Question # 1
| You arc creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data module to handle the missing data.
You need to select a data cleaning method.
Which method should you use? | | A. Synthetic Minority | | B. Replace using Probabilistic PAC | | C. Replace using MICE | | D. Normalization |
B. Replace using Probabilistic PAC
Question # 2
| You use Azure Machine Learning to train a model based on a dataset named dataset1.
You define a dataset monitor and create a dataset named dataset2 that contains new data.
You need to compare dataset1 and dataset2 by using the Azure Machine Learning SDK for Python.
Which method of the DataDriftDetector class should you use? | | A. run | | B. get | | C. backfill | | D. update |
C. backfill
Explanation:
A backfill run is used to see how data changes over time.
[Reference:, https://docs.microsoft.com/en-us/python/api/azureml-datadrift/azureml.datadrift.datadriftdetector.datadriftdetector, , ]
Question # 3
| You use Azure Machine Learning studio to analyze an mltable data asset containing a decimal column named column1. You need to verify that the column1 values are normally distributed.
Which statistic should you use? | | A. Max | | B. Type | | C. Profile | | D. Mean |
C. Profile
Question # 4
You manage an Azure Machine learning workspace. The workspace includes an Azure Machine Learning kubernetes compute target configured as an Azure Kubemetes Service (AKS) cluster named AKS1 AKS1 is configured to enable the targeting of different nodes to train workloads.
You must run a command job on AK51 by using the Azure ML Python SDK v2? The command job must select different types of compute nodes. The compare node types must be specified by using a command parameter.
You need to configure the command parameter.
Which parameter should you use?
| | A. compute | | B. environment | | C. instance_type | | D. limits |
Explanation:
from azure.ai.ml import command
# define the command
command_job = command(
command="python -c "print('Hello world!')"",
environment="AzureML-lightgbm-3.2-ubuntu18.04-py37-cpu@latest",
compute="",
instance_type=""
Question # 5
| You train and register a model in your Azure Machine Learning workspace.
You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.
You need to create the inferencing script for the ParallelRunStep pipeline step.
Which two functions should you include? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point. | | A. run(mini_batch) | | B. main() | | C. batch() | | D. init() | | E. score(mini_batch) |
A. run(mini_batch)
D. init()
Explanation:
[Reference:, https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learningpipelines/parallel-run, , ]
Question # 6
| You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan.
You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives.
You need to determine the extent to which each feature in the customer data is influencing predictions.
What should you do? | | A. Enable data drift monitoring for the model and its training dataset. | | B. Score the model against some test data with known label values and use the results to calculate a
confusion matrix. | | C. Use the Hyperdrive library to test the model with multiple hyperparameter values. | | D. Use the interpretability package to generate an explainer for the model. | | E. Add tags to the model registration indicating the names of the features in the training dataset. |
D. Use the interpretability package to generate an explainer for the model.
Explanation:
When you compute model explanations and visualize them, you're not limited to an existing model explanation for an automated ML model. You can also get an explanation for your model with different test data. The steps in this section show you how to compute and visualize engineered feature importance based on your test data.
[Reference:, https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl, , , , ]
Question # 7
You are building a binary classification model by using a supplied training set.
The training set is imbalanced between two classes.
You need to resolve the data imbalance.
What are three possible ways to achieve this goal? Each correct answer presents a complete solution NOTE: Each correct selection is worth one point.
| | A. Penalize the classification | | B. Resample the data set using under sampling or oversampling | | C. Generate synthetic samples in the minority class. | | D. Use accuracy as the evaluation metric of the model. | | E. Normalize the training feature set. |
A. Penalize the classification
B. Resample the data set using under sampling or oversampling
D. Use accuracy as the evaluation metric of the model.
Explanation:
References:
https://machinelearningmastery.com/tactics -to-combat-imbalanced-classes-in-your-machine-learning-dataset/
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