test4

Question 1

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a
unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while
others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in
the review screen.
You have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-
learn. The script includes code to load a training data file which is also located in the scripts folder.
You must run the script as an Azure ML experiment on a compute cluster named aml-compute.
You need to configure the run to ensure that the environment includes the required packages for model training. You have
instantiated a variable named aml-compute that references the target compute cluster.
Solution: Run the following code:
from azureml.train.estimator import Estimator
sk_est = Estimator(source_directory=’./scripts’,
compute_target=aml-compute,
entry_script=’train.py’)

Does the solution meet the goal?

A. Yes
B. No

Question 2

You are building a recurrent neural network to perform a binary classification.
You review the training loss, validation loss, training accuracy, and validation accuracy for each training epoch.
You need to analyze model performance.
You need to identify whether the classification model is overfitted.
Which of the following is correct?

A. The training loss stays constant and the validation loss stays on a constant value and close to the training loss value when training the model.
B. The training loss decreases while the validation loss increases when training the model.
C. The training loss stays constant and the validation loss decreases when training the model.
D. The training loss increases while the validation loss decreases when training the model.

Question 3

You create a binary classification model by using Azure Machine Learning Studio.
You must tune hyperparameters by performing a parameter sweep of the model. The parameter sweep must meet the
following requirements:
iterate all possible combinations of hyperparameters minimize computing resources required to perform the sweep

You need to perform a parameter sweep of the model.
Which parameter sweep mode should you use?

A. Random sweep
B. Sweep clustering
C. Entire grid
D. Random grid

Question 4

HOTSPOT
You are running a training experiment on remote compute in Azure Machine Learning.
The experiment is configured to use a conda environment that includes the mlflow and azuremlcontrib-run packages.
You must use MLflow as the logging package for tracking metrics generated in the experiment.
You need to complete the script for the experiment.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:
Hot Area:
Answer Area
import numpy as np
# Import library to log metrics

from azureml.core import Run
import mlflow
import logging
# Start logging for this run

run = Run.get_context()
mlflow.start_run()
logger = logging.getLogger(“Run’)
reg_rate = 0.01
# Log the reg_rate metric

run.log(‘reg_rate’, np.float(reg_rate))
mlflow.log_metric(‘reg_rate’, np.float(reg_rate))
logger.info(np.float(reg_rate))
# Stop logging for this run

run.complete()
mlflow.end_run()
logger.setLevel(logging.INFO)