- (Exam Topic 3)
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?

1. A. Synthetic Minority
2. B. Replace using Probabilistic PAC
3. C. Replace using MICE
4. D. Normalization

Correct Answer: B

- (Exam Topic 3)
You have a Python script that executes a pipeline. The script includes the following code: from azureml.core import Experiment
pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline) You want to test the pipeline before deploying the script.
You need to display the pipeline run details written to the STDOUT output when the pipeline completes. Which code segment should you add to the test script?

1. A. pipeline_run.get.metrics()
2. B. pipeline_run.wait_for_completion(show_output=True)
3. C. pipeline_param = PipelineParameter(name="stdout", default_value="console")
4. D. pipeline_run.get_status()

Correct Answer: B
wait_for_completion: Wait for the completion of this run. Returns the status object after the wait. Syntax: wait_for_completion(show_output=False, wait_post_processing=False, raise_on_error=True) Parameter: show_output
Indicates whether to show the run output on sys.stdout.

- (Exam Topic 3)
You are analyzing a dataset containing historical data from a local taxi company. You arc developing a regression a regression model.
You must predict the fare of a taxi trip.
You need to select performance metrics to correctly evaluate the- regression model. Which two metrics can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

1. A. an F1 score that is high
2. B. an R Squared value dose to 1
3. C. an R-Squared value close to 0
4. D. a Root Mean Square Error value that is high
5. E. a Root Mean Square Error value that is low
6. F. an F 1 score that is low.

Correct Answer: BE
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

- (Exam Topic 3)
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.

Solution:
Box 1: Sampling Create a sample of data
This option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
* 1. Add the Partition and Sample module to your experiment in Studio, and connect the dataset.
* 2. Partition or sample mode: Set this to Sampling.
* 3. Rate of sampling.
See box 2 below.
Box 2: 0
* 3. Rate of sampling. Random seed for sampling: Optionally, type an integer to use as a seed value.
This option is important if you want the rows to be divided the same way every time. The default value is 0, meaning that a starting seed is generated based on the system clock. This can lead to slightly different results each time you run the experiment.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A

- (Exam Topic 3)
You have several machine learning models registered in an Azure Machine Learning workspace. You must use the Fairlearn dashboard to assess fairness in a selected model.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Solution:
Graphical user interface, text, application Description automatically generated
Step 1: Select a model feature to be evaluated.
Step 2: Select a binary classification or regression model.
Register your models within Azure Machine Learning. For convenience, store the results in a dictionary,
which maps the id of the registered model (a string in name:version format) to the predictor itself. Example:
model_dict = {}
lr_reg_id = register_model("fairness_logistic_regression", lr_predictor) model_dict[lr_reg_id] = lr_predictor
svm_reg_id = register_model("fairness_svm", svm_predictor) model_dict[svm_reg_id] = svm_predictor
Step 3: Select a metric to be measured Precompute fairness metrics.
Create a dashboard dictionary using Fairlearn's metrics package. Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml

Does this meet the goal?

1. A. Yes
2. B. No

Correct Answer: A