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Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Professional Data Engineer Exam
Exam Number:	Databricks-Certified-Professional-Data-Engineer
Exam Price:	$200 USD
Available Languages:	English
Related Certifications:	Databricks Certified Associate Developer Databricks Certified Data Analyst Associate
Exam Duration:	90 minutes
Exam Format:	Multiple Choice, Multiple Select
Passing Score:	70%
Real Exam Qty:	60
Certificate Validity Period:	2 years
Sample Questions:	Databricks Databricks-Certified-Professional-Data-Engineer Sample Questions
Exam Way:	Online proctored exam (Pearson VUE)
Pre Condition:	Recommended: 6+ months of hands-on experience with Databricks and data engineering concepts; familiarity with Python or Scala and SQL is strongly recommended
Official Syllabus URL:	https://www.databricks.com/learn/certification/professional-data-engineer

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Databricks Certified Professional Data Engineer試験に合格することは、ビッグデータとクラウドコンピューティングのキャリアを追求している個人にとって重要な成果になる可能性があります。この認定は、候補者がApache Sparkの深い理解を持ち、それを活用してスケーラブルなビッグデータソリューションを設計および実装できることを示しています。さらに、この認定は業界の多くの企業に認められており、候補者が高収入の仕事を得る可能性を高めることができます。

Databricks-Certified-Professional-Data-Engineer試験を受けるには、候補者は最初にDatabricks認定されたアソシエイトデータアナリストおよびDataBricks認定されたアソシエイトデータエンジニア試験を完了する必要があります。これらの試験は、専門レベルの試験に合格するために必要な知識とスキルの基盤を提供します。候補者はまた、Databricksを扱う経験があり、そのさまざまな機能と機能に精通している必要があります。

Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q174-Q179):

質問 # 174
Which of the following commands results in the successful creation of a view on top of the delta stream(stream on delta table)?

A. Spark.readStream.format("delta").table("sales").createOrReplaceTempView("streaming_vw")
B. Spark.read.format("delta").stream("sales").createOrReplaceTempView("streaming_vw")
C. You can not create a view on streaming data source.
D. Spark.read.format("delta").table("sales").createOrReplaceTempView("streaming_vw")
E. Spark.read.format("delta").table("sales").trigger("stream").createOrReplaceTempView("streaming_vw")
F. Spark.read.format("delta").table("sales").mode("stream").createOrReplaceTempView("streaming_vw")

正解：A

解説：
Explanation
The answer is
Spark.readStream.table("sales").createOrReplaceTempView("streaming_vw") When you load a Delta table as a stream source and use it in a streaming query, the query processes all of the data present in the table as well as any new data that arrives after the stream is started.
You can load both paths and tables as a stream, you also have the ability to ignore deletes and changes(updates, Merge, overwrites) on the delta table.
Here is more information,
https://docs.databricks.com/delta/delta-streaming.html#delta-table-as-a-source

質問 # 175
Which of the following statements can successfully read the notebook widget and pass the python variable to a SQL statement in a Python notebook cell?

A. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = '{order_date}' ")
(Correct)
B. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql("SELECT * FROM sales WHERE orderDate = order_date")
C. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = 'f{order_date }'")
D. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = 'order_date' ")
E. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = '${order_date }' ")

正解：A

質問 # 176
A table nameduser_ltvis being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
Theuser_ltvtable has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. The email, age. and ltv columns will be returned with the values in user ltv.
B. Three columns will be returned, but one column will be named "redacted" and contain only null values.
C. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
D. Only the email and itv columns will be returned; the email column will contain all null values.
E. The email and ltv columns will be returned with the values in user itv.

正解：C

解説：
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.
Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "CASE expression" section.

質問 # 177
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

A. Unit
B. functional
C. Manual
D. Integration

正解：A

解説：
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function.
Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.
:
Software Testing Fundamentals: Unit Testing

質問 # 178
A data engineer needs to install the PyYAML Python package for YAML file processing within their Databricks environment. However, the Databricks workspace is air-gapped and does not have direct internet access to download packages from PyPI. The engineer has already downloaded the required PyYAML wheel (.whl) file onto their laptop. The data engineer wants to install the PyYAML package from the local wheel file so that it is automatically available whenever any new cluster is provisioned in their Databricks workspace. Which approach should the data engineer use?

A. Upload the PyYAML.whl file under the data engineer's user home directory in the Workspace, and create a cluster-scoped init script that executes %pip install /path/to/PyYAML.whl on the shared cluster.
B. Upload the PyYAML.whl file to a Unity Catalog volume. Add the path to the Unity Catalog allowlist if required. Then create a cluster-scoped init script that executes pip install /path/to/PyYAML.whl.
C. Set up a private PyPI repository, register the wheel there, and create a cluster-scoped init script that executes /databricks/python/bin/pip install --index-url=https://{repo-url} PyYAML on the cluster.
D. Add the PyYAML.whl file directly to Databricks Git Repos and assume that any cluster linked to the Repo will automatically have PyYAML installed from that file.

正解：B

解説：
Databricks documents that libraries and init scripts can be sourced from Unity Catalog volumes, and for standard access mode, relevant paths can require Unity Catalog allowlist configuration. Databricks also documents that init scripts run on every cluster startup, which is the mechanism that makes a package automatically available whenever a new cluster is provisioned. (Databricks Documentation) Option A fits the air-gapped requirement because it does not depend on internet access and uses a startup-time installation mechanism. Option B still depends on reachable repository infrastructure. Option C is incorrect because %pip is notebook magic, not the correct form inside an init script. Option D is unsupported because storing a wheel in Git Repos does not automatically install it on cluster creation. (Databricks Documentation)

質問 # 179
......

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