DP-100 LATEST EXAM FEE & RELIABLE DP-100 TEST COST

DP-100 Latest Exam Fee & Reliable DP-100 Test Cost

DP-100 Latest Exam Fee & Reliable DP-100 Test Cost

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Tags: DP-100 Latest Exam Fee, Reliable DP-100 Test Cost, DP-100 Valid Exam Duration, DP-100 Dumps Free, DP-100 Free Updates

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What Next after This Certification?

The certification obtained from passing DP-100 exam is proof of intermediate skills in data science since it is within the associate hierarchy. Apart from applying for and getting a job in a preferred company, you can also expand your skills in an area of interest. With the role-based scheme, there are expert certifications awaiting you to earn. They include the Microsoft Certified: Azure Solutions Architect Expert and the Microsoft Certified: Azure DevOps Engineer Expert.

Microsoft DP-100 Certification Exam is designed for data scientists and data engineers who want to demonstrate their ability to design and implement data science solutions on Azure. DP-100 exam is intended for individuals who have experience using Azure to build and deploy machine learning models, as well as those who have experience with data storage and processing on Azure.

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Reliable DP-100 Test Cost & DP-100 Valid Exam Duration

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Microsoft DP-100 Exam Syllabus Topics:

TopicDetails

Manage Azure resources for machine learning (25-30%)

Create an Azure Machine Learning workspace- create an Azure Machine Learning workspace
- configure workspace settings
- manage a workspace by using Azure Machine Learning studio
Manage data in an Azure Machine Learning workspace- select Azure storage resources
- register and maintain datastores
- create and manage datasets
Manage compute for experiments in Azure Machine Learning- determine the appropriate compute specifications for a training workload
- create compute targets for experiments and training
- configure Attached Compute resources including Azure Databricks
- monitor compute utilization
Implement security and access control in Azure Machine Learning- determine access requirements and map requirements to built-in roles
- create custom roles
- manage role membership
- manage credentials by using Azure Key Vault
Set up an Azure Machine Learning development environment- create compute instances
- share compute instances
- access Azure Machine Learning workspaces from other development environments
Set up an Azure Databricks workspace- create an Azure Databricks workspace
- create an Azure Databricks cluster
- create and run notebooks in Azure Databricks
- link and Azure Databricks workspace to an Azure Machine Learning workspace

Run Experiments and Train Models (20-25%)

Create models by using the Azure Machine Learning Designer- create a training pipeline by using Azure Machine Learning designer
- ingest data in a designer pipeline
- use designer modules to define a pipeline data flow
- use custom code modules in designer
Run model training scripts- create and run an experiment by using the Azure Machine Learning SDK
- configure run settings for a script
- consume data from a dataset in an experiment by using the Azure Machine Learning SDK
- run a training script on Azure Databricks compute
- run code to train a model in an Azure Databricks notebook
Generate metrics from an experiment run- log metrics from an experiment run
- retrieve and view experiment outputs
- use logs to troubleshoot experiment run errors
- use MLflow to track experiments
- track experiments running in Azure Databricks
Use Automated Machine Learning to create optimal models- use the Automated ML interface in Azure Machine Learning studio
- use Automated ML from the Azure Machine Learning SDK
- select pre-processing options
- select the algorithms to be searched
- define a primary metric
- get data for an Automated ML run
- retrieve the best model
Tune hyperparameters with Azure Machine Learning- select a sampling method
- define the search space
- define the primary metric
- define early termination options
- find the model that has optimal hyperparameter values

Deploy and operationalize machine learning solutions (35-40%)

Select compute for model deployment- consider security for deployed services
- evaluate compute options for deployment
Deploy a model as a service- configure deployment settings
- deploy a registered model
- deploy a model trained in Azure Databricks to an Azure Machine Learning endpoint
- consume a deployed service
- troubleshoot deployment container issues
Manage models in Azure Machine Learning- register a trained model
- monitor model usage
- monitor data drift
Create an Azure Machine Learning pipeline for batch inferencing- configure a ParallelRunStep
- configure compute for a batch inferencing pipeline
- publish a batch inferencing pipeline
- run a batch inferencing pipeline and obtain outputs
- obtain outputs from a ParallelRunStep
Publish an Azure Machine Learning designer pipeline as a web service- create a target compute resource
- configure an Inference pipeline
- consume a deployed endpoint
Implement pipelines by using the Azure Machine Learning SDK- create a pipeline
- pass data between steps in a pipeline
- run a pipeline
- monitor pipeline runs
Apply ML Ops practices- trigger an Azure Machine Learning pipeline from Azure DevOps
- automate model retraining based on new data additions or data changes
- refactor notebooks into scripts
- implement source control for scripts

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q341-Q346):

NEW QUESTION # 341
A company has a Microsoft Azure HDInsight solution that uses different cluster types to process and analyze data.
Operations are continuous. Reports indicate slowdowns during a specific time window.
You need to determine a monitoring solution to track down the issue in the least amount of time.
What should you use?

  • A. HDInsight .NET SDK
  • B. Azure Log Analytics log search query
  • C. Azure Log Analytics alert rule query
  • D. Azure Monitor Metrics
  • E. Ambari REST API

Answer: E


NEW QUESTION # 342
You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.
Which three Azure Machine Learning Studio modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

Answer:

Explanation:


NEW QUESTION # 343
You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best performing model based on a metric named accuracy.
You submit the experiment with the following code:
You need to create Python code that returns the best model that is generated by the automated machine learning task. Which code segment should you use?

  • A.
  • B.
  • C.
  • D.

Answer: B

Explanation:
The get_output method returns the best run and the fitted model.
Reference:
https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated- machine-learning/classification/auto-ml-classification.ipynb


NEW QUESTION # 344

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 345
You need to select a feature extraction method.
Which method should you use?

  • A. Spearman correlation
  • B. Mann-Whitney test
  • C. Pearson's correlation
  • D. Mutual information

Answer: A

Explanation:
Spearman's rank correlation coefficient assesses how well the relationship between two variables can be described using a monotonic function.
Note: Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.
Scenario: The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.
References:


NEW QUESTION # 346
......

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