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HOTSPOT You create an Azure Machine Learning dataset containing automobile price data The dataset includes 10,000 rows and 10 columns You use Azure Machine Learning Designer to transform the dataset by using an Execute Python Script component and custom code. The code must combine three columns to create a new column. You need to configure the code function. Which configurations should you use? lo answer, select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
DRAG DROP You create an Azure Machine Learning workspace and an Azure Synapse Analytics workspace with a Spark pool. The workspaces are contained within the same Azure subscription. You must manage the Synapse Spark pool from the Azure Machine Learning workspace. You need to attach the Synapse Spark pool in Azure Machine Learning by usinq the Python SDK v2. 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.
You use Azure Machine Learning studio to analyze an mltable data asset containing a decimal column named column1. You need to verify that the column1 values are normally distributed. Which statistic should you use?
You register a model that you plan to use in a batch inference pipeline. The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called. You need to configure the pipeline. Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?
HOTSPOT You create an Azure Machine Learning model to include model files and a scorning script. You must deploy the model. The deployment solution must meet the following requirements: * Provide near real-time inferencing. * Enable endpoint and deployment level cost estimates. * Support logging to Azure Log Analytics. You need to configure the deployment solution. What should you configure? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
You train and publish a machine teaming model. You need to run a pipeline that retrains the model based on a trigger from an external system. What should you configure?
You create a multi-class image classification deep learning model. You train the model by using PyTorch version 1.2. You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed. What should you do?
You plan to create a speech recognition deep learning model. The model must support the latest version of Python. You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM). What should you recommend?
you create an Azure Machine learning workspace named workspace1. The workspace contains a Python SOK v2 notebook mat uses Mallow to correct model coaxing men's anal arracks from your local computer. Vou must reuse the notebook to run on Azure Machine I earning compute instance m workspace. You need to comminute to log training and artifacts from your data science code. What should you do?
You deploy a real-time inference service for a trained model. The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates. You need to implement a monitoring solution for the deployed model using minimal administrative effort. What should you do?

Question 22 - DP-100 discussion

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HOTSPOT

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.


Question 22
Correct answer: Question 22

Explanation:

Box 1: ds-workstation notebook VM

An authenticated connection must not be required for testing.

On a Microsoft Azure virtual machine (VM), including a Data Science Virtual Machine (DSVM), you create local user accounts while provisioning the VM. Users then authenticate to the VM by using these credentials.

Box 2: gpu-compute cluster

Image classification is well suited for GPU compute clusters

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-common-identity

https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/training-deep-learning

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asked 02/10/2024
Jacquezz Shorter
23 questions
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