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The candidates who want to take the Microsoft DP-100 exam are expected to have competence in its objectives. This means that they need to understand the scope of topics covered in this certification test. They are as follows:

1. Azure ML Workspace Set-Up (30-35%):

Experiment compute contexts management: The test takers must be able to create compute instances and compute targets for training and experiments. They have to know how to establish suitable compute specifications for training workloads.
Azure ML workspace creation: This area focuses on the students’ skills in creating Azure ML workspace, operating workspaces by using Azure ML studio, and configuring workspace settings.
Data objects management within Azure ML workspace: The applicants should have competence in the creation and management of datasets, as well as maintenance and registration of datastores.

The DP-100 Exam covers various topics related to data science, including designing and implementing data ingestion and processing solutions, designing and implementing machine learning models, designing and implementing data storage solutions, and designing and implementing data visualization solutions. DP-100 exam also evaluates the candidate's knowledge of Azure services such as Azure Machine Learning, Azure Databricks, Azure Stream Analytics, and Azure Data Factory.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q190-Q195):

NEW QUESTION # 190
You create an Azure Data Lake Storage Gen2 stowage account named storage1 containing a file system named fsi and a folder named folder1.
The contents of folder1 must be accessible from jobs on compute targets in the Azure Machine Learning workspace.
You need to construct a URl to reference folder1.
How should you construct the URI? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:
see the answer below.
Explanation
See below image

NEW QUESTION # 191
You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements.
Which three modules should you recommend be used in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

Step 1: Sweep Clustering
Start by using the "Tune Model Hyperparameters" module to select the best sets of parameters for each of the models we're considering.
One of the interesting things about the "Tune Model Hyperparameters" module is that it not only outputs the results from the Tuning, it also outputs the Trained Model.
Step 2: Train Model
Step 3: Evaluate Model
Scenario: You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.
You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.
References:
http://breaking-bi.blogspot.com/2017/01/azure-machine-learning-model-evaluation.html

NEW QUESTION # 192
You create an Azure Machine Learning workspace.
You must implement dedicated compute for model training in the workspace by using Azure Synapse compute resources. The solution must attach the dedicated compute and start an Azure Synapse session.
You need to implement the compute resources.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:

1 - Create a linked service by using Azure Machine Learning studio.
2 - Create an Azure Synapse workspace by using the Azure portal.
3 - Create an Apache Spark pool by using the Azure portal.

NEW QUESTION # 193
Hotspot Question
You create an Azure Machine Learning workspace.
You plan to write an Azure Machine Learning SDK for Python v2 script that logs an image for an experiment. The logged image must be available from the images tab in Azure Machine Learning Studio.
You need to complete the script.
Which code segments should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics?view=azureml- api-2&tabs=interactive#logging-images

NEW QUESTION # 194
You have a Python script that executes a pipeline. The script includes the following code:
from azureml.core import Experiment
pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline)
You want to test the pipeline before deploying the script.
You need to display the pipeline run details written to the STDOUT output when the pipeline completes.
Which code segment should you add to the test script?

A. pipeline_run.wait_for_completion(show_output=True)
B. pipeline_run.get.metrics()
C. pipeline_param = PipelineParameter(name="stdout",default_value="console")
D. pipeline_run.get_status()

Answer: A

Explanation:
wait_for_completion: Wait for the completion of this run. Returns the status object after the wait.
Syntax: wait_for_completion(show_output=False, wait_post_processing=False, raise_on_error=True) Parameter: show_output Indicates whether to show the run output on sys.stdout.

NEW QUESTION # 195
......

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