You can either define the pipeline using YAML syntax or through the user interface in Azure pipelineSelect one:TrueFalse

from azure.ai.ml.dsl import pipelinefrom azure.ai.ml import MLClientfrom azure.identity import DefaultAzureCredential, InteractiveBrowserCredentialfrom azure.ai.ml.entities import CommandComponent, PipelineComponent, Job, Componentfrom azure.ai.ml import PyTorchDistribution, Inputfrom azure.ai.ml.constants import AssetTypesfrom azure.ai.ml.entities import Datafrom azure.ai.ml.constants import AssetTypes, InputOutputModesfrom azure.ai.ml import Input

. You use Azure Machine Learning designer to create a training pipeline for a classification model. What must you do before deploying the model as a service? Create an inference pipeline from the training pipelineAdd an Evaluate Model module to the training pipelineClone the training pipeline with a different name

What's the best way to pause the pipeline until an approver signs off on a change? Use a release approval.Install a Marketplace extension that provides Azure Pipelines tasks that can pause the pipeline.Ask your approver to look at the change. Then manually trigger the pipeline to run.

Pipe uses a script that lives in a Docker containerSelect one:TrueFalse

. To deploy to Azure App Service, what resources does Azure Pipelines need? A build artifact and a service connection.A host name and an SSH key.A host name and the encrypted username and password.