Most Recent Microsoft DP-100 Exam Questions & Answers

Prepare for the Microsoft Designing and Implementing a Data Science Solution on Azure exam with our extensive collection of questions and answers. These practice Q&A are updated according to the latest syllabus, providing you with the tools needed to review and test your knowledge.

QA4Exam focus on the latest syllabus and exam objectives, our practice Q&A are designed to help you identify key topics and solidify your understanding. By focusing on the core curriculum, These Questions & Answers helps you cover all the essential topics, ensuring you're well-prepared for every section of the exam. Each question comes with a detailed explanation, offering valuable insights and helping you to learn from your mistakes. Whether you're looking to assess your progress or dive deeper into complex topics, our updated Q&A will provide the support you need to confidently approach the Microsoft DP-100 exam and achieve success.

The questions for DP-100 were last updated on Dec 19, 2024.

Viewing page 1 out of 88 pages.
Viewing questions 1-5 out of 442 questions

Get All 442 Questions & Answers

Question No. 1

You have an Azure Machine Learning workspace. You are connecting an Azure Data Lake Storage Gen2 account to the workspace as a data store. You need to authorize access from the workspace to the Azure Data Lake Storage Gen2 account.

What should you use?

AManaged identity

BSAS token

CService principal

DAccount key

Show Answer

Correct Answer: A

Question No. 2

You manage an Azure Machine Learning workspace. The development environment for managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks

A Synapse Spark Compute is currently attached and uses system-assigned identity

You need to use Python code to update the Synapse Spark Compute to use a user-assigned identity.

Solution: Create an instance of the MICIient class.

Does the solution meet the goal?

AYes

BNo

Show Answer

Correct Answer: B

Question No. 3

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.

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

Brun = Run(pipeline_steps)

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

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

Show Answer

Correct Answer: C, D

After you define your steps, you build the pipeline by using some or all of those steps.

# Build the pipeline. Example:

pipeline1 = Pipeline(workspace=ws, steps=[compare_models])

# Submit the pipeline to be run

pipeline_run1 = Experiment(ws, 'Compare_Models_Exp').submit(pipeline1)

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-machine-learning-pipelines

Question No. 4

You use an Azure Machine Learning workspace.

You have a trained model that must be deployed as a web service. Users must authenticate by using Azure Active Directory.

What should you do?

ADeploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_enabled parameter of the target configuration object to true

BDeploy the model to Azure Container Instances. During deployment, set the auch_enabled parameter of the target configuration object to true

CDeploy the model to Azure Container Instances. During deployment, set the coken_auch_enabled parameter of the target configuration object to true

DDeploy the model to Azure Kubernetes Service (AKS). During deployment, set the auch. enabled parameter of the target configuration object to true

Show Answer

Correct Answer: A

To control token authentication, use the token_auth_enabled parameter when you create or update a deployment

Token authentication is disabled by default when you deploy to Azure Kubernetes Service.

Note: The model deployments created by Azure Machine Learning can be configured to use one of two authentication methods:

key-based: A static key is used to authenticate to the web service.

token-based: A temporary token must be obtained from the Azure Machine Learning workspace (using Azure Active Directory) and used to authenticate to the web service.

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-authenticate-web-service

Question No. 5

You have a dataset that contains records of patients tested for diabetes. The dataset includes the patient s age.

You plan to create an analysis that will report the mean age value from the differentially private data derived from the dataset-

You need to identify the epsilon value to use in the analysis that minimizes the risk of exposing the actual data.

Which epsilon value should you use?

A-1.5

B-0.5

C0.5

D1.5

Show Answer

Correct Answer: C

Unlock All Questions for Microsoft DP-100 Exam

Full Exam Access, Actual Exam Questions, Validated Answers, Anytime Anywhere, No Download Limits, No Practice Limits

Get All 442 Questions & Answers