Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 1:

  HOTSPOT

  You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.

  

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

  

  

  ---

  
  

• Question 2:

  You have an Azure AI Foundry project.

  You plan to use the Azure AI Foundry portal to fine-tune a base Azure OpenAI Service model that can accept both text and images as input.

  You need to choose the suitable model.

  Which model should you choose?

  A. gpt-35-turbo
  B. davinci-002
  C. gpt-4
  D. gpt-4o
  C. gpt-4

• Question 3:

  HOTSPOT

  You must use in Azure Data Science Virtual Machine (DSVM) as a compute target.

  You need to attach an existing DSVM to the workspace by using the Azure Machine Learning SDK for Python.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 4:

  Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

  After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

  You are creating a new experiment in Azure Machine Learning Studio.

  One class has a much smaller number of observations than the other classes in the training set.

  You need to select an appropriate data sampling strategy to compensate for the class imbalance.

  Solution: You use the Stratified split for the sampling mode.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

  Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

  References:

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

• Question 5:

  HOTSPOT

  You manage an Azure Machine Learning workspace named workspace1.

  You must register an Azure Blob storage datastore in workspace1 by using an access key.

  You develop Python SDK v2 code to import all modules required to register the datastore.

  You need to complete the Python SDK v2 code to define the datastore.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 6:

  You create an Azure Machine Learning workspace.

  You must configure an event-driven workflow to automatically trigger upon completion of training runs in the workspace. The solution must minimize the administrative effort to configure the trigger.

  You need to configure an Azure service to automatically trigger the workflow.

  Which Azure service should you use?

  A. Event Grid subscription
  B. Azure Automation runbook
  C. Event Hubs Capture
  D. Event Hubs consumer
  A. Event Grid subscription

• Question 7:

  You use Azure Machine Learning designer to create a real-time service endpoint. You have a single Azure Machine Learning service compute resource.

  You train the model and prepare the real-time pipeline for deployment.

  You need to publish the inference pipeline as a web service.

  Which compute type should you use?

  A. a new Machine Learning Compute resource
  B. Azure Kubernetes Services
  C. HDInsight
  D. the existing Machine Learning Compute resource
  E. Azure Databricks
  B. Azure Kubernetes Services

  ---

  Explanation

  Azure Kubernetes Service (AKS) can be used real-time inference.

  https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

• Question 8:

  DRAG DROP

  You build and manage a model by using an Azure Machine Learning workspace.

  Before deploying the model, you must create a Responsible AI dashboard in Azure Machine Learning Studio.

  The dashboard must provide observations of the following:

  1. Metrics that show real-world impact on an outcome of interest due to taking a treatment policy

  2. Examples with minimal changes to a particular data point such that the model's prediction changes

  You need to implement the components for the Responsible AI dashboard.

  Which components should you implement? To answer, move the appropriate components to the correct observations. You may use each component once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

• Question 9:

  you create an Azure Machine learning workspace named workspace1. The workspace contains a Python SOK v2 notebook mat uses Mallow to correct model coaxing men's anal arracks from your local computer.

  Vou must reuse the notebook to run on Azure Machine I earning compute instance m workspace.

  You need to comminute to log training and artifacts from your data science code.

  What should you do?

  A. Configure the tracking URL.
  B. Instantiate the MLClient class.
  C. Log in to workspace1.
  D. Instantiate the job class.
  A. Configure the tracking URL.

• Question 10:

  HOTSPOT

  You manage an Azure Machine Learning workspace. You create a training script named sample_training_script.py. The script is used to train a predictive model in the conda environment defined by a file named environment.yml.

  You need to run the script as an experiment.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: ScriptRunConfig

  Configure and submit your training run

  Create a ScriptRunConfig

  Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on. Any arguments to your training script will be passed via command line if specified in the arguments parameter.

  The following code will configure a ScriptRunConfig object for submitting your job for execution on your local machine.

  from azureml.core import ScriptRunConfig src = ScriptRunConfig(source_directory='.', script='train_iris.py', arguments=['--kernel', 'linear', '--penalty', 1.0], environment=sklearn_env)

  Box 2: submit

  Submit your run from azureml.core import Experiment

  run = Experiment(ws,'Tutorial-TrainIRIS').submit(src) run.wait_for_completion(show_output=True)

  https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-train-scikit-learn