You create a Python script that runs a training experiment in Azure Machine Learning. The script uses the Azure Machine Learning SDK for Python.
You must add a statement that retrieves the names of the logs and outputs generated by the script.
You need to reference a Python class object from the SDK for the statement.
Which class object should you use?
Answer:
A
A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.
The run Class get_all_logs method downloads all logs for the run to a directory.
Incorrect Answers:
A: A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.
B: A ScriptRunConfig packages together the configuration information needed to submit a run in Azure ML, including the script, compute target, environment, and any distributed job-specific configs.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)
You run a script as an experiment in Azure Machine Learning.
You have a Run object named run that references the experiment run. You must review the log files that were generated during the experiment run.
You need to download the log files to a local folder for review.
Which two code segments can you run to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer:
AE
The run Class get_all_logs method downloads all logs for the run to a directory.
The run Class get_details gets the definition, status information, current log files, and other details of the run.
Incorrect Answers:
B: The run get_file_names list the files that are stored in association with the run.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)
You have the following code. The code prepares an experiment to run a script:
The experiment must be run on local computer using the default environment.
You need to add code to start the experiment and run the script.
Which code segment should you use?
Answer:
D
The experiment class submit method submits an experiment and return the active created run.
Syntax: submit(config, tags=None, **kwargs)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment
You use the following code to define the steps for a pipeline: from azureml.core import Workspace, Experiment, Run from azureml.pipeline.core import Pipeline from azureml.pipeline.steps import PythonScriptStep ws = Workspace.from_config()
. . .
step1 = PythonScriptStep(name="step1", ...)
step2 = PythonScriptsStep(name="step2", ...)
pipeline_steps = [step1, step2]
You need to add code to run the steps.
Which two code segments can you use to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer:
CD
After you define your steps, you build the pipeline by using some or all of those steps.
# Build the pipeline. Example:
pipeline1 = Pipeline(workspace=ws, steps=[compare_models])
# Submit the pipeline to be run
pipeline_run1 = Experiment(ws, 'Compare_Models_Exp').submit(pipeline1)
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-machine-learning-pipelines
HOTSPOT -
You create an Azure Databricks workspace and a linked Azure Machine Learning workspace.
You have the following Python code segment in the Azure Machine Learning workspace: import mlflow import mlflow.azureml import azureml.mlflow import azureml.core from azureml.core import Workspace subscription_id = 'subscription_id' resourse_group = 'resource_group_name' workspace_name = 'workspace_name' ws = Workspace.get(name=workspace_name, subscription_id=subscription_id, resource_group=resource_group) experimentName = "/Users/{user_name}/{experiment_folder}/{experiment_name}" mlflow.set_experiment(experimentName) uri = ws.get_mlflow_tracking_uri() mlflow.set_tracking_uri(uri)
Instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:
Answer:
Box 1: No -
The Workspace.get method loads an existing workspace without using configuration files. ws = Workspace.get(name="myworkspace", subscription_id='<azure-subscription-id>', resource_group='myresourcegroup')
Box 2: Yes -
MLflow Tracking with Azure Machine Learning lets you store the logged metrics and artifacts from your local runs into your Azure Machine Learning workspace.
The get_mlflow_tracking_uri() method assigns a unique tracking URI address to the workspace, ws, and set_tracking_uri() points the MLflow tracking URI to that address.
Box 3: Yes -
Note: In Deep Learning, epoch means the total dataset is passed forward and backward in a neural network once.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.workspace.workspace https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow
You create and register a model in an Azure Machine Learning workspace.
You must use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStep to score input data using the model. You must specify a value for the ParallelRunConfig compute_target setting of the pipeline step.
You need to create the compute target.
Which class should you use?
Answer:
C
Compute target to use for ParallelRunStep. This parameter may be specified as a compute target object or the string name of a compute target in the workspace.
The compute_target target is of AmlCompute or string.
Note: An Azure Machine Learning Compute (AmlCompute) is a managed-compute infrastructure that allows you to easily create a single or multi-node compute.
The compute is created within your workspace region as a resource that can be shared with other users
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunconfig https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class)
DRAG DROP -
You previously deployed a model that was trained using a tabular dataset named training-dataset, which is based on a folder of CSV files.
Over time, you have collected the features and predicted labels generated by the model in a folder containing a CSV file for each month. You have created two tabular datasets based on the folder containing the inference data: one named predictions-dataset with a schema that matches the training data exactly, including the predicted label; and another named features-dataset with a schema containing all of the feature columns and a timestamp column based on the filename, which includes the day, month, and year.
You need to create a data drift monitor to identify any changing trends in the feature data since the model was trained. To accomplish this, you must define the required datasets for the data drift monitor.
Which datasets should you use to configure the data drift monitor? To answer, drag the appropriate datasets to the correct data drift monitor options. Each source may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
Answer:
Box 1: training-dataset -
Baseline dataset - usually the training dataset for a model.
Box 2: predictions-dataset -
Target dataset - usually model input data - is compared over time to your baseline dataset. This comparison means that your target dataset must have a timestamp column specified.
The monitor will compare the baseline and target datasets.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-monitor-datasets
You plan to run a Python script as an Azure Machine Learning experiment.
The script contains the following code:
You must specify a file dataset as an input to the script. The dataset consists of multiple large image files and must be streamed directly from its source.
You need to write code to define a ScriptRunConfig object for the experiment and pass the ds dataset as an argument.
Which code segment should you use?
Answer:
A
If you have structured data not yet registered as a dataset, create a TabularDataset and use it directly in your training script for your local or remote experiment.
To load the TabularDataset to pandas DataFrame
df = dataset.to_pandas_dataframe()
Note: TabularDataset represents data in a tabular format created by parsing the provided file or list of files.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-with-datasets
You have a Jupyter Notebook that contains Python code that is used to train a model.
You must create a Python script for the production deployment. The solution must minimize code maintenance.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer:
AC
C: Python main function is a starting point of any program. When the program is run, the python interpreter runs the code sequentially. Main function is executed only when it is run as a Python program.
A: Refactoring, code style and testing
The first step is to modularise the notebook into a reasonable folder structure, this effectively means to convert files from .ipynb format to .py format, ensure each script has a clear distinct purpose and organise these files in a coherent way.
Once the project is nicely structured we can tidy up or refactor the code.
Reference:
https://www.guru99.com/learn-python-main-function-with-examples-understand-main.html https://towardsdatascience.com/from-jupyter-notebook-to-deployment-a-straightforward-example-1838c203a437
HOTSPOT -
You use an Azure Machine Learning workspace.
You create the following Python 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.
Hot Area:
Answer:
Box 1: No -
Environment is a required parameter. The environment to use for the run. If no environment is specified, azureml.core.runconfig.DEFAULT_CPU_IMAGE will be used as the Docker image for the run.
The following example shows how to instantiate a new environment. from azureml.core import Environment myenv = Environment(name="myenv")
Box 2: Yes -
Parameter compute_target: The compute target where training will happen. This can either be a ComputeTarget object, the name of an existing ComputeTarget, or the string "local". If no compute target is specified, your local machine will be used.
Box 3: Yes -
Parameter source_directory. A local directory containing code files needed for a run.
Parameter script. The file path relative to the source_directory of the script to be run.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.scriptrunconfig https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.environment.environment
