DP-100 Online Practice Questions and Answers

Correct Answer:

Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.

Step 2: Add a dataset to the experiment

Step 3: Add a Split Data module to create training and test dataset.

To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.

Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.

Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/two-class-support-vector-machine

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

Correct Answer:

In decision trees, the depth of the tree determines the variance. A complicated decision tree (e.g. deep) has low bias and high variance.

Note: In statistics and machine learning, the bias–variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa. Increasing the bias will decrease the variance. Increasing the variance will decrease the bias.

References: https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/

Correct Answer:

Box 1: Yes

Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time

and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration

that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other

hyperparameter configurations.

Box 2: Yes

uniform(low, high) - Returns a value uniformly distributed between low and high

Box 3: No

Bayesian sampling does not currently support any early termination policy.

Reference:

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

Correct Answer: A

The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft's Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM. DSVM integrates with Azure Machine Learning.

Incorrect Answers:

B: Use Machine Learning Studio when you want to experiment with machine learning models quickly and easily, and the built-in machine learning algorithms are sufficient for your solutions.

References: https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

Correct Answer: E

output_action (str): How the output is to be organized. Currently supported values are 'append_row' and 'summary_only'.

1.

'append_row' ?All values output by run() method invocations will be aggregated into one unique file named parallel_run_step.txt that is created in the output location.

2.

'summary_only'

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/ azureml.contrib.pipeline.steps.parallelrunconfig

Correct Answer: D

When you compute model explanations and visualize them, you're not limited to an existing model explanation for an automated ML model. You can also get an explanation 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.

Incorrect Answers:

A: In the context of machine learning, data drift is the change in model input data that leads to model performance degradation. It is one of the top reasons where model accuracy degrades over time, thus monitoring data drift helps detect model performance issues.

B: A confusion matrix is used to describe the performance of a classification model. Each row displays the instances of the true, or actual class in your dataset, and each column represents the instances of the class that was predicted by the model.

C: Hyperparameters are adjustable parameters you choose for model training that guide the training process. The HyperDrive package helps you automate choosing these parameters.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl

Correct Answer: D

We recommend creating a datastore for an Azure Blob container. When you create a workspace, an Azure blob container and an Azure file share are automatically registered to the workspace.

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

Correct Answer: A

Featurization str or FeaturizationConfig

Values: 'auto' / 'off' / FeaturizationConfig

Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used.

Column type is automatically detected. Based on the detected column type preprocessing/featurization is done as follows:

Categorical: Target encoding, one hot encoding, drop high cardinality categories, impute missing values.

Numeric: Impute missing values, cluster distance, weight of evidence.

DateTime: Several features such as day, seconds, minutes, hours etc.

Text: Bag of words, pre-trained Word embedding, text target encoding.

Reference:

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

Correct Answer: B

Azure Machine Learning compute clusters is used for Batch inference. Run batch scoring on serverless compute. Supports normal and low-priority VMs. No support for real-time inference.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

Correct Answer: B

Manage packages Since your compute instance has multiple kernels, make sure use %pip or %conda magic functions, which install packages into the currently running kernel. Don't use !pip or !conda, which refers to all packages (including packages outside the currently running kernel).

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