Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 91:

  HOTSPOT

  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.

  Hot Area:

  

  Correct Answer:

  

  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

• Question 92:

  HOTSPOT

  You are preparing to build a deep learning convolutional neural network model for image classification. You create a script to train the model using CUDA devices.

  You must submit an experiment that runs this script in the Azure Machine Learning workspace.

  The following compute resources are available:

  1.

  a Microsoft Surface device on which Microsoft Office has been installed. Corporate IT policies prevent the installation of additional software

  2.

  a Compute Instance named ds-workstation in the workspace with 2 CPUs and 8 GB of memory

  3.

  an Azure Machine Learning compute target named cpu-cluster with eight CPU-based nodes

  4.

  an Azure Machine Learning compute target named gpu-cluster with four CPU and GPU-based nodes

  You need to specify the compute resources to be used for running the code to submit the experiment, and for running the script in order to minimize model training time.

  Which resources should the data scientist use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: the ds-workstation notebook VM

  Box 2: the gpu-compute target Just as GPUs revolutionized deep learning through unprecedented training and inferencing performance, RAPIDS enables traditional machine learning practitioners to unlock game-changing performance with GPUs. With RAPIDS on Azure Machine Learning service, users can accelerate the entire machine learning pipeline, including data processing, training and inferencing, with GPUs from the NC_v3, NC_v2, ND or ND_v2 families. Users can unlock performance gains of more than 20X (with 4 GPUs), slashing training times from hours to minutes and dramatically reducing time-to-insight.

  Reference: https://azure.microsoft.com/sv-se/blog/azure-machine-learning-service-now-supports-nvidia-s-rapids/

• Question 93:

  HOTSPOT

  You write code to retrieve an experiment that is run from your Azure Machine Learning workspace.

  The run used the model interpretation support in Azure Machine Learning to generate and upload a model explanation.

  Business managers in your organization want to see the importance of the features in the model.

  You need to print out the model features and their relative importance in an output that looks similar to the following.

  

  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:

  

  Correct Answer:

  

  Box 1: from_run_id

  from_run_id(workspace, experiment_name, run_id)

  Create the client with factory method given a run ID.

  Returns an instance of the ExplanationClient.

  Parameters

  Workspace Workspace An object that represents a workspace.

  experiment_name str The name of an experiment.

  run_id str A GUID that represents a run.

  Box 2: list_model_explanations

  list_model_explanations returns a dictionary of metadata for all model explanations available.

  Returns

  A dictionary of explanation metadata such as id, data type, explanation method, model type, and upload time, sorted by upload time

  Box 3: explanation

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-contrib-interpret/azureml.contrib.interpret.explanation.explanation_client.explanationclient?view=azure-ml-py

• Question 94:

  HOTSPOT

  You have a multi-class image classification deep learning model that uses a set of labeled photographs. You create the following code to select hyperparameter values when training the model.

  

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Yes

  Hyperparameters are adjustable parameters you choose to train a model that govern the training process itself. Azure Machine Learning allows you to automate hyperparameter exploration in an efficient manner, saving you significant time

  and resources. You specify the range of hyperparameter values and a maximum number of training runs. The system then automatically launches multiple simultaneous runs with different parameter configurations and finds the configuration

  that results in the best performance, measured by the metric you choose. Poorly performing training runs are automatically early terminated, reducing wastage of compute resources. These resources are instead used to explore other

  hyperparameter configurations.

  Box 2: Yes

  uniform(low, high) - Returns a value uniformly distributed between low and high

  Box 3: No

  Bayesian sampling does not currently support any early termination policy.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

• Question 95:

  HOTSPOT

  You deploy a model in Azure Container Instance.

  You must use the Azure Machine Learning SDK to call the model API.

  You need to invoke the deployed model using native SDK classes and methods.

  How should you complete the command? To answer, select the appropriate options in the answer areas.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: from azureml.core.webservice import Webservice

  The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:

  from azureml.core import Environment

  from azureml.core.webservice import Webservice

  from azureml.core.model import Model, InferenceConfig

  Box 2: predictions = service.run(input_json)

  Example: The following code demonstrates sending data to the service:

  import json

  test_sample = json.dumps({'data': [ [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]

  ]})

  test_sample = bytes(test_sample, encoding='utf8')

  prediction = service.run(input_data=test_sample) print(prediction)

  Reference: https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/how-to-deploy-azure-container-instance

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

• Question 96:

  HOTSPOT

  You are preparing to use the Azure ML SDK to run an experiment and need to create compute. You run the following code:

  

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: No

  If a training cluster already exists it will be used.

  Box 2: Yes

  The wait_for_completion method waits for the current provisioning operation to finish on the cluster.

  Box 3: Yes

  Low Priority VMs use Azure's excess capacity and are thus cheaper but risk your run being pre-empted.

  Box 4: No

  Need to use training_compute.delete() to deprovision and delete the AmlCompute target.

  Reference:

  https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/training/train-on-amlcompute/train-on-amlcompute.ipynb

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget

• Question 97:

  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:

  1.

  An authenticated connection must not be required for testing.

  2.

  The deployed model must perform with low latency during inferencing.

  3.

  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.

  Hot Area:

  

  Correct Answer:

  

  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

• Question 98:

  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.

  Hot Area:

  

  Correct Answer:

  

  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

• Question 99:

  HOTSPOT

  A coworker registers a datastore in a Machine Learning services workspace by using the following code:

  

  You need to write code to access the datastore from a notebook.

  How should you complete the code segment? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: DataStore

  To get a specific datastore registered in the current workspace, use the get() static method on the Datastore class:

  # Get a named datastore from the current workspace

  datastore = Datastore.get(ws, datastore_name='your datastore name')

  Box 2: ws

  Box 3: demo_datastore

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data

• Question 100:

  HOTSPOT

  You create an Azure Machine Learning compute target named ComputeOne by using the STANDARD_D1 virtual machine image.

  You define a Python variable named was that references the Azure Machine Learning workspace. You run the following Python code:

  

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1:Yes

  ComputeTargetException class: An exception related to failures when creating, interacting with, or configuring a compute target. This exception is commonly raised for failures attaching a compute target, missing headers, and unsupported

  configuration values.

  Create(workspace, name, provisioning_configuration)

  Provision a Compute object by specifying a compute type and related configuration.

  This method creates a new compute target rather than attaching an existing one.

  Box 2: Yes

  Box 3: No

  The line before print('Step1') will fail.

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.computetarget