Microsoft DP-100 Latest Test Labs | DP-100 Free Vce Dumps

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q506-Q511):

NEW QUESTION # 506
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 are using Azure Machine Learning to run an experiment that trains a classification model.
You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.
You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.
Solution: Run the following code:

Does the solution meet the goal?

A. Yes
B. No

Answer: B

Explanation:
Use a solution with logging.info(message) instead.
Note: Python printing/logging example:
logging.info(message)
Destination: Driver logs, Azure Machine Learning designer
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-debug-pipelines

NEW QUESTION # 507
A biomedical research company plans to enroll people in an experimental medical treatment trial.
You create and train a binary classification model to support selection and admission of patients to the trial. The model includes the following features: Age, Gender, and Ethnicity.
The model returns different performance metrics for people from different ethnic groups.
You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.
Which technique and constraint should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml

NEW QUESTION # 508
Your team is building a data engineering and data science development environment.
The environment must support the following requirements:
support Python and Scala
compose data storage, movement, and processing services into automated data pipelines the same tool should be used for the orchestration of both data engineering and data science support workload isolation and interactive workloads enable scaling across a cluster of machines You need to create the environment.
What should you do?

A. Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.
B. Build the environment in Azure Databricks and use Azure Container Instances for orchestration.
C. Build the environment in Azure Databricks and use Azure Data Factory for orchestration.
D. Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.

Answer: C

Explanation:
In Azure Databricks, we can create two different types of clusters.
Standard, these are the default clusters and can be used with Python, R, Scala and SQL High-concurrency Azure Databricks is fully integrated with Azure Data Factory.

NEW QUESTION # 509
You are performing a classification task in Azure Machine learning Studio.
You must prepare balanced testing and training samples based on a provided data set.
Warning samples based on a provided data set.
You need to split the data with a 0.75:0.25.
Which value should you use for each parameter? To answer, select the appropriate options m the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 510
You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.
Original and scaled data is shown in the following image.

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

Answer:

Explanation:

Explanation:
Box 1: StandardScaler
The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centred around 0, with a standard deviation of 1.
Example:

All features are now on the same scale relative to one another.
Box 2: Min Max Scaler

Notice that the skewness of the distribution is maintained but the 3 distributions are brought into the same scale so that they overlap.
Box 3: Normalizer
References:
http://benalexkeen.com/feature-scaling-with-scikit-learn/

NEW QUESTION # 511
......

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Microsoft DP-100 Latest Test Labs | DP-100 Free Vce Dumps

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Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q506-Q511):

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