You manage an Azure Machine Learning workspace.
You plan to irain a natural language processing (NLP) tew classification model in multiple languages by using Azure Machine learning Python SDK v2. You need to configure the language of the text classification job by using automated machine learning. Which method of the TextClassifkationlob class should you use?
You create a Python script named train.py and save it in a folder named scripts. The script uses the scikit-learn framework to train a machine learning model.
You must run the script as an Azure Machine Learning experiment on your local workstation.
You need to write Python code to initiate an experiment that runs the train.py script.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You create an Azure Machine Learning workspace and a dataset. The dataset includes age values for a large group of diabetes patients. You use the dp.mean function from the SmartNoise library to calculate the mean of the age value. You store the value in a variable named age.mean.
You must output the value of the interval range of released mean values that will be returned 95 percent of the time.
You need to complete the code.
Which code values should you use? To answer, select the appropriate options in the answer area
NOTE: Each correct selection is worth one point.
You use the following code to run a script as an experiment in Azure Machine Learning:
You must identify the output files that are generated by the experiment run.
You need to add code to retrieve the output file names.
Which code segment should you add to the script?
You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training.
You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value.
You need to modify the inference pipeline to meet the requirement.
What should you do?
You have a dataset that is stored m an Azure Machine Learning workspace.
You must perform a data analysis for differentiate privacy by using the SmartNoise SDK.
You need to measure the distribution of reports for repeated queries to ensure that they are balanced
Which type of test should you perform?
You use the Azure Machine learning SDK foe Python to create a pipeline that includes the following step:
The output of the step run must be cached and reused on subsequent runs when the source.directory value has not changed.
You need to define the step.
What should you include in the step definition?
You manage an Azure Machine learning workspace. You develop a machine teaming model.
You are deploying the model to use a low-pointy VM mm a pacing discount.
You need to deploy the model.
Which compute large! should you use?
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 plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:
from azureml.core import Run
import pandas as pd
run = Run.get_context()
data = pd.read_csv('data.csv')
label_vals = data['label'].unique()
# Add code to record metrics here
run.complete()
The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.
You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.
Solution: Replace the comment with the following code:
run.upload_file('outputs/labels.csv', './data.csv')
Does the solution meet the goal?
You manage are Azure Machine Learning workspace by using the Python SDK v2.
You must create an automated machine learning job to generate a classification model by using data files stored in Parquet format. You must configure an auto scaling compute target and a data asset for the job.
You need to configure the resources for the job.
Which resource configuration should you use? to answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
You manage an Azure Machine Learning workspace. You build a model for which you must configure a Responsible Al dashboard. Based on what you learn from the dashboard, you must perform the following activities:
• Determine what must be done to get a desirable outcome from the model.
• Identify the features that have the most direct effect on your outcome of interest.
You need to select the components to use for the Responsible Al dashboard configuration. Which two components should you add? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.
You must use Hyperdrive to try combinations of the following hyperparameter values:
• learning_rate: any value between 0.001 and 0.1
• batch_size: 16, 32, or 64
You need to configure the search space for the Hyperdrive experiment.
Which two parameter expressions should you use? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
You use the Azure Machine Learning designer to create and run a training pipeline.
The pipeline must be run every night to inference predictions from a large volume of files. The folder where the files will be stored is defined as a dataset.
You need to publish the pipeline as a REST service that can be used for the nightly inferencing run.
What should you do?
You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website.
Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days. You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand. Which deployment compute option should you use?
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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:
Does the solution meet the goal?
