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The DP-100 exam covers a wide range of topics, including data exploration and preparation, modeling, visualization, and deployment. Candidates will be tested on their ability to use various Azure tools and services, such as Azure Machine Learning, Azure Databricks, and Azure Cognitive Services, to build end-to-end data science solutions. They will also need to demonstrate their understanding of the principles of machine learning, statistical analysis, and data visualization, as well as their ability to communicate insights and findings to stakeholders. Passing DP-100 Exam is a significant achievement for data science professionals who want to showcase their expertise in Azure-based data science solutions.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q246-Q251):

NEW QUESTION # 246
You register the following versions of a model.

You use the Azure ML Python SDK to run a training experiment. You use a variable named run to reference the experiment run.
After the run has been submitted and completed, 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:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where

NEW QUESTION # 247
You create a multi-class image classification deep learning model.
The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.
You need to use the Azure Machine Learning SDK to configure the schedule for the pipeline.
Which four 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 - Publish the pipeline.
2 - Retrieve the pipeline ID.
3 - Create a ScheduleRecurrence..
4 - Define an Azure Machine Learning pipeline schedule..
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-schedule-pipelines

NEW QUESTION # 248
You need to select a feature extraction method.
Which method should you use?

A. Permutation Feature Importance
B. Mood's median test
C. Kendall correlation
D. Mutual information

Answer: C

Explanation:
Explanation
In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter ), is a statistic used to measure the ordinal association between two measured quantities.
It is a supported method of the Azure Machine Learning Feature selection.
Scenario: When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules

NEW QUESTION # 249
You use Azure Machine Learning to train a machine learning model.
You use the following training script in Python to perform logging:

You must use a Python script to define a sweep job.
You need to provide the primary metric and goal you want hyperparameter tuning to optimize.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 250
You create a batch inference pipeline by using the Azure ML SDK. You configure the pipeline parameters by executing the following code:

You need to obtain the output from the pipeline execution.
Where will you find the output?

A. a file named parallel_run_step.txt located in the output folder
B. the debug log
C. the Activity Log in the Azure portal for the Machine Learning workspace
D. the Inference Clusters tab in Machine Learning studio
E. the digit_identification.py script

Answer: A

Explanation:
output_action (str): How the output is to be organized. Currently supported values are 'append_row' and
'summary_only'.
* 'append_row' - All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.
* 'summary_only'
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/ azureml.contrib.pipeline.steps.parallelrunconfig

NEW QUESTION # 251
......

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