높은통과율Databricks-Certified-Professional-Data-Engineer최신버전시험대비공부문제시험덤프문제다운받기

참고: DumpTOP에서 Google Drive로 공유하는 무료 2025 Databricks Databricks-Certified-Professional-Data-Engineer 시험 문제집이 있습니다: https://drive.google.com/open?id=1aSLEcqHhARIndzNqgsIRFdES2l0jlOS9

DumpTOP의Databricks인증 Databricks-Certified-Professional-Data-Engineer덤프공부가이드에는Databricks인증 Databricks-Certified-Professional-Data-Engineer시험의 가장 최신 시험문제의 기출문제와 예상문제가 정리되어 있어Databricks인증 Databricks-Certified-Professional-Data-Engineer시험을 패스하는데 좋은 동반자로 되어드립니다. Databricks인증 Databricks-Certified-Professional-Data-Engineer시험에서 떨어지는 경우Databricks인증 Databricks-Certified-Professional-Data-Engineer덤프비용전액 환불신청을 할수 있기에 보장성이 있습니다.시험적중율이 떨어지는 경우 덤프를 빌려 공부한 것과 같기에 부담없이 덤프를 구매하셔도 됩니다.

Databricks Certified Professional Data Engineer Exam은 데이터 엔지니어링 작업에 Databricks를 사용하는 데이터 전문가의 지식과 기술을 테스트하도록 설계되었습니다. 시험은 데이터 수집, 데이터 변환, 데이터 저장 및 데이터 분석을 포함한 다양한 주제를 다룹니다. 시험은 또한 후보자의 데이터 사업 도구 및 서비스를 사용하여 이러한 작업을 효과적으로 수행하는 능력을 테스트합니다.

>> Databricks-Certified-Professional-Data-Engineer최신버전 시험대비 공부문제 <<

Databricks-Certified-Professional-Data-Engineer시험대비 최신 덤프문제 - Databricks-Certified-Professional-Data-Engineer시험대비 덤프자료

DumpTOP는 오래된 IT인증시험덤프를 제공해드리는 전문적인 사이트입니다. DumpTOP의 Databricks인증 Databricks-Certified-Professional-Data-Engineer덤프는 업계에서 널리 알려진 최고품질의Databricks인증 Databricks-Certified-Professional-Data-Engineer시험대비자료입니다. Databricks인증 Databricks-Certified-Professional-Data-Engineer덤프는 최신 시험문제의 시험범위를 커버하고 최신 시험문제유형을 포함하고 있어 시험패스율이 거의 100%입니다. DumpTOP의Databricks인증 Databricks-Certified-Professional-Data-Engineer덤프를 구매하시면 밝은 미래가 보입니다.

최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q85-Q90):

질문 # 85
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. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
B. Databricks has autotuned to a smaller target file size based on the overall size of data in the table
C. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations
D. Databricks has autotuned to a smaller target file size based on the amount of data in each partition

정답：C

설명：
Explanation
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 References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Optimize" section.

질문 # 86
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.
Which piece of information is critical to this decision?

A. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
B. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
C. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires Setting multiple fields in a single update.
D. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.

정답：D

설명：
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.References:
* Databricks Documentation on Delta Lake's Time Travel: Delta Lake Time Travel
* Databricks Blog on Managing Slowly Changing Dimensions in Delta Lake: Managing SCDs in Delta Lake

질문 # 87
identifies if its needs to be converted to Fahrenheit or Celcius with a one-word letter F or C?
select udf_convert(60,'C') will result in 15.5
select udf_convert(10,'F') will result in 50

A. 1.CREATE FUNCTION udf_convert(temp DOUBLE, measure STRING)
2.RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
3. ELSE (temp - 33 ) * 5/9
4. END
B. 1.CREATE USER FUNCTION udf_convert(temp DOUBLE, measure STRING)
2.RETURNS DOUBLE
3.RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
4. ELSE (temp - 33 ) * 5/9
5.END
C. 1.CREATE UDF FUNCTION udf_convert(temp DOUBLE, measure STRING)
2. RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
3. ELSE (temp - 33 ) * 5/9
4. END
D. 1. CREATE UDF FUNCTION udf_convert(temp DOUBLE, measure STRING)
2. RETURNS DOUBLE
3. RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
4. ELSE (temp - 33 ) * 5/9
5. END
E. 1.CREATE FUNCTION udf_convert(temp DOUBLE, measure STRING)
2.RETURNS DOUBLE
3.RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
4. ELSE (temp - 33 ) * 5/9
5. END
(Correct)

정답：E

설명：
Explanation
The answer is
1.CREATE FUNCTION udf_convert(temp DOUBLE, measure STRING)
2.RETURNS DOUBLE
3.RETURN CASE WHEN measure == 'F' then (temp * 9/5) + 32
4. ELSE (temp - 33 ) * 5/9
5. END

질문 # 88
The data science team has created and logged a production model using MLflow. The following code correctly imports and applies the production model to output the predictions as a new DataFrame namedpredswith the schema "customer_id LONG, predictions DOUBLE, date DATE".

The data science team would like predictions saved to a Delta Lake table with the ability to compare all predictions across time. Churn predictions will be made at most once per day.
Which code block accomplishes this task while minimizing potential compute costs?

A.
B. preds.write.format("delta").save("/preds/churn_preds")
C.
D. preds.write.mode("append").saveAsTable("churn_preds")
E.

정답：D

질문 # 89
Which of the following Auto loader structured streaming commands successfully performs a hop from the landing area into Bronze?

A. 1.spark
2..readStream
3..load(rawSalesLocation)
4..writeStream
5..option("checkpointLocation", checkpointPath).outputMode("append")
6..table("uncleanedSales")
B. 1.spark
2..readStream
3..format("cloudFiles")
4..option("cloudFiles.format","csv")
5..option("cloudFiles.schemaLocation", checkpoint_directory)
6..load("landing")
7..writeStream.option("checkpointLocation", checkpoint_directory)
8..table(raw)
(Correct)
C. 1.spark
2..read
3..load(rawSalesLocation)
4..writeStream
5..option("checkpointLocation", checkpointPath)
6..outputMode("append")
7..table("uncleanedSales")
D. 1.spark
2..readStream
3..format("csv")
4..option("cloudFiles.schemaLocation", checkpoint_directory)
5..load("landing")
6..writeStream.option("checkpointLocation", checkpoint_directory)
7..table(raw)
E. 1.spark
2..read
3..format("cloudFiles")
4..option("cloudFiles.format","csv")
5..option("cloudFiles.schemaLocation", checkpoint_directory)
6..load("landing")
7..writeStream.option("checkpointLocation", checkpoint_directory)
8..table(raw)

정답：B

설명：
Explanation
The answer is
1.spark
2..readStream
3..format("cloudFiles") # use Auto loader
4..option("cloudFiles.format","csv") # csv format files
5..option("cloudFiles.schemaLocation", checkpoint_directory)
6..load('landing')
7..writeStream.option("checkpointLocation", checkpoint_directory)
8..table(raw)
Note: if you chose the below option which is incorrect because it does not have readStream
1.spark.read.format("cloudFiles")
2..option("cloudFiles.format","csv")
3....
4...
5...
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.
A diagram of a house Description automatically generated with low confidence

질문 # 90
......

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원하는 대답 대신, 아이의 엄마가 한 톤 높아진 목소리로 물었다, 만에 하나 혈교가 존재한다면 가만히 지켜만 볼 수 없었던 것이다, DumpTOP의Databricks Databricks-Certified-Professional-Data-Engineer인증시험의 자료 메뉴에는Databricks Databricks-Certified-Professional-Data-Engineer인증시험실기와Databricks Databricks-Certified-Professional-Data-Engineer인증시험 문제집으로 나누어져 있습니다.우리 사이트에서 관련된 학습가이드를 만나보실 수 있습니다.

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DumpTOP선택으로Databricks Databricks-Certified-Professional-Data-Engineer시험을 패스하도록 도와드리겠습니다, 날따라 새로운 시스템을 많이 개발하여 고객님께 더욱 편하게 다가갈수 있는 Databricks-Certified-Professional-Data-Engineer : Databricks Certified Professional Data Engineer Exam덤프제공 사이트가 되겠습니다, DumpTOP는 엘리트한 전문가들의 끊임없는 연구와 자신만의 노하우로 Databricks Databricks-Certified-Professional-Data-Engineer덤프자료를 만들어 냄으로 여러분의 꿈을 이루어드립니다.

덤프구매후 시험에서 실패한다면 보상정책이 있나요?

2025 DumpTOP 최신 Databricks-Certified-Professional-Data-Engineer PDF 버전 시험 문제집과 Databricks-Certified-Professional-Data-Engineer 시험 문제 및 답변 무료 공유: https://drive.google.com/open?id=1aSLEcqHhARIndzNqgsIRFdES2l0jlOS9

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높은통과율Databricks-Certified-Professional-Data-Engineer최신버전시험대비공부문제시험덤프문제다운받기

Databricks-Certified-Professional-Data-Engineer시험대비 최신 덤프문제 - Databricks-Certified-Professional-Data-Engineer시험대비 덤프자료

최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q85-Q90):

100% 유효한 Databricks-Certified-Professional-Data-Engineer최신버전 시험대비 공부문제 최신덤프자료

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