Databricks-Certified-Professional-Data-Engineer試験、Databricks-Certified-Professional-Data-Engineer無料過去問

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Databricks認定プロフェッショナルデータエンジニア認定試験は、候補者がDatabricksテクノロジーとデータエンジニアリングの概念を深く理解する必要がある挑戦的な試験です。候補者は、Apache Spark、Delta Lake、SQL、およびPythonでの仕事の経験が必要です。また、AWS、Azure、Google Cloudプラットフォームなどのクラウドベースのデータプラットフォームでの作業経験も必要です。

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どんな業界で自分に良い昇進機会があると希望する職人がとても多いと思って、IT業界にも例外ではありません。ITの専門者はDatabricksのDatabricks-Certified-Professional-Data-Engineer認定試験があなたの願望を助けって実現できるのがよく分かります。JPTestKingはあなたの夢に実現させるサイトでございます。

Databricks認定の専門的なデータエンジニア認定を取得することにより、データの専門家は、DataBricksプラットフォームを使用してデータソリューションを構築および管理するための専門知識を実証できます。この認定は、個人が自分のキャリアを前進させるのに役立ち、データ主導の目標を達成するのに役立つ資格のあるデータ専門家を特定して雇う方法を組織に提供することができます。

Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q22-Q27):

質問 # 22
Which of the below SQL commands create a Global temporary view?

A. 1. CREATE OR REPLACE GLOBAL TEMPORARY VIEW view_name
2. AS SELECT * FROM table_name
(Correct)
B. 1.CREATE OR REPLACE TEMPORARY VIEW view_name
2. AS SELECT * FROM table_name
C. 1. CREATE OR REPLACE LOCAL TEMPORARY VIEW view_name
2. AS SELECT * FROM table_name
D. 1. CREATE OR REPLACE LOCAL VIEW view_name
2. AS SELECT * FROM table_name
E. 1.CREATE OR REPLACE VIEW view_name
2. AS SELECT * FROM table_name

正解：A

解説：
Explanation
1. CREATE OR REPLACE GLOBAL TEMPORARY VIEW view_name
2. AS SELECT * FROM table_name
There are two types of temporary views that can be created Local and Global
*A session-scoped temporary view is only available with a spark session, so another note-book in the same cluster can not access it. if a notebook is detached and reattached local temporary view is lost.
*A global temporary view is available to all the notebooks in the cluster but if a cluster re-starts a global temporary view is lost.

質問 # 23
You are asked to debug a databricks job that is taking too long to run on Sunday's, what are the steps you are going to take to identify the step that is taking longer to run?

A. Under Workflow UI and jobs select job you want to monitor and select the run, notebook activity can be viewed.
B. A notebook activity of job run is only visible when using all-purpose cluster.
C. Once a job is launched, you cannot access the job's notebook activity.
D. Enable debug mode in the Jobs to see the output activity of a job, output should be available to view.
E. Use the compute's spark UI to monitor the job activity.

正解：A

解説：
Explanation
The answer is, Under Workflow UI and jobs select job you want to monitor and select the run, notebook activity can be viewed.
You have the ability to view current active runs or completed runs, once you click the run you can see the A picture containing graphical user interface Description automatically generated

Click on the run to view the notebook output
Graphical user interface, text, application, email Description automatically generated

質問 # 24
A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A. All related files and metadata are dropped and recreated in a single ACID transaction.
B. A new Delta transaction log Is created for the renamed table.
C. The table reference in the metastore is updated and all data files are moved.
D. The table reference in the metastore is updated and no data is changed.
E. The table name change is recorded in the Delta transaction log.

正解：D

解説：
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An externaltable can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.

質問 # 25
A data engineer has created a new cluster using shared access mode with default configurations. The data engineer needs to allow the development team access to view the driver logs if needed.
What are the minimal cluster permissions that allow the development team to accomplish this?

A. CAN VIEW
B. CAN MANAGE
C. CAN RESTART
D. CAN ATTACH TO

正解：A

解説：
Databricks provides different permission levels to control access to clusters. The correct minimal permission required for viewing driver logs is CAN VIEW.
Databricks Cluster Permission Levels:
CAN ATTACH TO:
Allows users to attach notebooks to a cluster but does not allow them to view logs.
Not sufficient for viewing driver logs.
CAN MANAGE:
Grants full control over the cluster, including starting, stopping, and editing configurations.
Too broad for this requirement.
CAN VIEW (Correct Answer):
Allows users to view cluster details, logs, and status but not modify any configurations.
Minimal required permission for viewing logs.
CAN RESTART:
Grants permission to restart the cluster, but does not include log access.
Not sufficient for viewing logs.
Conclusion:
The minimal permission needed to allow the development team to view driver logs is CAN VIEW.
Reference:
Databricks Cluster Permissions Documentation

質問 # 26
When defining external tables using formats CSV, JSON, TEXT, BINARY any query on the exter-nal tables caches the data and location for performance reasons, so within a given spark session any new files that may have arrived will not be available after the initial query. How can we address this limitation?

A. UNCACHE TABLE table_name
B. CACHE TABLE table_name
C. CLEAR CACH table_name
D. REFRESH TABLE table_name
E. BROADCAST TABLE table_name

正解：D

解説：
Explanation
The answer is REFRESH TABLE table_name
REFRESH TABLE table_name will force Spark to refresh the availability of external files and any changes.
When spark queries an external table it caches the files associated with it, so that way if the table is queried again it can use the cached files so it does not have to retrieve them again from cloud object storage, but the drawback here is that if new files are available Spark does not know until the Refresh command is ran.

質問 # 27
......

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Databricks-Certified-Professional-Data-Engineer試験、Databricks-Certified-Professional-Data-Engineer無料過去問

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Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q22-Q27):

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