DP-100 Online Practice Questions and Answers

Correct Answer:

Box 1: Yes

The compute is created within your workspace region as a resource that can be shared with other users.

Box 2: Yes

It is displayed as a compute cluster.

View compute targets

1.

To see all compute targets for your workspace, use the following steps:

2.

Navigate to Azure Machine Learning studio.

3.

Under Manage, select Compute.

4.

Select tabs at the top to show each type of compute target.

Box 3: Yes

min_nodes is not specified, so it defaults to 0.

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.amlcomputeprovisioningconfiguration

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

Correct Answer:

Box 1: forcasting

Task: The type of task to run. Values can be 'classification', 'regression', or 'forecasting' depending on the type of automated ML problem to solve.

Box 2: temperature

The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).

Box 3: observation_time

time_column_name: The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. This setting is being deprecated.

Please use forecasting_parameters instead.

Box 4: 7

"predicts temperature over the next seven days"

max_horizon: The desired maximum forecast horizon in units of time-series frequency. The default value is 1.

Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster should predict out. When task type is forecasting, this parameter is required.

Box 5: 50

"For the initial round of training, you want to train a maximum of 50 different models."

Iterations: The total number of different algorithm and parameter combinations to test during an automated ML experiment.

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig

Correct Answer: A

The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.

Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.

Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.

Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.

Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words:

ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.

Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be

used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.

AUC.

Reference:

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

Correct Answer: B

Those are metrics for evaluating classification models, instead use: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/evaluate-model

Correct Answer: B

label_vals has the unique labels (from the statement label_vals = data['label'].unique()), and it has to be logged.

Note:

Instead use the run_log function to log the contents in label_vals:

for label_val in label_vals: run.log('Label Values', label_val)

Reference: https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

Correct Answer: D

Correct Answer: C

Roles and requirements for Azure Data Share With Azure Data Share service, you can share data without exchanging credentials between data provider and consumer. For snapshot-based sharing, Azure Data Share service uses Managed Identities (previously known as MSIs) to authenticate to Azure data store. Azure Data Share resource's managed identity needs to be granted access to Azure data store to read or write data.

Reference: https://learn.microsoft.com/en-us/azure/data-share/concepts-roles-permissions

Correct Answer: B

ScriptRunConfig Class

Represents configuration information for submitting a training run in Azure Machine Learning.

Constructor

ScriptRunConfig(source_directory, script=None, arguments=None, run_config=None, _telemetry_values=None, compute_target=None, environment=None, distributed_job_config=None, resume_from=None,

max_run_duration_seconds=2592000, command=None, docker_runtime_config=None)

Parameters include:

arguments

list or str

Required

Optional command line arguments to pass to the training script. Arguments are passed in pairs, for example, ['璦rg1', arg1_val, '璦rg2', arg2_val].

Example:

# configure and submit your training run

config = ScriptRunConfig(source_directory='.',

script='train.py',

arguments=['--arg1', arg1_val, '--arg2', arg2_val],

compute_target=cluster,

environment=env)

Incorrect:

Not A:

Parameter _telemetry_values

dict

Required

Internal use only.

Reference:

https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.script_run_config.scriptrunconfig

Correct Answer: D

Track runs from your local machine or remote compute.

Tracking using MLflow with Azure Machine Learning lets you store the logged metrics and artifacts runs that were executed on your local machine into your Azure Machine Learning workspace.

Set up tracking environment

To track a run that is not running on Azure Machine Learning compute (from now on referred to as "local compute"), you need to point your local compute to the Azure Machine Learning MLflow Tracking URI.

Note:

You can get the Azure Machine Learning MLflow tracking URI using the Azure Machine Learning SDK v1 for Python. Ensure you have the library azureml-sdk installed in the cluster you are using. The following sample gets the unique

MLFLow tracking URI associated with your workspace. Then the method set_tracking_uri() points the MLflow tracking URI to that URI.

1.

Using the workspace configuration file:

ws = Workspace.from_config() mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())

2.

Using the subscription ID, resource group name and workspace name:

from azureml.core import Workspace

import mlflow

#Enter details of your Azure Machine Learning workspace

subscription_id = ''

resource_group = ''

workspace_name = ''

ws = Workspace.get(name=workspace_name,

subscription_id=subscription_id,

resource_group=resource_group)

mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())

Reference: https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-use-mlflow

Correct Answer: C

Epsilon: Put simplistically, epsilon is a non-negative value that provides an inverse measure of the amount of noise added to the data. A low epsilon results in a dataset with a greater level of privacy, while a high epsilon results in a dataset that is closer to the original data. Generally, you should use epsilon values between 0 and 1. Epsilon is correlated with another value named delta, that indicates the probability that a report generated by an analysis is not fully private.

Note: Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private) the data is.

Reference: https://github.com/MicrosoftLearning/mslearn-dp100/blob/main/13%20-%20Explore%20Differential%20Privacy.ipynb https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy