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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Designing and Implementing a Data Science Solution on Azure |
| Exam Number: | DP-100 |
| Related Certifications: | Microsoft Certified: Azure Data Scientist Associate |
| Available Languages: | Spanish, Chinese (Simplified), English, Korean, German, Japanese, French, Portuguese |
| Certificate Validity Period: | 1 year |
| Exam Duration: | 100 minutes |
| Exam Format: | Lab, Drag and Drop, Multiple Choice, Interactive Tasks, Case Study |
| Real Exam Qty: | 40-60 |
| Exam Price: | $165 USD |
| Passing Score: | 700/1000 |
| Sample Questions: | Microsoft DP-100 Sample Questions |
| Exam Way: | Online proctored exam or test center delivery through Pearson VUE. |
| Pre Condition: | Candidates should have experience with Azure services, Python programming, and machine learning frameworks such as Scikit-Learn, PyTorch, or TensorFlow. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-100/ |
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The Microsoft DP-100 Exam is designed to test the candidate's knowledge of a wide range of topics, including data ingestion and processing, machine learning models, real-time and batch data processing, monitoring and optimization solutions, as well as data storage and management solutions. Designing and Implementing a Data Science Solution on Azure certification is a rigorous test of a candidate's skills and knowledge, and passing the exam requires a deep understanding of these topics.
NEW QUESTION # 200
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 # 201
You manage an Azure Machine Learning workspace.
You must define the execution environments for your jobs and encapsulate the dependencies for your code.
You need to configure the environment from a Docker build context.
How should you complete the rode segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
NEW QUESTION # 202
You have an Azure Machine Learning workspace and a data source file ./data/cc_data.csv in the local storage.
You plan to use Azure Machine Learning Python SDK v2 to store the content of the cc.data.csv file in a data asset named cc_data_asset in the workspace.
You write code to connect to the workspace and import all required libraries.
You need to complete the remaining code to ensure it will result in the cc_data_asset that contains the data from cc_data.csv.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 203
You write code to retrieve an experiment that is run from your Azure Machine Learning workspace.
The run used the model interpretation support in Azure Machine Learning to generate and upload a model explanation.
Business managers in your organization want to see the importance of the features in the model.
You need to print out the model features and their relative importance in an output that looks similar to the following.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: from_run_id
from_run_id(workspace, experiment_name, run_id)
Create the client with factory method given a run ID.
Returns an instance of the ExplanationClient.
Parameters
Workspace Workspace An object that represents a workspace.
experiment_name str The name of an experiment.
run_id str A GUID that represents a run.
Box 2: list_model_explanations
list_model_explanations returns a dictionary of metadata for all model explanations available.
Returns
A dictionary of explanation metadata such as id, data type, explanation method, model type, and upload time, sorted by upload time Box 3: explanation Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-interpret/azureml.contrib.interpret.explanation.expl
NEW QUESTION # 204
You are implementing a machine learning model to predict stock prices.
The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools.
What should you do?
Answer: A
NEW QUESTION # 205
......
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