- (Exam Topic 3)
You run a script as an experiment in Azure Machine Learning.
You have a Run object named run that references the experiment run. You must review the log files that were generated during the experiment run.
You need to download the log files to a local folder for review.
Which two code segments can you run to achieve this goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

1. A. run.get_details()
2. B. run.get_file_names()
3. C. run.get_metrics()
4. D. run.download_files(output_directory='./runfiles')
5. E. run.get_all_logs(destination='./runlogs')

Correct Answer: AE
The run Class get_all_logs method downloads all logs for the run to a directory.
The run Class get_details gets the definition, status information, current log files, and other details of the run. Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)

- (Exam Topic 3)
You are developing a hands-on workshop to introduce Docker for Windows to attendees. You need to ensure that workshop attendees can install Docker on their devices.
Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

1. A. Microsoft Hardware-Assisted Virtualization Detection Tool
2. B. Kitematic
3. C. BIOS-enabled virtualization
4. D. VirtualBox
5. E. Windows 10 64-bit Professional

Correct Answer: CE
C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.
Ensure that hardware virtualization support is turned on in the BIOS settings. For example:

E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher. References:
https://docs.docker.com/toolbox/toolbox_install_windows/ https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/

- (Exam Topic 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?

1. A. Enable data drift monitoring for the model and its training dataset.
2. B. Score the model against some test data with known label values and use the results to calculate a confusion matrix.
3. C. Use the Hyperdrive library to test the model with multiple hyperparameter values.
4. D. Use the interpretability package to generate an explainer for the model.
5. E. Add tags to the model registration indicating the names of the features in the training dataset.

Correct Answer: D
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

- (Exam Topic 3)
You are creating a new Azure Machine Learning pipeline using the designer.
The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a website. You have not created a dataset for this file.
You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.
Which module should you add to the pipeline in Designer?

1. A. Convert to CSV
2. B. Enter Data Manually D
3. C. Import Data
4. D. Dataset

Correct Answer: D
The preferred way to provide data to a pipeline is a Dataset object. The Dataset object points to data that lives in or is accessible from a datastore or at a Web URL. The Dataset class is abstract, so you will create an instance of either a FileDataset (referring to one or more files) or a TabularDataset that's created by from one or more files with delimited columns of data.
Example:
from azureml.core import Dataset
iris_tabular_dataset = Dataset.Tabular.from_delimited_files([(def_blob_store, 'train-dataset/iris.csv')]) Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-your-first-pipeline

- (Exam Topic 3)
You are a data scientist building a deep convolutional neural network (CNN) for image classification. The CNN model you built shows signs of overfitting.
You need to reduce overfitting and converge the model to an optimal fit.
Which two actions should you perform? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

1. A. Reduce the amount of training data.
2. B. Add an additional dense layer with 64 input units
3. C. Add L1/L2 regularization.
4. D. Use training data augmentation
5. E. Add an additional dense layer with 512 input units.

Correct Answer: AC
References:
https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/ https://en.wikipedia.org/wiki/Convolutional_neural_network