Madison Lee Madison Lee
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Databricks-Certified-Professional-Data-Engineer높은통과율인기덤프, Databricks-Certified-Professional-Data-Engineer합격보장가능덤프공부
다른 방식으로 같은 목적을 이룰 수 있다는 점 아세요? 여러분께서는 어떤 방식, 어느 길을 선택하시겠습니까? 많은 분들은Databricks인증Databricks-Certified-Professional-Data-Engineer시험패스로 자기 일에서 생활에서 한층 업그레이드 되기를 바랍니다. 하지만 모두 다 알고계시는그대로Databricks인증Databricks-Certified-Professional-Data-Engineer시험은 간단하게 패스할 수 있는 시험이 아닙니다. 많은 분들이Databricks인증Databricks-Certified-Professional-Data-Engineer시험을 위하여 많은 시간과 정신력을 투자하고 있습니다. 하지만 성공하는 분들은 적습니다.
Databricks Certified Professional Data Engineer 자격증은 데이터 엔지니어링 분야에 전문화하고 Databricks 기술에 대한 전문 지식을 증명하고자하는 개인에게 완벽한 선택입니다. 또한 Databricks를 사용하는 회사에게는 대량의 데이터를 효과적으로 관리하고 분석하기위한 필요한 기술을 보유한 직원을 보장하고자하는 가치있는 자격증입니다.
Databricks 인증된 전문 데이터 엔지니어 자격증은 Databricks를 사용하여 데이터 엔지니어링 기술과 전문성을 인증하려는 데이터 엔지니어들에게 유용한 자격증입니다. 이 인증은 후보자가 Databricks를 사용하여 데이터 솔루션을 설계하고 구현하는 데 필요한 지식과 기술을 보유하고 있다는 것을 고용주와 고객에게 입증합니다. 또한 데이터 엔지니어들이 경쟁이 심한 취업 시장에서 차별화되고 진로 발전 기회를 제공합니다.
>> Databricks-Certified-Professional-Data-Engineer높은 통과율 인기덤프 <<
시험준비에 가장 좋은 Databricks-Certified-Professional-Data-Engineer높은 통과율 인기덤프 최신버전 덤프데모문제 다운로드
Databricks-Certified-Professional-Data-Engineer인증시험은 IT업계에 종사하고 계신 분이시라면 최근 많은 인기를 누리고 있다는 것을 알고 계실것입니다. Databricks-Certified-Professional-Data-Engineer인증시험을 패스하여 자격증을 취득하는데 가장 쉬운 방법은 Pass4Test에서 제공해드리는 Databricks-Certified-Professional-Data-Engineer덤프를 공부하는 것입니다. Databricks Databricks-Certified-Professional-Data-Engineer덤프에 있는 문제와 답만 기억하시면 Databricks-Certified-Professional-Data-Engineer시험을 패스하는데 많은 도움이 됩니다.덤프구매후 최신버전으로 업데이트되면 업데이트버전을 시스템 자동으로 구매시 사용한 메일주소로 발송해드려 덤프유효기간을 최대한 길게 연장해드립니다.
최신 Databricks Certification Databricks-Certified-Professional-Data-Engineer 무료샘플문제 (Q77-Q82):
질문 # 77
The current ELT pipeline is receiving data from the operations team once a day so you had setup an AUTO LOADER process to run once a day using trigger (Once = True) and scheduled a job to run once a day, operations team recently rolled out a new feature that allows them to send data every 1 min, what changes do you need to make to AUTO LOADER to process the data every 1 min.
- A. Change AUTO LOADER trigger to .trigger(ProcessingTime = "1 minute")
- B. Change AUTO LOADER trigger to ("1 minute")
- C. Convert AUTO LOADER to structured streaming
- D. Setup a job cluster run the notebook once a minute
- E. Enable stream processing
정답:A
질문 # 78
A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in thegeo_lookuptable.
Before executing the code, runningSHOWTABLESon the current database indicates the database contains only two tables:geo_lookupandsales.
Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?
- A. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af:
if this entity exists, Cmd 2 will succeed. - B. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
- C. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
- D. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
- E. Both commands will fail. No new variables, tables, or views will be created.
정답:D
설명:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view. Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter. Verified References: [Databricks Certified Data Engineer Professional], under
"Language Interoperability" section; Databricks Documentation, under "Mix languages" section.
질문 # 79
A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?
- A. Post_time
- B. User_id
- C. Date
- D. Post_id
정답:C
설명:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, thedatecolumn is a good candidate for partitioning for several reasons:
* Time-Based Queries: If queries frequently filter or group by date, partitioning by thedatecolumn can significantly improve performance by limiting the amount of data scanned.
* Granularity: Thedatecolumn likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
* Data Skew: Other columns likepost_idoruser_idmight lead to uneven partition sizes (data skew), which can negatively impact performance.
Partitioning bypost_timecould also be considered, but typicallydateis preferred due to its more manageable granularity.
References:
* Delta Lake Documentation on Table Partitioning: Optimizing Layout with Partitioning
질문 # 80
A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above
format regardless of whether a table already exists with this name?
- A. 1. CREATE OR REPLACE TABLE table_name ( id STRING, birthDate DATE, avgRating FLOAT )
- B. 1. CREATE TABLE table_name AS
2. SELECT id STRING, birthDate DATE, avgRating FLOAT - C. 1. CREATE OR REPLACE TABLE table_name
2. WITH COLUMNS ( id STRING, birthDate DATE, avgRating FLOAT ) USING DELTA - D. 1. CREATE TABLE IF NOT EXISTS table_name ( id STRING, birthDate DATE, avgRating FLOAT )
- E. 1. CREATE OR REPLACE TABLE table_name AS
2. SELECT id STRING, birthDate DATE, avgRating FLOAT USING DELTA
정답:A
질문 # 81
A data engineer is performing a join operating to combine values from a static userlookup table with a streaming DataFrame streamingDF.
Which code block attempts to perform an invalid stream-static join?
- A. streamingDF.join(userLookup, ["userid"], how="inner")
- B. streamingDF.join(userLookup, ["user_id"], how="outer")
- C. userLookup.join(streamingDF, ["user_id"], how="right")
- D. userLookup.join(streamingDF, ["userid"], how="inner")
- E. streamingDF.join(userLookup, ["user_id"], how="left")
정답:C
설명:
In Spark Structured Streaming, certain types of joins between a static DataFrame and a streaming DataFrame are not supported. Specifically, a right outer join where the static DataFrame is on the left side and the streaming DataFrame is on the right side is not valid. This is because Spark Structured Streaming cannot handle scenarios where it has to wait for new rows to arrive in the streaming DataFrame to match rows in the static DataFrame. The other join types listed (inner, left, and full outer joins) are supported in streaming-static DataFrame joins.
Reference:
Structured Streaming Programming Guide: Join Operations
Databricks Documentation on Stream-Static Joins: Databricks Stream-Static Joins
질문 # 82
......
우리 Pass4Test에서는 여러분을 위하여 정확하고 우수한 서비스를 제공하였습니다. 여러분의 고민도 덜어드릴 수 있습니다. 빨리 성공하고 빨리Databricks Databricks-Certified-Professional-Data-Engineer인증시험을 패스하고 싶으시다면 우리 Pass4Test를 장바구니에 넣으시죠 . Pass4Test는 여러분의 아주 좋은 합습가이드가 될것입니다. Pass4Test로 여러분은 같고 싶은 인증서를 빠른시일내에 얻게될것입니다.
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