineer考試心得 & Databricks Databricks-Certified-Professional-Data-Engineer認證資料：Databricks Certified Professional Data Engineer Exam通過認證

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最新的 Databricks Certification Databricks-Certified-Professional-Data-Engineer 免費考試真題 (Q56-Q61):

問題 #56
You have configured AUTO LOADER to process incoming IOT data from cloud object storage every 15 mins, recently a change was made to the notebook code to update the processing logic but the team later realized that the notebook was failing for the last 24 hours, what steps team needs to take to reprocess the data that was not loaded after the notebook was corrected?

A. Autoloader automatically re-processes data that was not loaded
B. Manually re-load the data
C. Enable back_fill = TRUE to reprocess the data
D. Delete the checkpoint folder and run the autoloader again
E. Move the files that were not processed to another location and manually copy the files into the ingestion path to reprocess them

答案：A

解題說明：
Explanation
The answer is,
Autoloader automatically re-processes data that was not loaded using the checkpoint.

問題 #57
Which statement describes Delta Lake optimized writes?

A. Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
B. A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
C. An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.
D. Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.

答案：B

解題說明：
Delta Lake optimized writes involve a shuffle operation before writing out data to the Delta table. The shuffle operation groups data by partition keys, which can lead to a reduction in the number of output files and potentially larger files, instead of multiple smaller files. This approach can significantly reduce the total number of files in the table, improve read performance by reducing the metadata overhead, and optimize the table storage layout, especially for workloads with many small files.
References:
* Databricks documentation on Delta Lake performance tuning: https://docs.databricks.com/delta
/optimizations/auto-optimize.html

問題 #58
What is the purpose of a gold layer in Multi-hop architecture?

A. Powers ML applications, reporting, dashboards and adhoc reports.
B. Preserves grain of original data, without any aggregations
C. Optimizes ETL throughput and analytic query performance
D. Data quality checks and schema enforcement
E. Eliminate duplicate records

答案：A

解題說明：
Explanation
The answer is Powers ML applications, reporting, dashboards and adhoc reports.
Review the below link for more info,
Medallion Architecture - Databricks
Gold Layer:
1.Powers Ml applications, reporting, dashboards, ad hoc analytics
2.Refined views of data, typically with aggregations
3.Reduces strain on production systems
4.Optimizes query performance for business-critical 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.

問題 #59
What is the output of below function when executed with input parameters 1, 3 :
1.def check_input(x,y):
2. if x < y:
3. x= x+1
4. if x>y:
5. x= x+1
6. if x <y:
7. x = x+1
8. return x

A. 0
B. 1
C. 2
D. 3
E. 4

答案：E

問題 #60
Your team member is trying to set up a delta pipeline and build a second gold table to the same pipeline with aggregated metrics based on an existing Delta Live table called sales_orders_cleaned but he is facing a problem in starting the pipeline, the pipeline is failing to state it cannot find the table sales_orders_cleaned, you are asked to identify and fix the problem.
1.CREATE LIVE TABLE sales_order_in_chicago
2.AS
3.SELECT order_date, city, sum(price) as sales,
4.FROM sales_orders_cleaned
5.WHERE city = 'Chicago')
6.GROUP BY order_date, city

A. Delta live tables pipeline can only have one table
B. The pipeline needs to be deployed so the first table is created before we add a second table
C. Delta live table can be used in a group by clause
D. Use STREAMING LIVE instead of LIVE table
E. Sales_orders_cleaned table is missing schema name LIVE

答案：E

解題說明：
Explanation
The answer is, Sales_orders_cleaned table is missing schema name LIVE
Every Delta live table should have schema LIVE
Here is the correct syntax,
1.CREATE LIVE TABLE sales_order_in_chicago
2.AS
3.SELECT order_date, city, sum(price) as sales,
4.FROM LIVE.sales_orders_cleaned
5.WHERE city = 'Chicago')
6.GROUP BY order_date, city

問題 #61
......

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