Practice Free DP-100 Exam Online Questions

You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.

You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module.

Which two hyperparameters should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

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DRAG DROP

You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements.

Which three modules should you recommend be used in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

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Correct Answer:

Explanation:

Step 1: Sweep Clustering

Start by using the "Tune Model Hyperparameters" module to select the best sets of parameters for each of the models we’re considering.

One of the interesting things about the "Tune Model Hyperparameters" module is that it not only outputs the results from the Tuning, it also outputs the Trained Model.

Step 2: Train Model

Step 3: Evaluate Model

Scenario: You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.

You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.

Reference: http://breaking-bi.blogspot.com/2017/01/azure-machine-learning-model-evaluation.html

HOTSPOT

You are developing a deep learning model by using TensorFlow. You plan to run the model training workload on an Azure Machine Learning Compute Instance.

You must use CUDA-based model training.

You need to provision the Compute Instance.

Which two virtual machines sizes can you use? To answer, select the appropriate virtual machine sizes in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

Explanation:

CUDA is a parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units). CUDA enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.

Reference: https://www.infoworld.com/article/3299703/what-is-cuda-parallel-programming-for-gpus.html

HOTSPOT

You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.

You must test and deploy the trained model as a web service.

The deployed model must meet the following requirements:

✑ An authenticated connection must not be required for testing.

✑ The deployed model must perform with low latency during inferencing.

✑ The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application.

You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted.

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

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Correct Answer:

Explanation:

Box 1: ds-workstation notebook VM

An authenticated connection must not be required for testing.

On a Microsoft Azure virtual machine (VM), including a Data Science Virtual Machine (DSVM), you create local user accounts while provisioning the VM. Users then authenticate to the VM by using these credentials.

Box 2: gpu-compute cluster

Image classification is well suited for GPU compute clusters

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-common-identity

https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/training-deep-learning

You use Azure Machine Learning Designer to load the following datasets into an experiment:

Dataset1

Dataset2

You use Azure Machine Learning Designer to load the following datasets into an experiment:

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

Solution: Use the Join Data component.

Does the solution meet the goal?

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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?

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You have a Python script that executes a pipeline.

The script includes the following code:

from azureml.core import Experiment

pipeline_run = Experiment(ws, ‘pipeline_test’).submit(pipeline)

You want to test the pipeline before deploying the script.

You need to display the pipeline run details written to the STDOUT output when the pipeline completes.

Which code segment should you add to the test script?

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HOTSPOT

You use an Azure Machine Learning workspace. Azure Data Factor/ pipeline, and a dataset monitor that runs en a schedule to detect data drift.

You need to Implement an automated workflow to trigger when the dataset monitor detects data drift and launch the Azure Data Factory pipeline to update the dataset. The solution must minimize the effort to configure the workflow.

How should you configure the workflow? To answer select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

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Correct Answer:

You have an Azure Machine Learning workspace. You connect to a terminal session from the

Notebooks page in Azure Machine Learning studio. You plan to add a new Jupyter kernel that will be

accessible from the same terminal session. You need to perform the task that must be completed

before you can add the new kernel.

Solution: Create a compute instance.

Does the solution meet the goal?

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You use differential privacy to ensure your reports are private. The calculated value of the epsilon for your data is 1.8. You need to modify your data to ensure your reports are private.

Which epsilon value should you accept for your data?

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