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Earning the DP-100 certification can help you advance your career in data science and machine learning. It demonstrates to employers that you have the knowledge and skills to design and implement data-driven solutions using Azure services. Additionally, Microsoft offers a variety of other Azure certifications that can complement the DP-100 and help you build a well-rounded skillset in cloud computing.

Knowing Associated Certification

The Microsoft DP-100 test is associated with the Microsoft Certified: Azure Data Scientist Associate certificate. It is a recently launched certification by Microsoft trying to impart the knowledge of concepts related to machine learning techniques. As a rule, earners are known to have industry-standard expertise related to evaluation and deployment models for building ML solutions. Apart from grating this noteworthy designation, the Microsoft DP-100 Exam will also help the test-taker to gain some ACE college credits.

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Microsoft DP-100 Exam is a certification exam that validates the skills of individuals in designing and implementing data science solutions on Azure. It is a particularly valuable certification for professionals in the field of data science and machine learning who are interested in utilizing the Azure platform to develop scalable and efficient data solutions. Designing and Implementing a Data Science Solution on Azure certification exam is designed to test the candidate's ability to design and implement data science solutions using Azure technologies, including Azure Machine Learning, Azure Databricks, and Azure Cognitive Services.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q181-Q186):

NEW QUESTION # 181
You are preparing to use the Azure ML SDK to run an experiment and need to create compute. You run the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: No
If a training cluster already exists it will be used.
Box 2: Yes
The wait_for_completion method waits for the current provisioning operation to finish on the cluster.
Box 3: Yes
Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted.
Box 4: No
Need to use training_compute.delete() to deprovision and delete the AmlCompute target.
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget


NEW QUESTION # 182
You use Data Science Virtual Machines (DSVMs) for Windows and Linux in Azure.
You need to access the DSVMs.
Which utilities should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 183
Hotspot Question
You have a Python data frame named salesData in the following format:

The data frame must be unpivoted to a long data format as follows:

You need to use the pandas.melt() function in Python to perform the transformation.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

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


NEW QUESTION # 184
You manage an Azure Machine Learning workspace. The development environment for managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks.
A Synapse Spark Compute is currently attached and uses system-assigned identity.
You need to use Python code to update the Synapse Spark Compute to use a user-assigned identity.
Solution: Initialize the DefaultAzureCredential class.
Does the solution meet the goal?

Answer: B


NEW QUESTION # 185
You have the following code. The code prepares an experiment to run a script:

The experiment must be run on local computer using the default environment.
You need to add code to start the experiment and run the script.
Which code segment should you use?

Answer: B

Explanation:
Explanation
The experiment class submit method submits an experiment and return the active created run.
Syntax: submit(config, tags=None, **kwargs)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment


NEW QUESTION # 186
......

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