Simulation DP-100 Questions & DP-100 Verified Answers

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Microsoft DP-100 exam covers various topics related to data science, including data exploration and preparation, data modeling, machine learning algorithms, and data visualization. DP-100 exam also assesses the candidate's ability to use Azure services for data science, such as Azure Machine Learning, Azure Databricks, and Azure HDInsight. To prepare for the exam, candidates can take online courses, attend training sessions, and practice with sample questions and labs provided by Microsoft. By passing the Microsoft DP-100 Exam, candidates can demonstrate their expertise in designing and implementing data science solutions on Azure, which can help them advance their career and gain recognition in the industry.

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Microsoft DP-100 Exam Syllabus Topics:

Topic	Details
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 (Q117-Q122):

NEW QUESTION # 117
You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint. The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.
You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.
You need to configure store1.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Box 1: Regenerate the keys of account1.
Azure Blob Storage support authentication through Account key or SAS token.
To authenticate your access to the underlying storage service, you can provide either your account key, shared access signatures (SAS) tokens, or service principal Box 2: Update the authentication for store1.
For Azure Machine Learning studio users, several features rely on the ability to read data from a dataset; such as dataset previews, profiles and automated machine learning. For these features to work with storage behind virtual networks, use a workspace managed identity in the studio to allow Azure Machine Learning to access the storage account from outside the virtual network.
Note: Some of the studio's features are disabled by default in a virtual network. To re-enable these features, you must enable managed identity for storage accounts you intend to use in the studio.
The following operations are disabled by default in a virtual network:
Preview data in the studio.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data

NEW QUESTION # 118
A set of CSV files contains sales records. All the CSV files have the same data schema.
Each CSV file contains the sales record for a particular month and has the filename sales.csv. Each file in stored in a folder that indicates the month and year when the data was recorded. The folders are in an Azure blob container for which a datastore has been defined in an Azure Machine Learning workspace. The folders are organized in a parent folder named sales to create the following hierarchical structure:

At the end of each month, a new folder with that month's sales file is added to the sales folder.
You plan to use the sales data to train a machine learning model based on the following requirements:
* You must define a dataset that loads all of the sales data to date into a structure that can be easily converted to a dataframe.
* You must be able to create experiments that use only data that was created before a specific previous month, ignoring any data that was added after that month.
* You must register the minimum number of datasets possible.
You need to register the sales data as a dataset in Azure Machine Learning service workspace.
What should you do?

A. Create a tabular dataset that references the datastore and specifies the path 'sales/*/sales.csv', register the dataset with the name sales_dataset and a tag named month indicating the month and year it was registered, and use this dataset for all experiments.
B. Create a new tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file every month. Register the dataset with the name sales_dataset_MM-YYYY each month with appropriate MM and YYYY values for the month and year. Use the appropriate month-specific dataset for experiments.
C. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file. Register the dataset with the name sales_dataset each month as a new version and with a tag named month indicating the month and year it was registered. Use this dataset for all experiments, identifying the version to be used based on the month tag as necessary.
D. Create a tabular dataset that references the datastore and explicitly specifies each 'sales/mm-yyyy/ sales.csv' file every month. Register the dataset with the name sales_dataset each month, replacing the existing dataset and specifying a tag named month indicating the month and year it was registered. Use this dataset for all experiments.

Answer: A

Explanation:
Specify the path.
Example:
The following code gets the workspace existing workspace and the desired datastore by name. And then passes the datastore and file locations to the path parameter to create a new TabularDataset, weather_ds.
from azureml.core import Workspace, Datastore, Dataset
datastore_name = 'your datastore name'
# get existing workspace
workspace = Workspace.from_config()
# retrieve an existing datastore in the workspace by name
datastore = Datastore.get(workspace, datastore_name)
# create a TabularDataset from 3 file paths in datastore
datastore_paths = [(datastore, 'weather/2018/11.csv'),
(datastore, 'weather/2018/12.csv'),
(datastore, 'weather/2019/*.csv')]
weather_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)

NEW QUESTION # 119
You have several machine learning models registered in an Azure Machine Learning workspace.
You must use the Fairlearn dashboard to assess fairness in a selected model.
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:

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml

NEW QUESTION # 120
You are analyzing a dataset containing historical data from a local taxi company. You arc developing a regression a regression model.
You must predict the fare of a taxi trip.
You need to select performance metrics to correctly evaluate the- regression model.
Which two metrics can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. a Root Mean Square Error value that is high
B. an F1 score that is high
C. an R Squared value dose to 1
D. an F 1 score that is low.
E. a Root Mean Square Error value that is low
F. an R-Squared value close to 0

Answer: C,E

Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

NEW QUESTION # 121
You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy.
The policy uses a slack_factor set to 01. an evaluation interval set to 1, and an evaluation delay set to b.
You need to evaluate the outcome of the early termination policy
What should you evaluate? To answer, select the appropriate options m the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 122
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Simulation DP-100 Questions & DP-100 Verified Answers

Hot Simulation DP-100 Questions | High Pass-Rate DP-100: Designing and Implementing a Data Science Solution on Azure 100% Pass

Microsoft DP-100 Exam Syllabus Topics:

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

Run Experiments and Train Models (20-25%)

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

Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q117-Q122):

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