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Frenquent DP-100 Update | DP-100 Latest Dump

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Microsoft DP-100 certification exam is a challenging exam that requires candidates to have a strong understanding of data science concepts and Microsoft Azure data services. DP-100 exam is designed to test the candidate's ability to solve real-world data science problems using Microsoft Azure data services. Candidates who pass the exam will be able to demonstrate their ability to design and implement data science solutions on Microsoft Azure.

The DP-100 Exam covers a range of topics related to data science, including data preparation, feature engineering, model training, and deployment. Candidates will be tested on their ability to use Azure services to build end-to-end machine learning solutions that can be deployed at scale.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q277-Q282):

NEW QUESTION # 277
Hotspot Question
You are evaluating a Python NumPy array that contains six data points defined as follows:
data = [10, 20, 30, 40, 50, 60]
You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library:
train: [10 40 50 60], test: [20 30]
train: [20 30 40 60], test: [10 50]
train: [10 20 30 50], test: [40 60]
You need to implement a cross-validation to generate the output.
How should you complete the code segment? To answer, select the appropriate code segment in the dialog box in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
Box 1: k-fold
Box 2: 3
K-Folds cross-validator provides train/test indices to split data in train/test sets. Split dataset into k consecutive folds (without shuffling by default). The parameter n_splits ( int, default=3) is the number of folds. Must be at least 2.
Box 3: data
Example: Example:
>>>
>>> from sklearn.model_selection import KFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([1, 2, 3, 4])
>>> kf = KFold(n_splits=2)
>>> kf.get_n_splits(X)
>>> print(kf)
KFold(n_splits=2, random_state=None, shuffle=False)
>>> for train_index, test_index in kf.split(X):
... print("TRAIN:", train_index, "TEST:", test_index)
... X_train, X_test = X[train_index], X[test_index]
... y_train, y_test = y[train_index], y[test_index]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
References:
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

NEW QUESTION # 278
You create an Azure Machine Learning workspace.
You must configure an event-driven workflow to automatically trigger upon completion of training runs in the workspace. The solution must minimize the administrative effort to configure the trigger.
You need to configure an Azure service to automatically trigger the workflow.
Which Azure service should you use?

A. Azure Automation runbook
B. Event Hubs Capture
C. Event Grid subscription
D. Event Hubs consumer

Answer: C

NEW QUESTION # 279
You create an MLflow model.
You must deploy the model to Azure Machine Learning for batch inference.
You need to create the batch deployment.
Which two components should you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Compute target
B. Online endpoint
C. Model files
D. Environment
E. Kubernetes online endpoint

Answer: A,C

Explanation:
MLflow models don't require you to indicate an environment or a scoring script when creating the deployments as it is created for you. However, you can specify them if you want to customize how the deployment does inference.
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-mlflow-batch?tabs=cli

NEW QUESTION # 280
Hotspot Question
You manage an Azure subscription that contains the following resources:

You plan to implement a solution that will automatically trigger the retraining of the model implemented by MLPipeline1. The trigger must be invoked if data drift is detected in Dataset1.
You need to select the components to invoke and run the solution. The solution must minimize coding implementation and maintenance efforts.
Which components should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 281
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.

Answer:

Explanation:

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.
Incorrect Answers:
Multiclass Decision Jungle module:
Decision jungles are a recent extension to decision forests. A decision jungle consists of an ensemble of decision directed acyclic graphs (DAGs).
L-BFGS:
L-BFGS stands for "limited memory Broyden-Fletcher-Goldfarb-Shanno". It can be found in the wwo-Class Logistic Regression module, which is used to create a logistic regression model that can be used to predict two (and only two) outcomes.
References:
<https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/linear-regr ession>

NEW QUESTION # 282
......

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