Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 551:

  DRAG DROP

  You create an Azure Machine Learning workspace.

  You need to use the shared file system of the workspace to store a clone of a private Git repository.

  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.

  Select and Place:

  

  

• Question 552:

  You are evaluating a completed binary classification machine learning model.

  You need to use the precision as the evaluation metric.

  Which visualization should you use?

  A. Violin plot
  B. Gradient descent
  C. Box plot
  D. Binary classification confusion matrix
  D. Binary classification confusion matrix

  ---

  Explanation

  Incorrect Answers:

  A: A violin plot is a visual that traditionally combines a box plot and a kernel density plot.

  B: Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point.

  C: A box plot lets you see basic distribution information about your data, such as median, mean, range and quartiles but doesn't show you how your data looks throughout its range.

  References:

  https://machinelearningknowledge.ai/confusion-matrix-and-performance-metrics-machine-learning/

• Question 553:

  You are attaching an Azure Databricks-based compute resource to an Azure Machine Learning development workspace.

  You need to configure parameters to attach the resource.

  Which three parameters should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Workspace name
  B. Compute name
  C. Workspace user credentials
  D. Workspace resource ID
  E. Access token
  A. Workspace name
  B. Compute name
  E. Access token

• Question 554:

  HOTSPOT

  You create an Azure Machine Learning model to include model files and a scoring script.

  You must deploy the model. The deployment solution must meet the following requirements:

  Provide near real-time inferencing.

  Enable endpoint and deployment level cost estimates.

  Support logging to Azure Log Analytics.

  You need to configure the deployment solution.

  What should you configure? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: Managed online

  Endpoint type

  Online endpoints are endpoints that are used for online (real-time) inferencing. Compared to batch endpoints, online endpoints contain deployments that are ready to receive data from clients and can send responses back in real time.

  Managed online endpoints can help streamline your deployment process. Managed online endpoints provide the following benefits over Kubernetes online endpoints:

  Managed infrastructure

  Automatically provisions the compute and hosts the model (you just need to specify the VM type and scale settings) Automatically updates and patches the underlying host OS image Automatic node recovery if there's a system failure Monitoring and logs

  Monitor model availability, performance, and SLA using native integration with Azure Monitor.

  Debug deployments using the logs and native integration with Azure Log Analytics.

  Note: Use Azure Machine Learning endpoints to streamline model deployments for both real-time and batch inference deployments. Endpoints provide a unified interface to invoke and manage model deployments across compute types.

  Incorrect:

  * Batch

  Need to Provide near real-time inferencing.

  * Managed online endpoints help to deploy your ML models in a turnkey manner. Managed online endpoints work with powerful CPU and GPU machines in Azure in a scalable, fully managed way. Managed online endpoints take care of serving, scaling, securing, and monitoring your models, freeing you from the overhead of setting up and managing the underlying infrastructure. The main example in this doc uses managed online endpoints for deployment.

  Kubernetes online endpoint allows you to deploy models and serve online endpoints at your fully configured and managed Kubernetes cluster anywhere, with CPUs or GPUs.

  Box 2: Docker image

  Deployment

  Online deployments requirements

  To create an online endpoint, you need to specify the following elements:

  Model files (or specify a registered model in your workspace) Scoring script - code needed to do scoring/inferencing Environment - a Docker image with Conda dependencies, or a dockerfile Compute instance & scale settings

  Note: A deployment is a set of resources required for hosting the model that does the actual inferencing.

  https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints#managed-online-endpoints-vs-kubernetes-online-endpoints

• Question 555:

  You are planning to host practical training to acquaint learners with data visualization creation using Python. Learner devices are able to connect to the internet.

  Learner devices are currently NOT configured for Python development. Also, learners are unable to install software on their devices as they lack administrator permissions. Furthermore, they are unable to access Azure subscriptions.

  It is imperative that learners are able to execute Python-based data visualization code.

  Which of the following actions should you take?

  A. You should consider configuring the use of Azure Container Instance.
  B. You should consider configuring the use of Azure BatchAI.
  C. You should consider configuring the use of Azure Notebooks.
  D. You should consider configuring the use of Azure Kubernetes Service.
  C. You should consider configuring the use of Azure Notebooks.

  ---

  Explanation

  https://notebooks.azure.com/

• Question 556:

  HOTSPOT

  You create a Python script named train.py and save it in a folder named scripts. The script uses the scikit-learn framework to train a machine learning model.

  You must run the script as an Azure Machine Learning experiment on your local workstation.

  You need to write Python code to initiate an experiment that runs the train.py script.

  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.

  

  

  ---

  Box 1: source_directory

  source_directory: A local directory containing code files needed for a run.

  Box 2: script

  Script: The file path relative to the source_directory of the script to be run.

  Box 3: environment

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

• Question 557:

  You are in the process of creating a machine learning model. Your dataset includes rows with null and missing values.

  You plan to make use of the Clean Missing Data module in Azure Machine Learning Studio to detect and fix the null and missing values in the dataset.

  Recommendation: You make use of the Replace with median option.

  Will the requirements be satisfied?

  A. Yes
  B. No
  A. Yes

• Question 558:

  You manage an Azure Machine Learning workspace. You have a folder that contains a CSV file. The folder is registered as a folder data asset.

  You plan to use the folder data asset for data wrangling during interactive development.

  You need to access and load the folder data asset into a Pandas data frame.

  Which method should you use to achieve this goal?

  A. mltable.from_parquet_files()
  B. mltable.from_delimited_files()
  C. mltable.from_data_lake()
  D. mltable.load()
  B. mltable.from_delimited_files()

• Question 559:

  DRAG DROP

  You have an Azure Machine Learning workspace. You are running an experiment on your local computer.

  You need to ensure that you can use MLflow Tracking with Azure Machine Learning Python SDK v2 to store metrics and artifacts from your local experiment runs m the workspace.

  In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  

  ---

  Explanation:

  Step 1: Go to the workspace in the Azure portal

  Configure MLflow tracking URI

  To connect MLflow to an Azure Machine Learning workspace, you need the tracking URI for the workspace. Each workspace has its own tracking URI and it has the protocol azureml://.

  1. Get the tracking URI for your workspace:

  a. Use the Azure Machine Learning portal to get the tracking URI: (Steps 1-2) b. Open the Azure Machine Learning studio portal and log in using your credentials. (Step 1) c. In the upper right corner, click on the name of your workspace to show the Directory + Subscription + Workspace blade. d. Click on View all properties in Azure Portal. e. On the Essentials section, you will find the property MLflow tracking URI. (Step 2)

  Step 2: Retrieve the tracking URI of the workspace

  Step 3: Import MLflow and MLClient classes.

  2. Configuring the tracking URI:

  Then the method set_tracking_uri() points the MLflow tracking URI to that URI.

  import mlflow (Step 3) mlflow.set_tracking_uri(mlflow_tracking_uri) (Step 4)

  Step 4: Set the MLflow tracking URI and experiment name.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow-configure-tracking?view=azureml-api-2&tabs=studio%2Cmlflow

• Question 560:

  You are with a time series dataset in Azure Machine Learning Studio.

  You need to split your dataset into training and testing subsets by using the Split Data module.

  Which splitting mode should you use?

  A. Recommender Split
  B. Regular Expression Split
  C. Relative Expression Split
  D. Split Rows with the Randomized split parameter set to true
  C. Relative Expression Split