Exam Microsoft DP-100 Dumps | DP-100 Valid Test Braindumps

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Microsoft DP-100 Exam Syllabus Topics:

SectionObjectives
Topic 1: Explore and analyze data- Ingest and prepare data for modeling
- Perform exploratory data analysis
Topic 2: Train machine learning models- Train models using Azure Machine Learning
- Tune hyperparameters and evaluate models
Topic 3: Deploy and consume models- Deploy models to endpoints
- Monitor deployed models and endpoints
Topic 4: Optimize and manage models- Improve model performance
- Track experiments and manage model lifecycle
Topic 5: Design and prepare a machine learning solution- Plan and configure Azure Machine Learning workspace
- Select appropriate Azure services for machine learning workloads
- Manage compute and data assets

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q322-Q327):

NEW QUESTION # 322
You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

Answer:

Explanation:


NEW QUESTION # 323
You use Data Science Virtual Machines (DSVMs) for Windows and Linux in Azure.
You need to access the DSVMs.
Which utilities should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 324
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: B


NEW QUESTION # 325
You need to identify the methods for dividing the data according to the testing requirements.
Which properties should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation


Scenario: Testing
You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio.
Box 1: Assign to folds
Use Assign to folds option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.
Not Head: Use Head mode to get only the first n rows. This option is useful if you want to test a pipeline on a small number of rows, and don't need the data to be balanced or sampled in any way.
Not Sampling: The Sampling option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
Box 2: Partition evenly
Specify the partitioner method: Indicate how you want data to be apportioned to each partition, using these options:
* Partition evenly: Use this option to place an equal number of rows in each partition. To specify the number of output partitions, type a whole number in the Specify number of folds to split evenly into text box.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/partition-and-sample


NEW QUESTION # 326
You use the following code to run a script as an experiment in Azure Machine Learning:

You must identify the output files that are generated by the experiment run.
You need to add code to retrieve the output file names.
Which code segment should you add to the script?

Answer: E

Explanation:
You can list all of the files that are associated with this run record by called run.get_file_names() Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments


NEW QUESTION # 327
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