Databricks-Certified-Data-Engineer-Professional높은통과율인기덤프문제완벽한시험대비자료

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Databricks-Certified-Data-Engineer-Professional높은 통과율 인기 덤프문제 최신 시험대비자료

KoreaDumps덤프공부가이드는 업계에서 높은 인지도를 자랑하고 있습니다. KoreaDumps제품은 업데이트가 가장 빠르고 적중율이 가장 높아 업계의 다른 IT공부자료 사이트보다 출중합니다. KoreaDumps의Databricks인증 Databricks-Certified-Data-Engineer-Professional덤프는 이해하기 쉽고 모든Databricks인증 Databricks-Certified-Data-Engineer-Professional시험유형이 모두 포함되어 있어 덤프만 잘 이해하고 공부하시면 시험패스는 문제없습니다.

최신 Databricks Certification Databricks-Certified-Data-Engineer-Professional 무료샘플문제 (Q18-Q23):

질문 # 18
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 based on the overall size of data in the table
B. Databricks has autotuned to a smaller target file size based on the amount of data in each partition
C. Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
D. Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

정답：D

설명：
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from 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.

질문 # 19
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A. Yields faster deployment and execution times
B. Ensures that all steps interact correctly to achieve the desired end result
C. Improves the quality of your data
D. Validates a complete use case of your application
E. Troubleshooting is easier since all steps are isolated and tested individually

정답：E

설명：
Unit tests are small, isolated tests that are used to check specific parts of the code, such as functions or classes.

질문 # 20
An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

A. input_dict = input()
date= input_dict["date"]
B. date = spark.conf.get("date")
C. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
D. import sys
date = sys.argv[1]
E. date = dbutils.notebooks.getParam("date")

정답：C

설명：
The code block that should be used to create the date Python variable used in the above code block is:
dbutils.widgets.text("date", "null") date = dbutils.widgets.get("date") This code block uses the dbutils.widgets API to create and get a text widget named "date" that can accept a string value as a parameter. The default value of the widget is "null", which means that if no parameter is passed, the date variable will be "null". However, if a parameter is passed through the Databricks Jobs API, the date variable will be assigned the value of the parameter.
For example, if the parameter is "2021-11-01", the date variable will be "2021-11-01". This way, the notebook can use the date variable to load data from the specified path.

질문 # 21
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
B. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
C. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
D. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
E. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

정답：D

설명：
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints. Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.

질문 # 22
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
B. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
C. Define a view against the products_per_order table and define the dashboard against this view.
D. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.
E. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.

정답：B

질문 # 23
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