- (Exam Topic 3)
You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images. You must meet the following requirements:
• Reduce the number of training epochs.
• Reduce the size of the neural network.
• Reduce over-fitting of the neural network.
You need to select the image modification values.
Which value should you use? To answer, select the appropriate Options in the answer area. NOTE: Each correct selection is worth one point.
Solution:
Does this meet the goal?
Correct Answer:
A
- (Exam Topic 3)
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 plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:
from azureml.core import Run import pandas as pd
run = Run.get_context()
data = pd.read_csv('data.csv') label_vals = data['label'].unique()
# Add code to record metrics here run.complete()
The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.
You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.
Solution: Replace the comment with the following code: for label_val in label_vals:
run.log('Label Values', label_val) Does the solution meet the goal?
Correct Answer:
A
The run_log function is used to log the contents in label_vals: for label_val in label_vals:
run.log('Label Values', label_val) Reference:
https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai
- (Exam Topic 3)
You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size.
You have the following requirements:
Models must be built using Caffe2 or Chainer frameworks.
Data scientists must be able to use a data science environment to build the machine learning pipelines and train models on their personal devices in both connected and disconnected network environments. Personal devices must support updating machine learning pipelines when connected to a network. You need to select a data science environment.
Which environment should you use?
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.
- (Exam Topic 3)
You create and register a model in an Azure Machine Learning workspace.
You must use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStep to score input data using the model. You must specify a value for the ParallelRunConfig compute_target setting of the pipeline step.
You need to create the compute target. Which class should you use?
Correct Answer:
C
Compute target to use for ParallelRunStep. This parameter may be specified as a compute target object or the string name of a compute target in the workspace.
The compute_target target is of AmlCompute or string.
Note: An Azure Machine Learning Compute (AmlCompute) is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parall https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class)
- (Exam Topic 3)
You create an Azure Machine Learning workspace and set up a development environment. You plan to train a deep neural network (DNN) by using the Tensorflow framework and by using estimators to submit training scripts.
You must optimize computation speed for training runs.
You need to choose the appropriate estimator to use as well as the appropriate training compute target configuration.
Which values should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
Solution:
Box 1: Tensorflow
TensorFlow represents an estimator for training in TensorFlow experiments. Box 2: 12 vCPU, 112 GB memory..,2 GPU,..
Use GPUs for the deep neural network. Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn
Does this meet the goal?
Correct Answer:
A