Bill Hall Bill Hall's Profile Page

Bill Hall Bill Hall

0 Course Enrolled • 0 Course Completed

Biography

Databricks-Certified-Professional-Data-Engineer考試內容 & Databricks-Certified-Professional-Data-Engineer認證資料

P.S. VCESoft在Google Drive上分享了免費的、最新的Databricks-Certified-Professional-Data-Engineer考試題庫：https://drive.google.com/open?id=1UviwYnMjoMi_6dh-bb0spI8xGOqEFGmT

你瞭解VCESoft的Databricks-Certified-Professional-Data-Engineer考試考古題嗎？為什麼用過的人都讚不絕口呢？是不是很想試一試它是否真的那麼有效果？趕快點擊VCESoft的網站去下載吧，每個問題都有提供demo，覺得好用可以立即購買。你購買了考古題以後還可以得到一年的免費更新服務，一年之內，只要你想更新你擁有的資料，那麼你就可以得到最新版。有了這個資料你就能輕鬆通過Databricks-Certified-Professional-Data-Engineer考試，獲得資格認證。

考試包括多選題和實踐練習，旨在測試候選人在使用Databricks方面的知識和技能。通過考試的候選人將獲得Databricks Certified Professional Data Engineer認證，該認證被全球雇主認可，作為候選人在使用Databricks構建和維護數據管道方面專業知識和能力的驗證。總的來說，Databricks Certified Professional Data Engineer認證考試是任何想在大數據工程和分析方面推進自己職業生涯的人的寶貴資格。

>> Databricks-Certified-Professional-Data-Engineer考試內容 <<

Databricks-Certified-Professional-Data-Engineer認證資料 & Databricks-Certified-Professional-Data-Engineer考題

VCESoft提供的Databricks Databricks-Certified-Professional-Data-Engineer 認證考試測試練習題和真實的考試題目很相似。如果你選擇了VCESoft提供的測試練習題和答案，我們會給你提供一年的免費線上更新服務。VCESoft可以100%保證你通過考試，如果你的考試未通過，我們將全額退款給你。

最新的 Databricks Certification Databricks-Certified-Professional-Data-Engineer 免費考試真題 (Q57-Q62):

問題 #57
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built-in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used.
Which strategy will yield the best performance without shuffling data?

A. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
B. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
C. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
D. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
E. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.

答案：D

問題 #58
When writing streaming data, Spark's structured stream supports the below write modes

A. Complete, Incremental, Update
B. Append, overwrite, Continuous
C. Append, Complete, Update
D. Delta, Complete, Continuous
E. Append, Delta, Complete

答案：C

解題說明：
Explanation
The answer is Append, Complete, Update
*Append mode (default) - This is the default mode, where only the new rows added to the Result Table since the last trigger will be outputted to the sink. This is supported for only those queries where rows added to the Result Table is never going to change. Hence, this mode guarantees that each row will be output only once (assuming fault-tolerant sink). For example, queries with only select, where, map, flatMap, filter, join, etc. will support Append mode.
*Complete mode - The whole Result Table will be outputted to the sink after every trigger. This is supported for aggregation queries.
*Update mode - (Available since Spark 2.1.1) Only the rows in the Result Table that were updated since the last trigger will be outputted to the sink. More information to be added in future releases.

問題 #59
A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
B. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
C. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
D. Databricks has autotuned to a smaller target file size based on the overall size of data in the table

答案：A

解題說明：
This is the correct answer because Databricks has a feature called Auto Optimize, which automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones and sorting data within each file by a specified column. However, Auto Optimize also considers the trade-off between file size and merge performance, and may choose a smaller target file size to reduce the duration of merge operations, especially for streaming workloads that frequently update existing records. Therefore, it is possible that Auto Optimize has autotuned to a smaller target file size based on the characteristics of the streaming production job. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section. https://docs.databricks.com/en/delta/tune-file-size.html#autotune-table 'Autotune file size based on workload'

問題 #60
When working with AUTO LOADER you noticed that most of the columns that were inferred as part of loading are string data types including columns that were supposed to be integers, how can we fix this?

A. Update the checkpoint location
B. Correct the incoming data by explicitly casting the data types
C. Provide the schema of the target table in the cloudfiles.schemalocation
D. Provide schema hints
E. Provide the schema of the source table in the cloudfiles.schemalocation

答案：D

解題說明：
Explanation
The answer is, Provide schema hints.
1.spark.readStream
2.format("cloudFiles")
3.option("cloudFiles.format", "csv")
4.option("header", "true")
5.option("cloudFiles.schemaLocation", schema_location)
6.option("cloudFiles.schemaHints", "id int, description string")
7.load(raw_data_location)
8.writeStream
9.option("checkpointLocation", checkpoint_location)
10.start(target_delta_table_location)option("cloudFiles.schemaHints", "id int, description string")
# Here we are providing a hint that id column is int and the description is a string When cloudfiles.schemalocation is used to store the output of the schema inference during the load process, with schema hints you can enforce data types for known columns ahead of time.

問題 #61
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 leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
B. 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.
C. 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.
D. 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.
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. Reference:
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 #62
......

有很多網站提供資訊Databricks的Databricks-Certified-Professional-Data-Engineer考試，為你提供 Databricks的Databricks-Certified-Professional-Data-Engineer考試認證和其他的培訓資料，VCESoft是唯一的網站，為你提供優質的Databricks的Databricks-Certified-Professional-Data-Engineer考試認證資料，在VCESoft指導和幫助下，你完全可以通過你的第一次Databricks的Databricks-Certified-Professional-Data-Engineer考試，我們VCESoft提供的試題及答案是由現代和充滿活力的資訊技術專家利用他們的豐富的知識和不斷積累的經驗，為你的未來在IT行業更上一層樓。

Databricks-Certified-Professional-Data-Engineer認證資料: https://www.vcesoft.com/Databricks-Certified-Professional-Data-Engineer-pdf.html

我們的線上服務是研究資料，它包含類比訓練題，和Databricks Databricks-Certified-Professional-Data-Engineer認證考試相關的考試練習題和答案，我們保證，僅僅使用我們的 Databricks Databricks-Certified-Professional-Data-Engineer 認證題庫，而不需要購買任何其他材料或參加昂貴的培訓，您就可以在第一次參加考試時便順利通過 Databricks-Certified-Professional-Data-Engineer 認證考試，如果你選擇了報名參加Databricks Databricks-Certified-Professional-Data-Engineer 認證考試，你就應該馬上選擇一份好的學習資料或培訓課程來準備考試，試試我們的免費的 Databricks-Certified-Professional-Data-Engineer考題，親身體驗一下吧，Databricks Databricks-Certified-Professional-Data-Engineer考試內容 24小時/7天全天候全時段售後客服，最開始的時候，每成功解答出一道Databricks-Certified-Professional-Data-Engineer考題都是值得高興的。

任何人對工作的變化，含義和幾乎每個人都感興趣的人都會發現這本有價值的書，果然是女大不中留啊，妳讓哥哥好傷心，我們的線上服務是研究資料，它包含類比訓練題，和Databricks Databricks-Certified-Professional-Data-Engineer認證考試相關的考試練習題和答案。

VCESoft Databricks-Certified-Professional-Data-Engineer考試內容 - 立即獲取

我們保證，僅僅使用我們的 Databricks Databricks-Certified-Professional-Data-Engineer 認證題庫，而不需要購買任何其他材料或參加昂貴的培訓，您就可以在第一次參加考試時便順利通過 Databricks-Certified-Professional-Data-Engineer 認證考試，如果你選擇了報名參加Databricks Databricks-Certified-Professional-Data-Engineer 認證考試，你就應該馬上選擇一份好的學習資料或培訓課程來準備考試。

試試我們的免費的 Databricks-Certified-Professional-Data-Engineer考題，親身體驗一下吧，24小時/7天全天候全時段售後客服。

BONUS!!! 免費下載VCESoft Databricks-Certified-Professional-Data-Engineer考試題庫的完整版：https://drive.google.com/open?id=1UviwYnMjoMi_6dh-bb0spI8xGOqEFGmT

Bill Hall Bill Hall

Biography

Resource

Company