HOTSPOT

You are performing a classification task in Azure Machine Learning Studio.

You must prepare balanced testing and training samples based on a provided data set.

You need to split the data with a 0.75:0.25 ratio.

Which value should you use for each parameter? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Reveal Solution Hide Solution

Correct Answer:

Explanation:

Box 1: Split rows

Use the Split Rows option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50.

You can also randomize the selection of rows in each group, and use stratified sampling. In stratified sampling, you must select a single column of data for which you want values to be apportioned equally among the two result datasets.

Box 2: 0.75

If you specify a number as a percentage, or if you use a string that contains the "%" character, the value is interpreted as a percentage. All percentage values must be within the range (0, 100), not including the values 0 and 100.

Box 3: Yes

To ensure splits are balanced.

Box 4: No

If you use the option for a stratified split, the output datasets can be further divided by subgroups, by selecting a strata column.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/split-data

HOTSPOT

You create a binary classification model using Azure Machine Learning Studio.

You must use a Receiver Operating Characteristic (RO C) curve and an F1 score to evaluate the model.

You need to create the required business metrics.

How should you complete the experiment? To answer, select the appropriate options in the dialog box in the answer area. NOTE: Each correct selection is worth one point.

Reveal Solution Hide Solution

Correct Answer:

HOTSPOT

You are tuning a hyperparameter for an algorithm. The following table shows a data set with different hyperparameter, training error, and validation errors.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.

Reveal Solution Hide Solution

Correct Answer:

Explanation:

Box 1: 4

Choose the one which has lower training and validation error and also the closest match.

Minimize variance (difference between validation error and train error).

Box 2: 5

Minimize variance (difference between validation error and train error).

Reference: https://medium.com/comet-ml/organizing-machine-learning-projects-project-management-guidelines-2d2b85651bbd

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?

Reveal Solution Hide Solution

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.

Reveal Solution Hide Solution

Your team is building a data engineering and data science development environment.

The environment must support the following requirements:

✑ support Python and Scala

✑ compose data storage, movement, and processing services into automated data pipelines

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

✑ support workload isolation and interactive workloads

✑ enable scaling across a cluster of machines

You need to create the environment.

What should you do?

Reveal Solution Hide Solution

DRAG DROP

You are building an intelligent solution using machine learning models.

The environment must support the following requirements:

✑ Data scientists must build notebooks in a cloud environment

✑ Data scientists must use automatic feature engineering and model building in machine learning pipelines.

✑ Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.

✑ Notebooks must be exportable to be version controlled locally.

You need to create the environment.

Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Reveal Solution Hide Solution

Correct Answer:

Explanation:

Step 1: Create an Azure HDInsight cluster to include the Apache Spark Mlib library

Step 2: Install Microsot Machine Learning for Apache Spark You install AzureML on your Azure HDInsight cluster.

Microsoft Machine Learning for Apache Spark (MMLSpark) provides a number of deep learning and data science tools for Apache Spark, including seamless integration of Spark Machine Learning pipelines with Microsoft Cognitive Toolkit (CNTK) and OpenCV, enabling you to quickly create powerful, highly-scalable predictive and analytical models for large image and text datasets.

Step 3: Create and execute the Zeppelin notebooks on the cluster

Step 4: When the cluster is ready, export Zeppelin notebooks to a local environment.

Notebooks must be exportable to be version controlled locally.

References:

https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-zeppelin-notebook

https://azuremlbuild.blob.core.windows.net/pysparkapi/intro.html

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?

Reveal Solution Hide Solution

You are implementing a machine learning model to predict stock prices.

The model uses a PostgreSQL database and requires GPU processing.

You need to create a virtual machine that is pre-configured with the required tools.

What should you do?

Reveal Solution Hide Solution

You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.

You have the following data available for model building:

✑ Video recordings of sporting events

✑ Transcripts of radio commentary about events

✑ Logs from related social media feeds captured during sporting events

You need to select an environment for creating the model.

Which environment should you use?

Reveal Solution Hide Solution