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Exam DP-100 Topics, DP-100 Updated CBT

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q468-Q473):

NEW QUESTION # 468
You need to modify the inputs for the global penalty event model to address the bias and variance 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.

Answer:

Explanation:

NEW QUESTION # 469
You create a training pipeline using the Azure Machine Learning designer. You upload a CSV file that contains the data from which you want to train your model.
You need to use the designer to create a pipeline that includes steps to perform the following tasks:
Select the training features using the pandas filter method.
Train a model based on the naive_bayes.GaussianNB algorithm.
Return only the Scored Labels column by using the query SELECT [Scored Labels] FROM t1; Which modules should you use? To answer, drag the appropriate modules to the appropriate locations. Each module name may be used once, 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:

NEW QUESTION # 470
You need to replace the missing data in the AccessibilityToHighway 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.

Answer:

Explanation:

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 AccessibilityToHighway 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

NEW QUESTION # 471
You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.

Answer:

Explanation:

Reference:
https://medium.com/comet-ml/organizing-machine-learning-projects-project-management-guidelines-2d2b85651bbd

NEW QUESTION # 472
You manage an Azure Al Foundry project.
You plan to develop a RAG solution from a set of PDF files. To achieve this, you plan to create a vector index from the data. You need to select the location of the data you plan to index.
Which two data sources can you use? Each correct answer presents a complete solution. Choose two. NOTE:
Each correct selection is worth one point.

A. Azure Data Lake Storage Gen2
B. OneLake in Microsoft Fabric
C. Azure Blob Storage
D. Data in Azure Al Foundry

Answer: A,C

NEW QUESTION # 473
......

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