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Examcollection DP-100 Questions Answers | Microsoft Exam DP-100 Blueprint: Designing and Implementing a Data Science Solution on Azure Pass Certify

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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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Exam DP-100 Blueprint & Demo DP-100 Test

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The Microsoft DP-100 Exam is designed to test the candidate's understanding of key concepts such as data exploration and preparation, modeling, deployment, and maintenance of machine learning models. It requires a deep understanding of Azure data services, including Azure Machine Learning, Azure Databricks, and Azure HDInsight. Furthermore, candidates are expected to have a good understanding of programming languages such as Python and R.

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q434-Q439):

NEW QUESTION # 434
You create an Azure Machine Learning workspace and set up a development environment. You plan to train a deep neural network (DNN) by using the Tensorflow framework and by using estimators to submit training scripts.
You must optimize computation speed for training runs.
You need to choose the appropriate estimator to use as well as the appropriate training compute target configuration.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 435
You are running a training experiment on remote compute in Azure Machine Learning.
The experiment is configured to use a conda environment that includes the mlflow and azureml-contrib-run packages.
You must use MLflow as the logging package for tracking metrics generated in the experiment.
You need to complete the script for the experiment.
How should you complete the code? 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/how-to-use-mlflow

NEW QUESTION # 436
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.

Answer:

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

NEW QUESTION # 437
You need to implement a new cost factor scenario for the ad response models as illustrated in the performance curve exhibit.
Which technique should you use?

A. Set the threshold to 0.2 and retrain if weighted Kappa deviates +/- 5% from 0.6.
B. Set the threshold to 0.05 and retrain if weighted Kappa deviates +/- 5% from 0.5.
C. Set the threshold to 0.75 and retrain if weighted Kappa deviates +/- 5% from 0.15.
D. Set the threshold to 0.5 and retrain if weighted Kappa deviates +/- 5% from 0.45.

Answer: D

Explanation:
Scenario:
Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated from 0.1
+/- 5%.

NEW QUESTION # 438
You are using the Azure Machine Learning Service to automate hyperparameter exploration of your neural network classification model.
You must define the hyperparameter space to automatically tune hyperparameters using random sampling according to following requirements:
* The learning rate must be selected from a normal distribution with a mean value of 10 and a standard deviation of 3.
* Batch size must be 16, 32 and 64.
* Keep probability must be a value selected from a uniform distribution between the range of 0.05 and
* 0.1.
You need to use the param_sampling method of the Python API for the Azure Machine Learning Service.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.
Example:
from azureml.train.hyperdrive import RandomParameterSampling
param_sampling = RandomParameterSampling( {
"learning_rate": normal(10, 3),
"keep_probability": uniform(0.05, 0.1),
"batch_size": choice(16, 32, 64)
}
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters

NEW QUESTION # 439
......

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