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The following DP-100 questions are part of our Microsoft DP-100 real exam questions full version. There are 442 in our DP-100 full version. All of our DP-100 real exam questions can guarantee you success in the first attempt. If you fail DP-100 exam with our Microsoft DP-100 real exam questions, you will get full payment fee refund. Want to practice and study full verion of DP-100 real exam questions? Go now!

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Microsoft DP-100 Exam Actual Questions

The questions for DP-100 were last updated on Feb 21,2025 .

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Question#1

DRAG DROP
You need to correct the model fit issue.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.


A. 

Explanation:
Step 1: Augment the data
Scenario: Columns in each dataset contain missing and null values. The datasets also contain many outliers.
Step 2: Add the Bayesian Linear Regression module.
Scenario: You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.
Step 3: Configure the regularization weight.
Regularization typically is used to avoid overfitting. For example, in L2 regularization weight, type the value to use as the weight for L2 regularization. We recommend that you use a non-zero value to avoid overfitting.
Scenario:
Model fit: The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Question#2

HOTSPOT
You need to set up the Permutation Feature Importance module according to the model training requirements.
Which properties should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.


A. 

Explanation:
Box 1: Accuracy
Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.
Box 2: R-Squared

Question#3

HOTSPOT
You need to replace the missing data in the Accessibility To High way columns.
How should you configure the Clean Missing Data module? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.


A. 

Explanation:
Box 1: Replace using MICE
Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.
Scenario: The Accessibility To High way column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.
Box 2: Propagate
Cols with all missing values indicate if columns of all missing values should be preserved in the output.
References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

Question#4

DRAG DROP
You need to implement early stopping criteria as suited in the model training requirements.
Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order. NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.


A. 

Explanation:
You need to implement an early stopping criterion on models that provides savings without terminating promising jobs.
Truncation selection cancels a given percentage of lowest performing runs at each evaluation interval. Runs are compared based on their performance on the primary metric and the lowest X% are terminated.
Example:
from azureml.train.hyperdrive import TruncationSelectionPolicy
early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5)

Question#5

HOTSPOT
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 option-, m the answer area. NOTE: Each correct selection is worth one point.


A. 

Exam Code: DP-100Q & A: 442 Q&AsUpdated:  Feb 21,2025

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