Microsoft DP-100 Free Certification Exam Questions Answer Mar 2026 update

Question # 51

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:

• /data/2018/Q1.csv

• /data/2018/Q2.csv

• /data/2018/Q3.csv

• /data/2018/Q4.csv

• /data/2019/Q1.csv

All files store data in the following format:

id,f1,f2i

1,1.2,0

2,1,1,

1 3,2.1,0

You run the following code:

You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

Solution: Run the following code:

Does the solution meet the goal?

Yes

Question # 52

You manage an Azure Al Foundry project.

You develop a Prompt flow that includes a large language model (LLM) node and an upstream node with a single output. You need to link the LLM node input with the output of the upstream node by using a YAML flow configuration. Which flow configuration should you use?

$(upstream_node_name.output)

<#upstream_node_nameoutput#>

(% upstream node_name,output%}

{{upstream.node.nameoutput})

Question # 53

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are analyzing a numerical dataset which contains missing values in several columns.

You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.

You need to analyze a full dataset to include all values.

Solution: Remove the entire column that contains the missing data point.

Does the solution meet the goal?

Yes

Question # 54

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?

Yes

Question # 55

You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training.

You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value.

You need to modify the inference pipeline to meet the requirement.

What should you do?

Add a Select Columns in Dataset module to the inference pipeline after the dataset and use it to select all columns other than the label.

Delete the dataset from the training pipeline and recreate the real-time inference pipeline.

Delete the Web Service Input module from the inference pipeline.

Replace the dataset in the inference pipeline with an Enter Data Manually module that includes data for the feature columns but not the label column.

Question # 56

You use the Azure Machine Learning Python SDK to create a batch inference pipeline.

You must publish the batch inference pipeline so that business groups in your organization can use the pipeline. Each business group must be able to specify a different location for the data that the pipeline submits to the model for scoring.

You need to publish the pipeline.

What should you do?

Create multiple endpoints for the published pipeline service and have each business group submit jobs to its own endpoint.

Define a PipelineParameter object for the pipeline and use it to specify the business group-specific input dataset for each pipeline run.

Define a OutputFileDatasetConfig object for the pipeline and use the object to specify the business group-specific input dataset for each pipeline run.

Have each business group run the pipeline on local compute and use a local file for the input data.

Question # 57

After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You are analyzing a numerical dataset which contain missing values in several columns.

You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.

You need to analyze a full dataset to include all values.

Solution: Use the last Observation Carried Forward (IOCF) method to impute the missing data points.

Does the solution meet the goal?

Yes

Question # 58

You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements.

Which three modules should you recommend be used in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.