DP-100 Exam Dumps - Microsoft Azure Questions and Answers

The entry script receives data submitted to a deployed web service and passes it to the model. It then takes the response returned by the model and returns that to the client. The script is specific to your model. It must understand the data that the model expects and returns.

The two things you need to accomplish in your entry script are:

Loading your model (using a function called init())

Running your model on input data (using a function called run())

The two steps are present: process_step and train_step

Note:

Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.

PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.

For example, the pipeline train step depends on the process_step_output output of the pipeline process step:

from azureml.pipeline.core import Pipeline, PipelineData

from azureml.pipeline.steps import PythonScriptStep

datastore = ws.get_default_datastore()

process_step_output = PipelineData("processed_data", datastore=datastore)

process_step = PythonScriptStep(script_name="process.py",

arguments=["--data_for_train", process_step_output],

outputs=[process_step_output],

compute_target=aml_compute,

source_directory=process_directory)

train_step = PythonScriptStep(script_name="train.py",

arguments=["--data_for_train", process_step_output],

inputs=[process_step_output],

compute_target=aml_compute,

source_directory=train_directory)

pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])

A Deep Learning Virtual Machine is a pre-configured environment for deep learning using GPU instances.

References:

https://azuremarketplace.microsoft.com/en-au/marketplace/apps/microsoft-ads.dsvm-deep-learning

Box 1: Yes

In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.

Box 2: Yes

learning_rate has a normal distribution with mean value 10 and a standard deviation of 3.

Box 3: No

keep_probability has a uniform distribution with a minimum value of 0.05 and a maximum value of 0.1.

Box 4: No

number_of_hidden_layers takes on one of the values [3, 4, 5].