Databricks Certification Associate-Developer-Apache-Spark-3.5 Dumps & Associate-Developer-Apache-Spark-3.5 Test Dumps Demo - New Associate-Developer-Apache-Spark-3.5 Test Forum - Cads-Group

  • Exam Number/Code : Associate-Developer-Apache-Spark-3.5
  • Exam Name : Databricks Certified Associate Developer for Apache Spark 3.5 - Python
  • Questions and Answers : 260 Q&As
  • Price: $ 99.00 $ 39.00

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NEW QUESTION: 1
HOTSPOT
A company uses Azure for several virtual machine (VM) and website workloads. The company plans to assign administrative roles to a specific group of users. You have a resource group named GROUP1 and a virtual machine named VM2.
The users have the following responsibilities:

You need to assign the appropriate level of privileges to each of the administrators by using the principle of least privilege.
What should you do? To answer, select the appropriate target objects and permission levels in the answer area.

Answer:
Explanation:

* Owner can manage everything, including access.
* Contributors can manage everything except access.
Note: Azure role-based access control allows you to grant appropriate access to Azure AD users, groups, and services, by assigning roles to them on a subscription or resource group or individual resource level.
Reference: Role-based access control in the Microsoft Azure portal
http://azure.microsoft.com/en-us/documentation/articles/role-based-access-control-configure/

NEW QUESTION: 2
Which statements best describe how architecture principles are used within the Oracle Reference Architecture (ORA)?
A. The architecture principles for Oracle products are identified whenever an Oracle product incorporated into the architecture.
B. Architecture principles are rules that must be followed in order to comply with the documented architecture.
C. Architecture principles provide recommendations (based on industry best practices) that should be followed.
D. ORA uses multiple architectural views where each view has its own architecture principles.
E. ORA documents describe the architectural principles upon which the architecture is based.
Answer: E
Explanation:
Explanation/Reference:
The purpose of ORA is to provide a reference architecture for designing, building, and integrating solutions based on modern technology from Oracle and other vendors. The reference architecture offers architecture principles and guidance based on recommendations from Oracle product development architects and field experts.
Information provided by ORA gives architects an understanding of how to design solutions for the Oracle environment and best leverage its capabilities.
Note: Oracle Reference Architecture (ORA) defines a detailed and consistent architecture for developing and integrating solutions based on Oracle technologies. The reference architecture offers architecture principles and guidance based on recommendations from technical experts across Oracle. It covers a broad spectrum of concerns pertaining to technology architecture, including middleware, database, hardware, processes, and services.
Reference: IT Strategies from Oracle, An Overview, Release 3.0

NEW QUESTION: 3
When an administrator creates a new access policy in the Visual Policy Editor, which three options are included by default? (Choose three.)
A. A fallback option
B. An empty Resource Assign item
C. An Allow Ad box
D. A Deny End box
E. A Start box
F. A Block All option
Answer: A,D,E

NEW QUESTION: 4
次の形式のsalesDataという名前のPythonデータフレームがあります。
データフレームは、次のように長いデータ形式にピボット解除する必要があります。
変換を実行するには、Pythonでpandas.melt()関数を使用する必要があります。
コードセグメントをどのように完了する必要がありますか?回答するには、回答領域で適切なオプションを選択します。
注:正しい選択はそれぞれ1ポイントの価値があります。

Answer:
Explanation:

Explanation

Box 1: dataFrame
Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)[source] Where frame is a DataFrame Box 2: shop Paramter id_vars id_vars : tuple, list, or ndarray, optional Column(s) to use as identifier variables.
Box 3: ['2017','2018']
value_vars : tuple, list, or ndarray, optional
Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.
Example:
df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},
'B': {0: 1, 1: 3, 2: 5},
'C': {0: 2, 1: 4, 2: 6}})
pd.melt(df, id_vars=['A'], value_vars=['B', 'C'])
A variable value
0 a B 1
1 b B 3
2 c B 5
3 a C 2
4 b C 4
5 c C 6
References:
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html

 

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Databricks Certification Associate-Developer-Apache-Spark-3.5 Dumps & Associate-Developer-Apache-Spark-3.5 Test Dumps Demo - New Associate-Developer-Apache-Spark-3.5 Test Forum - Cads-Group


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