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Databricks-Certified-Data-Engineer-Professional인기자격증 시험덤프공부 덤프로 시험패스 가능

Databricks인증Databricks-Certified-Data-Engineer-Professional시험은 국제적으로 승인해주는 IT인증시험의 한과목입니다. 근 몇년간 IT인사들에게 최고의 인기를 누리고 있는 과목으로서 그 난이도 또한 높습니다. 자격증을 취득하여 직장에서 혹은 IT업계에서 자시만의 위치를 찾으련다면 자격증 취득이 필수입니다. Databricks인증Databricks-Certified-Data-Engineer-Professional시험을 패스하고 싶은 분들은Pass4Test제품으로 가보세요.

최신 Databricks Certification Databricks-Certified-Data-Engineer-Professional 무료샘플문제 (Q95-Q100):

질문 # 95
Which statement describes integration testing?

A. Validates an application use case
B. Requires manual intervention
C. Validates behavior of individual elements of your application
D. Requires an automated testing framework
E. Validates interactions between subsystems of your application

정답：E

설명：
Integration testing is a type of software testing where components of the software are gradually integrated and then tested as a unified group.
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질문 # 96
The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?

A. Can manage
B. Can run
C. Can Read
D. Can edit

정답：C

설명：
Granting a user 'Can Read' permissions on a notebook within Databricks allows them to view the notebook's content without the ability to execute or edit it. This level of permission ensures that the new team member can review the production logic for learning or auditing purposes without the risk of altering the notebook's code or affecting production data and workflows. This approach aligns with best practices for maintaining security and integrity in production environments, where strict access controls are essential to prevent unintended modifications.

질문 # 97
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. dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
B. import sys
date = sys.argv[1]
C. date = spark.conf.get("date")
D. date = dbutils.notebooks.getParam("date")
E. input_dict = input()
date= input_dict["date"]

정답：A

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 # 98
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?

A. return spark.read.option("readChangeFeed", "true").table ("bronze")
B. def new_records():
C.
D. return spark.readStream.load("bronze")
E. return spark.readStream.table("bronze")

정답：C

설명：
https://docs.databricks.com/en/delta/delta-change-data-feed.html
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질문 # 99
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

A. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
B. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
C. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
D. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
E. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.

정답：C

설명：
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability.

질문 # 100
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