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DP-100 Valid Exam Simulator and Microsoft Reliable DP-100 Dumps Pdf: Designing and Implementing a Data Science Solution on Azure Pass Success

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2Pass4sure made an DP-100 Questions for the students so that they don't get confused to prepare for Designing and Implementing a Data Science Solution on Azure (DP-100) certification exam successfully in a short time. 2Pass4sure has designed the real DP-100 exam dumps after consulting many professionals and receiving positive feedback. The Designing and Implementing a Data Science Solution on Azure (DP-100) questions have many premium features, so you don't face any hurdles while preparing for Designing and Implementing a Data Science Solution on Azure (DP-100) exam and pass it with good grades.

The DP-100 Exam is designed to test candidates' knowledge and skills in various areas related to data science, such as data exploration and preparation, modeling, feature engineering, and machine learning. To pass the exam, candidates must demonstrate their ability to design and implement data science solutions using Azure services such as Azure Machine Learning, Azure Databricks, and Azure HDInsight, among others.

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q48-Q53):

NEW QUESTION # 48
You create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for each description? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation

Box 1: True Positive
A true positive is an outcome where the model correctly predicts the positive class Box 2: True Negative A true negative is an outcome where the model correctly predicts the negative class.
Box 3: False Positive
A false positive is an outcome where the model incorrectly predicts the positive class.
Box 4: False Negative
A false negative is an outcome where the model incorrectly predicts the negative class.
Note: Let's make the following definitions:
"Wolf" is a positive class.
"No wolf" is a negative class.
We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:
Reference:
https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative

NEW QUESTION # 49
You manage an Azure Machine Learning workspace by using the Python SDK v2.
You must create a compute cluster in the workspace. The compute cluster must run workloads and properly handle interruptions. You start by calculating the maximum amount of compute resources required by the workloads and size the cluster to match the calculations.
The cluster definition includes the following properties and values:
* name="mlcluster1''
* size="STANDARD.DS3.v2"
* min_instances=1
* maxjnstances=4
* tier="dedicated"
The cost of the compute resources must be minimized when a workload is active Of idle. Cluster property changes must not affect the maximum amount of compute resources available to the workloads run on the cluster.
You need to modify the cluster properties to minimize the cost of compute resources.
Which properties should you modify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 50
You are developing a machine learning solution by using the Azure Machine Learning designer.
You need to create a web service that applications can use to submit data feature values and retrieve a predicted label.
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 # 51
An organization uses Azure Machine Learning service and wants to expand their use of machine learning.
You have the following compute environments. The organization does not want to create another compute environment.

You need to determine which compute environment to use for the following scenarios.
Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios. Each compute environment 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:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets

NEW QUESTION # 52
You create a new Azure Databricks workspace.
You configure a new cluster for long-running tasks with mixed loads on the compute cluster as shown in the image below.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.databricks.com/clusters/configure.html

NEW QUESTION # 53
......

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