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Microsoft DP-100 certification exam is a highly recognized certification in the field of data science. It is designed to provide candidates with the skills and knowledge they need to design and implement data science solutions using Azure technologies. By achieving this certification, candidates can demonstrate their expertise in the field, and enhance their career prospects by opening up new opportunities in the industry.
To prepare for the Microsoft DP-100 Certification Exam, you should have a solid understanding of data science concepts and techniques, as well as experience working with Azure tools and services. Microsoft offers various training courses and resources to help you prepare for the exam, including online courses, practice tests, and study guides. You can also find numerous third-party resources, such as books and tutorials, to help you prepare for the exam.
Microsoft DP-100 Practice Test Online & Exam DP-100 Pass GuideNow is the ideal time to prepare for and crack the DP-100 exam. To do this, you just need to enroll in the DP-100 examination and start preparation with top-notch and updated Microsoft DP-100 actual exam dumps. All three formats of Designing and Implementing a Data Science Solution on Azure DP-100 Practice Test are available with up to three months of free Designing and Implementing a Data Science Solution on Azure exam questions updates, free demos, and a satisfaction guarantee. Just pay an affordable price and get DP-100 updated exam dumps. Target Audience & RequirementsThe candidates for this Microsoft exam are Azure Data Scientists. These professionals have expertise in applying their knowledge of machine learning and data science to run and implement ML workloads on Azure. This is particularly in the usage of Azure ML Service. These applicants are the experts in planning and creating the appropriate working environments for data science workloads within Azure. They also train predictive models and run data experiments. The individuals who want to earn ACE college credit can also take this certification test.
The Microsoft DP-100: Designing & Implementing a Data Science Solution on Azure test has no official requirement. However, the candidates must develop an in-depth understanding of the exam topics. They should also have expertise in model optimization and management and ML models deployment within the production. Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q512-Q517):NEW QUESTION # 512
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
You must register datastores in workspace1 for Azure Blob and Azure Data Lake Gen2 storage to meet the following requirements:
* Data scientists accessing the datastore must have the same level of access.
* Access must be restricted to specified containers or folders.
You need to configure a security access method used to register the Azure Blob and Azure Data lake Gen? storage in workspace1. Which security access method should you configure? To answer, select the appropriate options in the answers area.
NOTE: Each correct selection is worth one point. Answer:
Explanation:
NEW QUESTION # 513
You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.
You must test and deploy the trained model as a web service. The deployed model must meet the following requirements:
An authenticated connection must not be required for testing.
The deployed model must perform with low latency during inferencing.
The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application.
You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted.
Which compute resources 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 ... svm-common-identity https://docs.microsoft.com/en-us ... ining-deep-learning
NEW QUESTION # 514
You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and Theano. You need to select a pre configured DSVM to support the framework.
What should you create?
A. Geo AI Data Science Virtual Machine with ArcGIS
B. Data Science Virtual Machine for Windows 2016
C. Data Science Virtual Machine for Linux (Ubuntu)
D. Data Science Virtual Machine for Linux (CentOS)
E. Data Science Virtual Machine for Windows 2012
Answer: D
NEW QUESTION # 515
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 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.
B. 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.
C. 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 indicating the month and year it was registered. Use this dataset for all experiments.
D. 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.
Answer: B
Explanation:
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 # 516
You create a binary classification model. The model is registered in an Azure Machine Learning workspace. You use the Azure Machine Learning Fairness SDK to assess the model fairness.
You develop a training script for the model on a local machine.
You need to load the model fairness metrics into Azure Machine Learning studio.
What should you do?
A. Upload the training script
B. Implement the creace_group_metric_sec function
C. Implement the download_dashboard_by_upload_id function
D. Implement the upload_dashboard_dictionary function
Answer: D
Explanation:
import azureml.contrib.fairness package to perform the upload:
from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id Reference: https://docs.microsoft.com/en-us ... arning-fairness-aml
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