At ValidExamDumps, we consistently monitor updates to the Microsoft DP-100 exam questions by Microsoft. Whenever our team identifies changes in the exam questions,exam objectives, exam focus areas or in exam requirements, We immediately update our exam questions for both PDF and online practice exams. This commitment ensures our customers always have access to the most current and accurate questions. By preparing with these actual questions, our customers can successfully pass the Microsoft Designing and Implementing a Data Science Solution on Azure exam on their first attempt without needing additional materials or study guides.
Other certification materials providers often include outdated or removed questions by Microsoft in their Microsoft DP-100 exam. These outdated questions lead to customers failing their Microsoft Designing and Implementing a Data Science Solution on Azure exam. In contrast, we ensure our questions bank includes only precise and up-to-date questions, guaranteeing their presence in your actual exam. Our main priority is your success in the Microsoft DP-100 exam, not profiting from selling obsolete exam questions in PDF or Online Practice Test.
You manage an Azure Machine Learning workspace. The Pylhon scrip! named scriptpy reads an argument named training_dat
a. The trainlng.data argument specifies the path to the training data in a file named datasetl.csv.
You plan to run the scriptpy Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the datasct as a parameter value when you submit the script as a training job.
Solution: python script.py --training_data dataset1,csv
Does the solution meet the goal?
You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.
You have the following data available for model building:
Video recordings of sporting events
Transcripts of radio commentary about events
Logs from related social media feeds captured during sporting events
You need to select an environment for creating the model.
Which environment should you use?
Azure Cognitive Services expand on Microsoft's evolving portfolio of machine learning APIs and enable developers to easily add cognitive features -- such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding -- into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure Cognitive Services can be categorized into five main pillars - Vision, Speech, Language, Search, and Knowledge.
https://docs.microsoft.com/en-us/azure/cognitive-services/welcome
You train and register an Azure Machine Learning model
You plan to deploy the model to an online endpoint
You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.
Solution:
Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.
Does the solution meet the goal?
You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.
You must use a Responsible Al dashboard to assess the model.
You need to use the Azure Machine Learning studio Ul to generate the Responsible A dashboard.
What should you do first?
You use Azure Machine Learning designer to create a training pipeline for a regression model.
You need to prepare the pipeline for deployment as an endpoint that generates predictions asynchronously for a dataset of input data values.
What should you do?
You must first convert the training pipeline into a real-time inference pipeline. This process removes training modules and adds web service inputs and outputs to handle requests.
Incorrect Answers:
A: Use the Enter Data Manually module to create a small dataset by typing values.
https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy
