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Flashcards in Developing Models in Microsoft Azure Deck (10)
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1

What is the purpose of an early termination policy?

To truncate the experiment and save computing resources

To truncate the workspace and save computing resources

To truncate under-performing run and save computing resources

To kill the run instance.

To truncate under-performing run and save computing resources

2

One of the exit criteria in your automated machine learning experiment is experiment_timeout_minutes. What does this setting allow you to define?

How long the environment associated with the experiment will remain active

How long each run in an experiment can continue to run

How long the compute target associated with the experiment will remain active

How long an experiment can continue in run

How long an experiment can continue in run

3

Which parameter is not needed to create Estimators?

A reference to computing resources

A script parameter and its location

A reference to workspace

Execution environment and its dependencies

A reference to workspace

4

Which property of Run objects is needed to get a reference to it?

Run duration property

The Run ID property

Run number property

Run name property

The Run ID property

5

You are starting a new experiment in which you only need computing resources until the training run, and would like to be decommissioned after the run. What is an ideal candidate?

Run-based managed compute

Cloud-based VM

Attached compute

Persisted managed compute

Run-based managed compute

6

While creating a Machine Learning workspace, which resource is not created?

Microsoft Key Vault

Microsoft compute target

Microsoft Insight

Microsoft Storage

Microsoft compute target

7

What Machine Learning environment is not supported by Microsoft Azure?

User-managed

System-managed

Remotely managed

Curated

Remotely managed

8

You are working on preprocessing data as part of your model creation. You need to remove all the columns from your data except columns date, age, ssn and phone. What method would you use?

data.remove_columns(['date','age','ssn','phone'])

data.retain_columns(['date','age','ssn','phone'])

data.extract_columns(['date','age','ssn','phone'])

data.keep_columns(['date','age','ssn','phone'])

data.keep_columns(['date','age','ssn','phone'])

9

You are working on a dataset and as part of your preprocessing, you need to retain data for employees whose income is less than 100,000. What will achieve this?

data.remove(data['income'] > 100000)

data.split(data['income'] < 100000)

data.filter(data['income'] < 100000)

data.filter(data['income'] > 100000)

data.filter(data['income'] < 100000)

10

Which is not a sampling strategy supported by Microsoft Azure?

Random sampling

Stratified sampling

Bayesian sampling

Grid sampling

Stratified sampling