DP-100 Online Practice Questions and Answers

Correct Answer:

Box 1: 300

You type 300 (%), the module triples the percentage of minority cases (3000) compared to the original dataset (1000).

Box 2: 5

We should use 5 data rows.

Use the Number of nearest neighbors option to determine the size of the feature space that the SMOTE algorithm uses when in building new cases. A nearest neighbor is a row of data (a case) that is very similar to some target case. The

distance between any two cases is measured by combining the weighted vectors of all features.

By increasing the number of nearest neighbors, you get features from more cases.

By keeping the number of nearest neighbors low, you use features that are more like those in the original sample.

References:

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

Correct Answer:

Box 1: Yes

In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.

Box 2: Yes

learning_rate has a normal distribution with mean value 10 and a standard deviation of 3.

Box 3: No

keep_probability has a uniform distribution with a minimum value of 0.05 and a maximum value of 0.1.

Box 4: No

number_of_hidden_layers takes on one of the values [3, 4, 5].

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

Correct Answer:

Box 1: import mlflow

Import the mlflow and Workspace classes to access MLflow's tracking URI and configure your workspace.

Box 2: mlflow.start_run()

Set the MLflow experiment name with set_experiment() and start your training run with start_run().

Box 3: mlflow.log_metric(' ..')

Use log_metric() to activate the MLflow logging API and begin logging your training run metrics.

Box 4: mlflow.end_run()

Close the run:

run.endRun()

Reference:

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

Correct Answer:

Box 1: Regression

Regression is a supervised machine learning technique used to predict numeric values.

Box 2: Price

Reference:

https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer

Correct Answer:

Box 1: Regenerate the keys of account1.

Azure Blob Storage support authentication through Account key or SAS token.

To authenticate your access to the underlying storage service, you can provide either your account key, shared access signatures (SAS) tokens, or service principal

Box 2: Update the authentication for store1.

For Azure Machine Learning studio users, several features rely on the ability to read data from a dataset; such as dataset previews, profiles and automated machine learning. For these features to work with storage behind virtual networks,

use a workspace managed identity in the studio to allow Azure Machine Learning to access the storage account from outside the virtual network.

Note: Some of the studio's features are disabled by default in a virtual network. To re-enable these features, you must enable managed identity for storage accounts you intend to use in the studio.

The following operations are disabled by default in a virtual network:

Preview data in the studio.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data

Correct Answer:

Step 1: Define a cross-entropy function activation When using a neural network to perform classification and prediction, it is usually better to use cross-entropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network.

Step 2: Add cost functions for each target state.

Step 3: Evaluated the distance error metric.

References: https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/

Correct Answer: E

Example:

data_folder = args.data_folder

# Load Train and Test data

train_data = pd.read_csv(os.path.join(data_folder, 'data.csv'))

Reference:

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

Correct Answer: A

Leave One Out (LOO) cross-validation

Setting K = n (the number of observations) yields n-fold and is called leave-one out cross-validation (LOO), a special case of the K-fold approach.

LOO CV is sometimes useful but typically doesn't shake up the data enough. The estimates from each fold are highly correlated and hence their average can have high variance.

This is why the usual choice is K=5 or 10. It provides a good compromise for the bias-variance tradeoff.

Correct Answer: BC

Correct Answer: BCE

These actions will reset the notebook state and will reset all variables in the notebook. Action, Result

*

(B) Switch compute

Notebook automatically uses the new compute.

*

(C) Reset compute

Starts again when you try to run a cell

*

(E) Change the kernel Notebook uses new kernel

*

Stop compute No cells will run

*

Open notebook in Jupyter or JupyterLab Notebook opened in a new tab.

Incorrect:

These actions won't change the notebook state or the values of any variables in the notebook:

Action, Result

*

(not A) Stop the kernel

Stops any running cell. Running a cell will automatically restart the kernel.

*

(not D) Navigate to another workspace section

Running cells are stopped.

Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-run-jupyter-notebooks