Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 311:

  You have an Azure Machine Learning (ML) model deployed to an online endpoint.

  You need to review container logs from the endpoint by using Azure ML Python SDK v2. The logs must include the console log from the inference server, with print/log statements from the model's scoring script.

  What should you do first?

  A. Connect by using SSH to the inference server.
  B. Create an instance of the MLCIient class.
  C. Connect by using Docker tools to the inference server.
  D. Create an instance of the OnlineDeploymentOperations class.
  B. Create an instance of the MLCIient class.

  ---

  Explanation

  Get container logs

  To see log output from container, use the get_logs method as follows:

  Python ml_client.online_deployments.get_logs( name="<deployment-name>", endpoint_name="<endpoint-name>", lines=100 )

  You can also get logs from the storage initializer container by adding container_type="storage-initializer" option.

  Python ml_client.online_deployments.get_logs( name="<deployment-name>", endpoint_name="<endpoint-name>", lines=100, container_type="storage-initializer" )

  Note (B, not D): OnlineDeploymentOperations Class

  You should not instantiate this class directly. Instead, you should create an MLClient instance that instantiates it for you and attaches it as an attribute.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-online-endpoints

  https://learn.microsoft.com/en-us/python/api/azure-ai-ml/azure.ai.ml.operations.onlinedeploymentoperations

• Question 312:

  You register a model that you plan to use in a batch inference pipeline.

  The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called.

  You need to configure the pipeline.

  Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?

  A. process_count_per_node= "6"
  B. node_count= "6"
  C. mini_batch_size= "6"
  D. error_threshold= "6"
  C. mini_batch_size= "6"

  ---

  Explanation

  https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunconfig?view=azure-ml-py

• Question 313:

  DRAG DROP

  You manage an Azure Machine Learning workspace.

  You plan to import and wrangle data stored in Azure Data Lake Storage Gen2 with Apache Spark.

  You need to start interactive data wrangling with the user identity passthrough.

  Which three 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.

  Select and Place:

  

  

• Question 314:

  You are creating a machine learning model.

  You need to identify outliers in the data.

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

  NOTE: Each correct selection is worth one point.

  A. Venn diagram
  B. Box plot
  C. ROC curve
  D. Random forest diagram
  E. Scatter plot
  B. Box plot
  E. Scatter plot

  ---

  Explanation

  The box-plot algorithm can be used to display outliers.

  One other way to quickly identify Outliers visually is to create scatter plots.

  References:

  https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azure-machine-learning/

• Question 315:

  HOTSPOT

  You have an Azure Machine Learning workspace.

  You plan to use Azure Machine Learning designer to register multiple components in the workspace.

  You need to configure the component that supports registration.

  Which component configuration should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 316:

  You train a machine learning model.

  You must deploy the model as a real-time inference service for testing. The service requires low CPU utilization and less than 48 MB of RAM. The compute target for the deployed service must initialize automatically while minimizing cost and administrative overhead.

  Which compute target should you use?

  A. Azure Container Instance (ACI)
  B. attached Azure Databricks cluster
  C. Azure Kubernetes Service (AKS) inference cluster
  D. Azure Machine Learning compute cluster
  A. Azure Container Instance (ACI)

  ---

  Explanation

  Azure Container Instances (ACI) are suitable only for small models less than 1 GB in size. Use it for low-scale CPU-based workloads that require less than 48 GB of RAM. Note: Microsoft recommends using single-node Azure Kubernetes Service (AKS) clusters for dev-test of larger models.

  https://docs.microsoft.com/id-id/azure/machine-learning/how-to-deploy-and-where

• Question 317:

  You use the following code to run a script as an experiment in Azure Machine Learning:

  

  You must identify the output files that are generated by the experiment run.

  You need to add code to retrieve the output file names.

  Which code segment should you add to the script?

  A. files = run.get_properties()
  B. files= run.get_file_names()
  C. files = run.get_details_with_logs()
  D. files = run.get_metrics()
  E. files = run.get_details()
  B. files= run.get_file_names()

  ---

  Explanation

  You can list all of the files that are associated with this run record by called run.get_file_names()

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-track-experiments

• Question 318:

  HOTSPOT

  You train a classification model by using a decision tree algorithm.

  You create an estimator by running the following Python code. The variable feature_names is a list of all feature names, and class_names is a list of all class names.

  from interpret.ext.blackbox import TabularExplainer

  

  You need to explain the predictions made by the model for all classes by determining the importance of all features.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Yes

  TabularExplainer calls one of the three SHAP explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer).

  Box 2: Yes

  To make your explanations and visualizations more informative, you can choose to pass in feature names and output class names if doing classification.

  Box 3: No

  TabularExplainer automatically selects the most appropriate one for your use case, but you can call each of its three underlying explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer) directly.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-aml

• Question 319:

  You create a deep learning model for image recognition on Azure Machine Learning service using GPU- based training.

  You must deploy the model to a context that allows for real-time GPU-based inferencing.

  You need to configure compute resources for model inferencing.

  Which compute type should you use?

  A. Azure Container Instance
  B. Azure Kubernetes Service
  C. Field Programmable Gate Array
  D. Machine Learning Compute
  B. Azure Kubernetes Service

  ---

  Explanation

  You can use Azure Machine Learning to deploy a GPU-enabled model as a web service. Deploying a model on Azure Kubernetes Service (AKS) is one option. The AKS cluster provides a GPU resource that is used by the model for inference.

  Inference, or model scoring, is the phase where the deployed model is used to make predictions. Using GPUs instead of CPUs offers performance advantages on highly parallelizable computation.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-inferencing-gpus

• Question 320:

  DRAG DROP

  You are using a Git repository to track work in an Azure Machine Learning workspace.

  You need to authenticate a Git account by using SSH.

  Which three 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.

  Select and Place:

  

  

  ---

  Authenticate your Git Account with SSH: Step 1: Generating a public/private key pair

  Generate a new SSH key 1. Open the terminal window in the Azure Machine Learning Notebook Tab.

  2. Paste the text below, substituting in your email address.

  ssh-keygen -t rsa -b 4096 -C "[email protected]"

  This creates a new ssh key, using the provided email as a label.

  > Generating public/private rsa key pair.

  Step 2: Add the public key to the Git Account

  In your terminal window, copy the contents of your public key file.

  Step 3: Clone the Git repository by using an SSH repository URL 1. Copy the SSH Git clone URL from the Git repo.

  2. Paste the url into the git clone command below, to use your SSH Git repo URL. This will look something like:

  git clone [email protected]:GitUser/azureml-example.git Cloning into 'azureml-example'.

  https://docs.microsoft.com/en-us/azure/machine-learning/concept-train-model-git-integration