DP-100 final quiz

You are evaluating a completed binary classification machine.

You need to use the precision as the evaluation metric.

Which visualization should you use?

• coefficient of determination

• scatter plot

• Gradient descent

• Receiver Operating Characteristic (ROC) curve

You use the Azure Machine Learning Python SDK to define a pipeline to train a model.

The data used to train the model is read from a folder in a datastore.

You need to ensure the pipeline runs automatically whenever the data in the folder changes.

What should you do?

• Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder containing the training data in the path_on_datascore property

• Create a PipelineParameter with a default value that references the location where the training data is stored

• Create a ScheduleRecurrance object with a Frequency of auto. Use the object to create a Schedule for the pipeline

• Set the regenerate_outputs property of the pipeline to True

You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-class image classification deep learning model that uses a set of labeled bird photographs collected by experts.

You have 100,000 photographs of birds. All photographs use the JPG format and are stored in an Azure blob container in an Azure subscription.

You need to access the bird photograph files in the Azure blob container from the Azure Machine Learning service workspace that will be used for deep learning model training. You must minimize data movement.

What should you do?

• Create and register a dataset by using TabularDataset class that references the Azure blob storage containing bird photographs.

• Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.

• Copy the bird photographs to the blob datastore that was created with your Azure Machine Learning service workspace.

• Create an Azure Data Lake store and move the bird photographs to the store

You use Azure Machine Learning Studio to build a machine learning experiment.

You need to divide data into two distinct datasets.

Which module should you use?

• Group Data into Bins

• Split Data

• Load Trained Model

• Assign Data to Clusters

ou are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data.

You need to select a data cleaning method.

Which method should you use?

• Replace using Probabilistic PCA

• Normalization

• Synthetic Minority Oversampling Technique (SMOTE)

• Replace using MICE

You plan to deliver a hands-on workshop to several students. The workshop will focus on creating data visualizations using Python. Each student will use a device that has internet access.

Student devices are not configured for Python development. Students do not have administrator access to install software on their devices. Azure subscriptions are not available for students.

You need to ensure that students can run Python-based data visualization code.

Which Azure tool should you use?

• Azure Machine Learning Service

• Anaconda Data Science Platform

• Azure BatchAI

• Azure Notebooks

You are analyzing a dataset by using Azure Machine Learning Studio.

You need to generate a statistical summary that contains the p-value and the unique count for each feature column.

Which two modules can you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

• Export Count Table

• Convert to Indicator Values

• Execute Python Script

• Summarize Data

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.

• VirtualBox

• Kitematic

• BIOS-enabled virtualization

• Windows 10 64-bit Professional

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?

• Azure Machine Learning Studio

• Azure Machine Learning Service

• Azure Databricks

• Azure Kubernetes Service (AKS)

our team is building a data engineering and data science development environment. The environment must support

the following requirements:

1. support Python and Scala

2. compose data storage, movement, and processing services into automated data pipelines

3. the same tool should be used for the orchestration of both data engineering and data science

4. support workload isolation and interactive workloads

5. enable scaling across a cluster of machines

You need to create the environment.

What should you do?

• Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.

• Build the environment in Azure Databricks and use Azure Data Factory for orchestration.

• Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.

• Build the environment in Azure Databricks and use Azure Container Instances for orchestration.