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| Section | Objectives |
|---|---|
| Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
| Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Train machine learning models | - Train models using Azure Machine Learning - Tune hyperparameters and evaluate models |
| Design and prepare a machine learning solution | - Select appropriate Azure services for machine learning workloads - Manage compute and data assets - Plan and configure Azure Machine Learning workspace |
| Explore and analyze data | - Perform exploratory data analysis - Ingest and prepare data for modeling |
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NEW QUESTION # 188
You plan to use Hyperdrive to optimize the hyperparameters selected when training a model. You create the following code to define options for the hyperparameter experiment
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: No
max_total_runs (50 here)
The maximum total number of runs to create. This is the upper bound; there may be fewer runs when the sample space is smaller than this value.
Box 2: Yes
Policy EarlyTerminationPolicy
The early termination policy to use. If None - the default, no early termination policy will be used.
Box 3: No
Discrete hyperparameters are specified as a choice among discrete values. choice can be:
one or more comma-separated values
a range object
any arbitrary list object
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.hyperdriveconfig
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters
NEW QUESTION # 189
You are retrieving data from a large datastore by using Azure Machine Learning Studio.
You must create a subset of the data for testing purposes using a random sampling seed based on the system clock.
You add the Partition and Sample module to your experiment.
You need to select the properties for the module.
Which values should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample
NEW QUESTION # 190
You create a script for training a machine learning model in Azure Machine Learning service.
You create an estimator by running the following code:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 191
You tram and register a model by using the Azure Machine Learning Python SDK v2 in a local workstation. Python 3.7 and Visual Studio Code are instated on the workstation.
When you try to deploy the model into production to a Kubernetes online endpoint you experience an error in the scoring script that causes deployment to fail.
You need to debug the service on the local workstation before deploying the service to production.
Which three actions should you perform m sequence? To answer, move the appropriate actions from the list of actions from the answer area and arrange them in the correct order.
Answer:
Explanation:
1 - install Docker on the worstation.
2 - Run teh begin_create_or_update method of an MLClient class instance with the local parameter set to true.
3 - Debug and modify the scoring script as necessary.
NEW QUESTION # 192
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:
Does the solution meet the goal?
Answer: A
Explanation:
Explanation
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]) Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azu
NEW QUESTION # 193
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