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Microsoft Updated DP-100 Exam Questions and Answers by taran

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Microsoft DP-100 Exam Overview :

Exam Name: Designing and Implementing a Data Science Solution on Azure
Exam Code: DP-100 Dumps
Vendor: Microsoft Certification: Microsoft Azure
Questions: 460 Q&A's Shared By: taran
Question 16

You develop a chat flow in an Azure Al Foundry project

You plan to include a Jinja language-based prompt template in the How

You need to complete the provided template to display a list of inputs and outputs included in the flow.

How should you complete the provided template? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

Questions 16

Options:

Discussion
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Question 17

You use the following code to define the steps for a pipeline:

from azureml.core import Workspace, Experiment, Run

from azureml.pipeline.core import Pipeline

from azureml.pipeline.steps import PythonScriptStep

ws = Workspace.from_config()

. . .

step1 = PythonScriptStep(name="step1", ...)

step2 = PythonScriptsStep(name="step2", ...)

pipeline_steps = [step1, step2]

You need to add code to run the steps.

Which two code segments can you use to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

Options:

A.

experiment = Experiment(workspace=ws,name='pipeline-experiment')run = experiment.submit(config=pipeline_steps)

B.

run = Run(pipeline_steps)

C.

pipeline = Pipeline(workspace=ws, steps=pipeline_steps)experiment = Experiment(workspace=ws,name='pipeline-experiment')run = experiment.submit(pipeline)

D.

pipeline = Pipeline(workspace=ws, steps=pipeline_steps)run = pipeline.submit(experiment_name='pipeline-experiment')

Discussion
Question 18

You plan to provision an Azure Machine Learning Basic edition workspace for a data science project.

You need to identify the tasks you will be able to perform in the workspace.

Which three tasks will you be able to perform? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

D

Options:

A.

Create a Compute Instance and use it to run code in Jupyter notebooks.

B.

Create an Azure Kubernetes Service (AKS) inference cluster.

C.

Use the designer to train a model by dragging and dropping pre-defined modules.

D.

Create a tabular dataset that supports versioning.

E.

Use the Automated Machine Learning user interface to train a model.

Discussion
Question 19

You train and register an Azure Machine Learning model

You plan to deploy the model to an online endpoint

You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.

Solution:

Create a managed online endpoint and set the value of its auth.mode parameter to aml.token. Deploy the model to the online endpoint.

Does the solution meet the goal?

Options:

A.

Yes

B.

No

Discussion
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