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2025 Microsoft The Best DP-100: Designing and Implementing a Data Science Solution on Azure Test Certification Cost

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q144-Q149):

NEW QUESTION # 144
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:

NEW QUESTION # 145
You have a model with a large difference between the training and validation error values.
You must create a new model and perform cross-validation.
You need to identify a parameter set for the new model using Azure Machine Learning Studio.
Which module you should use for each step? To answer, drag the appropriate modules to the correct steps.
Each module may be used once or 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: Split data
Box 2: Partition and Sample
Box 3: Two-Class Boosted Decision Tree
Box 4: Tune Model Hyperparameters
Integrated train and tune: You configure a set of parameters to use, and then let the module iterate over multiple combinations, measuring accuracy until it finds a "best" model. With most learner modules, you can choose which parameters should be changed during the training process, and which should remain fixed.
We recommend that you use Cross-Validate Model to establish the goodness of the model given the specified parameters. Use Tune Model Hyperparameters to identify the optimal parameters.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

NEW QUESTION # 146
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:

You write Python SDK v2 code to materialize the data from the files in the sample.data folder into a Pandas data frame. You need to complete the Python SDK v2 code to use the MLTaWe folder as the materialization blueprint. 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

NEW QUESTION # 147
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. Yes
B. No

Answer: B

NEW QUESTION # 148
You are preparing to build a deep learning convolutional neural network model for image classification. You create a script to train the model using CUDA devices. You must submit an experiment that runs this script in the Azure Machine Learning workspace. The following compute resources are available:
* a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software
* a Compute Instance named ds-workstation in the workspace with 2 CPUs and 8 GB of memory
* an Azure Machine Learning compute target named cpu-cluster with eight CPU-based nodes
* an Azure Machine Learning compute target named gpu-cluster with four CPU and GPU-based nodes

Answer:

Explanation:

NEW QUESTION # 149
......

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