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DP-100 Practice Questions

Question # 1
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?

A. BatchCompute
B. AdlaCompute
C. AmlCompute
D. Aks Compute


C. AmlCompute

Explanation:

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), , , ]



Question # 2
You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events. Model performance has degraded due to model input data changes. You need to trigger a remediation ML pipeline based on an Azure Machine Learning event. Which event should you use?
A. RunStatusChanged
B. DatasetDriftDetected
C. ModelDeployed
D. RunCompleted


B. DatasetDriftDetected



Question # 3
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 # 4
You need to implement a model development strategy to determine a user’s tendency to respond to an ad. Which technique should you use?
A. Use a Relative Expression Split module to partition the data based on centroid distance.
B. Use a Relative Expression Split module to partition the data based on distance travelled to the event.
C. Use a Split Rows module to partition the data based on distance travelled to the event.
D. Use a Split Rows module to partition the data based on centroid distance.


A. Use a Relative Expression Split module to partition the data based on centroid distance.

Explanation:

Split Data partitions the rows of a dataset into two distinct sets.

The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.

Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.

Scenario:

Local market segmentation models will be applied before determining a user’s propensity to respond to an advertisement.

The distribution of features across training and production data are not consistent

References:

https://docs.microsoft.co m/en-us/azure/machine-learning/studio-module-reference/split-data



Question # 5
You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.

The code implements Bandit termination policy with slack_factor set to 02 and a sweep job with max_concurrent_trials set to 10.

You must increase effectiveness of the tuning process by improving sampling convergence. You need to select which sampling convergence to use. What should you select?

A. Set the value of slack. factor of earty. termination policy to 0.1.
B. Set the value of max_concurrent_trials to 4.
C. Set the value of slack_factor of eartyjermination policy to 0.9.
D. Set the value of max. concurrentjrials to 20.


B. Set the value of max_concurrent_trials to 4.



Question # 6
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 # 7
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 # 8
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?
A. Run
B. ScripcRunConfig
C. Workspace
D. Experiment


A. Run

Explanation:

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.

[Reference:, https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class), , ]


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