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Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure exam is a valuable certification for individuals who want to showcase their expertise in data science and Azure technologies. DP-100 exam evaluates the candidate's ability to design and implement data solutions using Azure cloud technologies and is beneficial for individuals who want to pursue a career in data science.

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These are following steps for registering the DP-100 exam.Step 1: Visit to Microsoft Exam RegistrationStep 2: Signup/Login to MICROSOFT accountStep 3: Search for MICROSOFT DP-100 Certifications ExamStep 4: Select Date and Center of examination and confirm with payment value of $165

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Three in Demand Microsoft DP-100 Exam Questions Formats

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The DP-100 Exam is designed to test candidates' knowledge and skills in various areas related to data science, such as data exploration and preparation, modeling, feature engineering, and machine learning. To pass the exam, candidates must demonstrate their ability to design and implement data science solutions using Azure services such as Azure Machine Learning, Azure Databricks, and Azure HDInsight, among others.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q518-Q523):

NEW QUESTION # 518
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.explanation_client.explanationclient?view=azure-ml-py


NEW QUESTION # 519
You are creating an experiment by using Azure Machine Learning Studio.
You must divide the data into four subsets for evaluation. There is a high degree of missing values in the dat a. You must prepare the data for analysis.
You need to select appropriate methods for producing the experiment.
Which three modules should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Answer:

Explanation:

1 - Import Data
2 - Clearn Missing Data
3 - Partition and Sample
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data


NEW QUESTION # 520
You previously deployed a model that was trained using a tabular dataset named training-dataset, which is based on a folder of CSV files.
Over time, you have collected the features and predicted labels generated by the model in a folder containing a CSV file for each month. You have created two tabular datasets based on the folder containing the inference data: one named predictions-dataset with a schema that matches the training data exactly, including the predicted label; and another named features-dataset with a schema containing all of the feature columns and a timestamp column based on the filename, which includes the day, month, and year.
You need to create a data drift monitor to identify any changing trends in the feature data since the model was trained. To accomplish this, you must define the required datasets for the data drift monitor.
Which datasets should you use to configure the data drift monitor? To answer, drag the appropriate datasets to the correct data drift monitor options. Each source may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: training-dataset
Baseline dataset - usually the training dataset for a model.
Box 2: predictions-dataset
Target dataset - usually model input data - is compared over time to your baseline dataset. This comparison means that your target dataset must have a timestamp column specified.
The monitor will compare the baseline and target datasets.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets


NEW QUESTION # 521
You manage an Azure Machine Learning workspace named workspace1by using the Python SDK v2.
You must register datastores in workspace 1 for Azure Blot storage and Azure Fetes storage to meet the following requirements.
* Azure Active Directory (Azure AD) authentication must be used for access to storage when possible.
* Credentials and secrets steed in workspace1 must be valid lot a specified time period when accessing Azure Files storage.
You need to configure a security access method used to register the Azure Blob and azure files storage in workspace1.
Which security access method should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 522
You are with a time series dataset in Azure Machine Learning Studio.
You need to split your dataset into training and testing subsets by using the Split Data module.
Which splitting mode should you use?

Answer: C

Explanation:
Split Rows: Use this option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50.
Incorrect Answers:
B: Regular Expression Split: Choose this option when you want to divide your dataset by testing a single column for a value.
C: Relative Expression Split: Use this option whenever you want to apply a condition to a number column.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data


NEW QUESTION # 523
......

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