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This skill set brings multiple benefits to you. You get well-paid jobs and promotions because firms prefer Designing and Implementing a Data Science Solution on Azure DP-100 certification holders. Although all professionals desire to earn certifications, many never find enough time to go beyond their graduation degree. Any area of accreditation is in high demand, and if you have a Designing and Implementing a Data Science Solution on Azure DP-100 Certification, you will grow in the information technology industry with ease.

Microsoft DP-100 Certification Exam is a challenging and comprehensive test that requires a deep understanding of data science concepts and Azure data services. By passing DP-100 exam, candidates can demonstrate their expertise in designing and implementing data science solutions on Azure and enhance their career prospects in the field. The DP-100 certification is a valuable asset for data professionals, data scientists, and developers who want to stay up-to-date with the latest trends and technologies in the data science industry.

3. Models Management and Optimization (20-25%):

Usage of model explainers for the interpretation of models: The learners have to demonstrate their competence in choosing model interpreters and generating the features of important data.
Models management: This objective focuses on trained model registration and monitoring of data drift and model usage.
Usage of Automated Machine Learning for the creation of optimal models: This section requires your skills in retrieving the best models and getting data for Automated ML runs. It also covers competence in defining primary metrics, selecting pre-processing alternatives, and determining the algorithms to be searched. The candidates should be able to use the Automated Machine Learning from Azure ML SDK as well as Automated ML interface within Azure ML studios.
Usage of hyperdrive for the tuning of hyperparameters: This domain will evaluate the ability of the applicants to define search space, primary metrics, and early termination alternatives. It also expects their skills in sampling techniques selection and model discovery that require optimal hyper-parameter values.

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By passing the DP-100 Certification Exam, data scientists and data engineers can demonstrate their expertise in designing and implementing data science solutions on Azure. Designing and Implementing a Data Science Solution on Azure certification can help candidates stand out in a competitive job market and increase their earning potential. It can also provide a foundation for further Microsoft certifications, such as the AI-100 (Designing and Implementing an Azure AI Solution) certification.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q460-Q465):

NEW QUESTION # 460
You manage an Azure Machine Learning workspace named workspacel by using the Python SDK v2.
You must register datastores in workspacel for Azure Blob and Azure Data Lake Gen2 storage to meet the following requirements:
* Data scientists accessing the datastore must have the same level of access.
* Access must be restricted to specified containers or folders.
You need to configure a security access method used to register the Azure Blob and Azure Data lake Gen?
storage in workspacel. Which security access method should you configure? To answer, select the appropriate options in the answcf area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

NEW QUESTION # 461
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You are using Azure Machine Learning Studio to perform feature engineering on a dataset.
You need to normalize values to produce a feature column grouped into bins.
Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.
Does the solution meet the goal?

A. Yes
B. No

Answer: A

Explanation:
Explanation/Reference:
Explanation:
Entropy MDL binning mode: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named <colname>quantized.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins

NEW QUESTION # 462
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 # 463
You are analyzing a dataset containing historical data from a local taxi company. You are developing a regression model.
You must predict the fare of a taxi trip.
You need to select performance metrics to correctly evaluate the regression model.
Which two metrics can you use? Each correct answer presents a complete solution?
NOTE: Each correct selection is worth one point.

A. a Root Mean Square Error value that is high
B. an F1 score that is low
C. a Root Mean Square Error value that is low
D. an R-Squared value close to 1
E. an F1 score that is high
F. an R-Squared value close to 0

Answer: C,D

Explanation:
RMSE and R2 are both metrics for regression models.
A: Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.
D: Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit.
However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.
Incorrect Answers:
C, E: F-score is used for classification models, not for regression models.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate- model

NEW QUESTION # 464
You have an Azure Machine Learning workspace named Workspace 1 Workspace! has a registered Mlflow model named model 1 with PyFunc flavor You plan to deploy model1 to an online endpoint named endpointl without egress connectivity by using Azure Machine learning Python SDK vl You have the following code:

You need to add a parameter to the ManagedOnllneDeployment object to ensure the model deploys successfully Solution: Add the with_package parameter.
Does the solution meet the goal?

A. No
B. Yes

Answer: A

NEW QUESTION # 465
......

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Evan Page Evan Page

Biography

Top Exam DP-100 Simulator 100% Pass | High-quality DP-100: Designing and Implementing a Data Science Solution on Azure 100% Pass

3. Models Management and Optimization (20-25%):

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q460-Q465):

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