Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 501:

  You create a multi-class image classification model with automated machine learning in Azure Machine Learning.

  You need to prepare labeled image data as input for model training in the form of an Azure Machine Learning tabular dataset.

  Which data format should you use?

  A. COCO
  B. JSONL
  C. JSON
  D. Pascal VOC
  B. JSONL

• Question 502:

  HOTSPOT

  You create an Azure Machine Learning workspace.

  You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a table in the following format:

  

  You need to complete the Python code to log the table.

  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 503:

  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 have an Azure Machine Learning workspace that includes an AmlCompute cluster and a batch endpoint.

  You clone a repository that contains an MLflow model to your local computer.

  You need to ensure that you can deploy the model to the batch endpoint.

  Solution: Create a data asset in the workspace.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 504:

  DRAG DROP

  You create an Azure Machine Learning workspace.

  You must implement dedicated compute for model training in the workspace by using Azure Synapse compute resources. The solution must attach the dedicated compute and start an Azure Synapse session.

  You need to implement the compute resources.

  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 505:

  You have a Python script that executes a pipeline. The script includes the following code:

  from azureml.core import Experiment

  pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline)

  You want to test the pipeline before deploying the script.

  You need to display the pipeline run details written to the STDOUT output when the pipeline completes.

  Which code segment should you add to the test script?

  A. pipeline_run.get.metrics()
  B. pipeline_run.wait_for_completion(show_output=True)
  C. pipeline_param = PipelineParameter(name="stdout", default_value="console")
  D. pipeline_run.get_status()
  B. pipeline_run.wait_for_completion(show_output=True)

  ---

  Explanation

  wait_for_completion: Wait for the completion of this run. Returns the status object after the wait.

  Syntax: wait_for_completion(show_output=False, wait_post_processing=False, raise_on_error=True)

  Parameter: show_output Indicates whether to show the run output on sys.stdout.

• Question 506:

  You manage an Azure Machine Learning workspace. The development environment for managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks.

  A Synapse Spark Compute is currently attached and uses system-assigned identity.

  You need to use Python code to update the Synapse Spark Compute to use a user- assigned identity.

  Solution: Initialize the DefaultAzureCredential class.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 507:

  HOTSPOT

  You monitor an Azure Machine Learning classification training experiment named train- classification on Azure Notebooks.

  You must store a table named table as an artifact in Azure Machine Learning Studio during model training.

  You need to collect and list the metrics by using MLfow.

  how should you complete the code segment? To answer, select the appropriate option in the answer area.

  NOTE: Each correct selection is worth on* point.

  

  

• Question 508:

  You have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.

  You are preparing to create a virtual machine that has the necessary tools built into it.

  You need to make use of the correct virtual machine type.

  Recommendation: You make use of a Geo AI Data Science Virtual Machine (Geo-DSVM) Windows edition.

  Will the requirements be satisfied?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  The Azure Geo AI Data Science VM (Geo-DSVM) delivers geospatial analytics capabilities from Microsoft's Data Science VM. Specifically, this VM extends the AI and data science toolkits in the Data Science VM by adding ESRI's market- leading ArcGIS Pro Geographic Information System.

  https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

• Question 509:

  HOTSPOT

  You use Azure Machine Learning and SmartNoise Python libraries to implement a differential privacy solution to protect a dataset containing citizen demographics for the city of Seattle in the United States.

  The solution has the following requirements:

  Allow for multiple queries targeting the mean and variance of the citizen's age.

  Ensure full plausible deniability.

  You need to define the query rate limit to minimize the risk of re-identification.

  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: privacy budget

  The SmartNoise system includes differentially private algorithms, techniques for managing privacy budgets for subsequent queries, and other capabilities.

  Box 2: Set the epsilon value to the smaller of the epsilon values assigned to the mean and variance queries

  Note:

  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.

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

  https://cloudblogs.microsoft.com/opensource/2021/02/18/create-privacy-preserving-synthetic-data-for-machine-learning-with-smartnoise/

• Question 510:

  You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named. amlignore.

  The directory also contains subdirectories named. /outputs and./logs.

  There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets.

  You create a file named. gift ignore in the root of the directory. You add the names of the 20 files to the. gift ignore file.

  These 20 files continue to be copied to the compute targets.

  You need to exclude the 20 files.

  What should you do?

  A. Add the contents of the file named. amlignore to the file named. gift ignore.
  B. Move the file named. gift ignore to the. /logs directory.
  C. Copy the contents of the file named. gift ignore to the file named. amlignore.
  D. Move the file named. gift ignore to the. /outputs directory.
  C. Copy the contents of the file named. gift ignore to the file named. amlignore.