Exam DP-100 topic 4 question 17 discussion

Actual exam question from Microsoft's DP-100

Question #: 17
Topic #: 4

You use the following Python code in a notebook to deploy a model as a web service: from azureml.core.webservice import AciWebservice from azureml.core.model import InferenceConfig inference_config = InferenceConfig(runtime='python', source_directory='model_files', entry_script='score.py', conda_file='env.yml') deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1) service = Model.deploy(ws, 'my-service', [model], inference_config, deployment_config) service.wait_for_deployment(True)
The deployment fails.
You need to use the Python SDK in the notebook to determine the events that occurred during service deployment an initialization.
Which code segment should you use?

A. service.state
B. service.get_logs()
C. service.serialize()
D. service.environment

Show Suggested Answer

Suggested Answer: B 🗳️
The first step in debugging errors is to get your deployment logs. In Python: service.get_logs()
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

by nick234987 at Oct. 15, 2021, 6:42 p.m.

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james2033

7 months, 3 weeks ago

Selected Answer: B

from azureml.core.webservice import AciWebservice from azureml.core.model import InferenceConfig inference_config = InferenceConfig(runtime = 'python', source_directory = 'model_files', entry_script = 'score.py', conda_file = 'env.yml') deployment_config = AciWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1) service = Model.deploy(ws, 'my-service', [model], inference_config, deployment_config) service.wait_for_deployment(True) logging like this ml_client.online_deployments.get_logs(name = "<deployment-name>", endpoint_name = "<endpoint-name>", lines=100) https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints?view=azureml-api-2&tabs=python#get-container-logs

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