DP-100 day 3

You are solving a classification task. The dataset is imbalanced. You need to select an Azure Machine Learning Studio module to improve the classification accuracy. Which module should you use?

• Permutation Feature Importance

• Filter Based Feature Selection

• Fisher Linear Discriminant Analysis

• Synthetic Minority Oversampling Technique (SMOTE)

You are using the Azure Machine Learning Python SDK to write code for an experiment. You need to record metrics from each run of the experiment, and be able to retrieve them easily from each run. What should you do?

• Add print statements to the experiment code to print the metrics.

• Use the log methods of the Run class to record named metrics.

• Save the experiment data in the outputs folder

You want to use a script-based experiment to train a PyTorch model, setting the batch size and learning rate hyperparameters to different values each time the experiment runs. What should you do?

• Create multiple script files – one for each batch size and learning rate combination you want to use

• Set the batch_size and learning_rate properties of the ScriptRunConfig before running the experiment

• Add arguments for batch size and learning rate to the script, and set them in the arguments property of the ScriptRunConfig

You are creating a machine learning model. You have a dataset that contains null rows. You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset. Which parameter should you use?

• Replace with mean

• Remove entire column

• Remove entire row

• Hot Deck

• Custom substitution value

You are evaluating a Python NumPy array that contains six data points defined as follows: data = [10, 20, 30, 40, 50, 60].

You must generate the following output by using the k-fold algorithm implantation in the Python Scikit-learn machine learning library: train: [10 40 50 60], test: [20 30] train: [20 30 40 60], test: [10 50] train: [10 20 30 50], test: [40 60]

You need to implement a cross-validation to generate the output.

How should you complete the code segment?

from numpy import array

from sklearn.model_selection import ......................

• K-means

• K-Fold

• Cross Validation

• Model Selection

Use the code in an image to run a script as an experiment in Azure Machine Learning:

You must identify the output files that are generated by the experiment run.You need to add code to retrieve the output file names.Which code segment should you add to the script?

• files = run.get_properties()

• files= run.get_file_names()

• files = run.get_details_with_logs()

• files = run.get_metrics()

• files = run.get_details()

You create a batch inference pipeline by using the Azure ML SDK. You configure the pipeline parameters by executing the code in an image:

You need to obtain the output from the pipeline execution. Where will you find the output?

• The digit_identification.py script

• The debug log

• The Activity Log in the Azure portal for the Machine Learning workspace

• The Inference Clusters tab in Machine Learning studio

• A file named parallel_run_step.txt located in the output folder

You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment. You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets. You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort. What should you do?

• Do not specify an environment in the run configuration for the experiment. Run the experiment by using the default environment.

• Create a virtual machine (VM) with the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.

• Create and register an Environment that includes the required packages. Use this Environment for all experiment runs.

• Create a config.yaml file defining the conda packages that are required and save the file in the experiment folder.

• Always run the experiment with an Estimator by using the default packages.

you can upload local files to the run's outputs folder by using the Run object's ..................................... method

• store_file

• mount_file

• upload_file

• download_file

You can able to start mlflow experiment using

• mlflow.start_run()

• mlflow.get_experiment()

• mlflow.init_run()

• mlflow.start_experiment()