Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 421:

  HOTSPOT

  You manage an Azure AI Foundry project.

  You plan to fine-tune a base model by using pre-uploaded training and validation data. You must specify a hyperparameter to ensure the job is reproducible.

  You need to submit the fine-tuning training job.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 422:

  You manage an Azure AI Foundry project.

  You plan to develop a RAG solution from PDF files by creating a vector index.

  You need to select the location of the data you plan to index.

  Which two data sources can you use? (Choose two.)

  A. Azure Data Lake Storage Gen2
  B. Data in Azure AI Foundry
  C. OneLake in Microsoft Fabric
  D. Azure Blob Storage
  A. Azure Data Lake Storage Gen2
  D. Azure Blob Storage

• Question 423:

  DRAG DROP

  You develop a Prompt flow in an Azure AI Foundry project.

  You plan to use variants and invoke a custom API in the flow.

  You need to add tools to the flow that will implement the planned functionality. Your solution must minimize development effort.

  Which tools should you use? To answer, move the appropriate tools to the correct functionalities. You may use each tool 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 424:

  You use an Azure Machine Learning workspace.

  You must monitor cost at the endpoint and deployment level.

  You have a trained model that must be deployed as an online endpoint. Users must authenticate by using Microsoft Entra ID.

  What should you do?

  A. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the token_auth_mode parameter of the target configuration object to true.
  B. Deploy the model to a managed online endpoint. During deployment, set the token_auth_mode parameter of the target configuration object to true.
  C. Deploy the model to Azure Kubernetes Service (AKS). During deployment, set the auth_mode parameter to configure the authentication type.
  D. Deploy the model to a managed online endpoint. During deployment, set the auth_mode parameter to configure the authentication type.
  B. Deploy the model to a managed online endpoint. During deployment, set the token_auth_mode parameter of the target configuration object to true.

• Question 425:

  You develop a machine learning project on a local machine. The project uses the Azure Machine Learning SDK for Python. You use Git as version control for scripts.

  You submit a training run that returns a Run object.

  You need to retrieve the active Git branch for the training run.

  Which two code segments should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. details = run.get_environment()
  B. details.properties['azureml.git.branch']
  C. details.properties['azureml.git.commit']
  D. details = run.get_details()
  B. details.properties['azureml.git.branch']
  C. details.properties['azureml.git.commit']

• Question 426:

  HOTSPOT

  You are developing a two-step Azure Machine Learning pipeline by using the Azure Machine Learning SDK for Python.

  The pipeline must pass temporary data from the first step to the second step.

  You need to configure the second step to ensure that it can use the temporary data from the first step.

  Which class and method should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 427:

  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 train a classification model by using a logistic regression algorithm.

  You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.

  You need to create an explainer that you can use to retrieve the required global and local feature importance values.

  Solution: Create a PFIExplainer.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 428:

  HOTSPOT

  You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint. The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.

  You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.

  You need to configure store1.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Regenerate the keys of account1.

  Azure Blob Storage support authentication through Account key or SAS token.

  To authenticate your access to the underlying storage service, you can provide either your account key, shared access signatures (SAS) tokens, or service principal

  Box 2: Update the authentication for store1.

  For Azure Machine Learning studio users, several features rely on the ability to read data from a dataset; such as dataset previews, profiles and automated machine learning. For these features to work with storage behind virtual networks, use a workspace managed identity in the studio to allow Azure Machine Learning to access the storage account from outside the virtual network.

  Note: Some of the studio's features are disabled by default in a virtual network. To re-enable these features, you must enable managed identity for storage accounts you intend to use in the studio.

  The following operations are disabled by default in a virtual network: Preview data in the studio.

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

• Question 429:

  You write five Python scripts that must be processed in the order specified in Exhibit A ?which allows the same modules to run in parallel, but will wait for modules with dependencies.

  You must create an Azure Machine Learning pipeline using the Python SDK, because you want to script to create the pipeline to be tracked in your version control system. You have created five PythonScriptSteps and have named the variables to match the module names.

  

  You need to create the pipeline shown. Assume all relevant imports have been done.

  Which Python code segment should you use?

  

  A. Option A
  B. Option B
  C. Option C
  D. Option D
  A. Option A

  ---

  Explanation

  The steps parameter is an array of steps. To build pipelines that have multiple steps, place the steps in order in this array.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-run-step

• Question 430:

  You have a dataset that contains records of patients tested for diabetes. The dataset includes the patient's age.

  You plan to create an analysis that will report the mean age value from the differentially private data derived from the dataset.

  You need to identify the epsilon value to use in the analysis that minimizes the risk of exposing the actual data.

  Which epsilon value should you use?

  A. -1.5
  B. -0.5
  C. 0.5
  D. 1.5
  C. 0.5

  ---

  Explanation

  Epsilon: Put simplistically, epsilon is a non-negative value that provides an inverse measure of the amount of noise added to the data. A low epsilon results in a dataset with a greater level of privacy, while a high epsilon results in a dataset that is closer to the original data. Generally, you should use epsilon values between 0 and 1. Epsilon is correlated with another value named delta, that indicates the probability that a report generated by an analysis is not fully private.

  Note: Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private) the data is.

  https://github.com/MicrosoftLearning/mslearn-dp100/blob/main/13%20-%20Explore%20Differential%20Privacy.ipynb

  https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy