Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 21:

  You are in the process of carrying out feature engineering on a dataset.

  You want to add a feature to the dataset and fill the column value.

  Recommendation: You must make use of the Group Categorical Values Azure Machine Learning Studio module.

  Will the requirements be satisfied?

  A. Yes
  B. No
  B. No

• Question 22:

  You have a deployment of an Azure OpenAI Service base model.

  You plan to fine-tune the model.

  You need to prepare a file that contains training data.

  Which file format should you use?

  A. JSON
  B. TSV
  C. JSONL
  D. CSV
  C. JSONL

• Question 23:

  DRAG DROP

  You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements.

  Which three modules should you recommend be used in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

  Select and Place:

  

  

  ---

  Step 1: Sweep Clustering

  Start by using the "Tune Model Hyperparameters" module to select the best sets of parameters for each of the models we're considering.

  One of the interesting things about the "Tune Model Hyperparameters" module is that it not only outputs the results from the Tuning, it also outputs the Trained Model.

  Step 2: Train Model

  Step 3: Evaluate Model

  Scenario: You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.

  You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.

  References:

  http://breaking-bi.blogspot.com/2017/01/azure-machine-learning-model-evaluation.html

• Question 24:

  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 model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.

  You start by creating a linear regression model.

  You need to evaluate the linear regression model.

  Solution: Use the following metrics: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Accuracy, Precision, Recall, F1 score, and AUC.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Accuracy, Precision, Recall, F1 score, and AUC are metrics for evaluating classification models.

  Note: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error are OK for the linear regression model.

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

• Question 25:

  HOTSPOT

  You register the following versions of a model.

  

  You use the Azure ML Python SDK to run a training experiment. You use a variable named run to reference the experiment run.

  After the run has been submitted and completed, you run the following code:

  

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

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

• Question 26:

  You have an Azure AI Foundry project and a CSV file stored in Azure Blob Storage.

  You plan to add the CSV file as grounding data to the project for RAG by using the file data type.

  You need to specify the URL schema to designate the blob location.

  Which URL schema should you specify?

  A. nfs
  B. file
  C. https
  D. sftp
  C. https

• Question 27:

  You manage an Azure Machine Learning workspace.

  You must log multiple metrics by using MLflow.

  You need to maximize logging performance.

  What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. MLflowClient.log_batch
  B. mlflow.log_metrics
  C. mlflow.log_metric
  D. mlflow.log_param
  A. MLflowClient.log_batch
  B. mlflow.log_metrics

  ---

  Explanation

  Performance considerations: If you need to log multiple metrics (or multiple values for the same metric) avoid making calls to mlflow.log_metric in loops. Better performance can be achieved by logging batch of metrics. Use the method mlflow.log_metrics which accepts a dictionary with all the metrics you want to log at once or use MLflowClient.log_batch which accepts multiple type of elements for logging.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics

• Question 28:

  HOTSPOT

  You deploy a model in Azure Container Instance.

  You must use the Azure Machine Learning SDK to call the model API.

  You need to invoke the deployed model using native SDK classes and methods.

  How should you complete the command? To answer, select the appropriate options in the answer areas.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: from azureml.core.webservice import Webservice

  The following code shows how to use the SDK to update the model, environment, and entry script for a web service to Azure Container Instances:

  from azureml.core import Environment from azureml.core.webservice import Webservice from azureml.core.model import Model, InferenceConfig

  Box 2: predictions = service.run(input_json)

  Example: The following code demonstrates sending data to the service:

  import json test_sample = json.dumps({'data': [ [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], [10, 9, 8, 7, 6, 5, 4, 3, 2, 1] ]})

  test_sample = bytes(test_sample, encoding='utf8') prediction = service.run(input_data=test_sample) print(prediction)

  https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/how-to-deploy-azure-container-instance

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

• Question 29:

  You have the following code. The code prepares an experiment to run a script:

  

  The experiment must be run on local computer using the default environment.

  You need to add code to start the experiment and run the script.

  Which code segment should you use?

  A. run = script_experiment.start_logging()
  B. run = Run(experiment=script_experiment)
  C. ws.get_run(run_id=experiment.id)
  D. run = script_experiment.submit(config=script_config)
  D. run = script_experiment.submit(config=script_config)

  ---

  Explanation

  The experiment class submit method submits an experiment and return the active created run.

  Syntax: submit(config, tags=None, **kwargs)

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

• Question 30:

  HOTSPOT

  You create a new Azure Databricks workspace.

  You configure a new cluster for long-running tasks with mixed loads on the compute cluster as shown in the image below.

  

  Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: No

  Running user code in separate processes is not possible in Scala.

  Box 2: No

  Autoscaling is enabled. Minimum 2 workers, Maximum 8 workers.

  https://docs.databricks.com/clusters/configure.html