Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 151:

  You need to implement a scaling strategy for the local penalty detection data.

  Which normalization type should you use?

  A. Streaming
  B. Weight
  C. Batch
  D. Cosine
  C. Batch

  ---

  Explanation

  Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper. In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript."

  Scenario:

  Local penalty detection models must be written by using BrainScript.

  References:

  https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics

• Question 152:

  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. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.

  You plan to add a new Jupyter kernel that will be accessible from the same terminal session.

  You need to perform the task that must be completed before you can add the new kernel.

  Solution: Delete the Python 3.8 - AzureML kernel.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 153:

  DRAG DROP

  You manage an Azure AI Foundry project.

  You deploy a large language model from the model catalog.

  You need to manually evaluate the model, collect statistics, and be able to review the results later.

  Select and Place:

  

  

• Question 154:

  You have a comma-separated values (CSV) file containing data from which you want to train a classification model.

  You are using the Automated Machine Learning interface in Azure Machine Learning studio to train the classification model. You set the task type to Classification.

  You need to ensure that the Automated Machine Learning process evaluates only linear models.

  What should you do?

  A. Add all algorithms other than linear ones to the blocked algorithms list.
  B. Set the Exit criterion option to a metric score threshold.
  C. Clear the option to perform automatic featurization.
  D. Clear the option to enable deep learning.
  E. Set the task type to Regression.
  A. Add all algorithms other than linear ones to the blocked algorithms list.

• Question 155:

  HOTSPOT

  You create an Azure Machine learning workspace. The workspace contains a folder named src. The folder contains a Python script named script1.py.

  You use the Azure Machine Learning Python SDK v2 to create a control script. You must use the control script to run script1.py as part of a training job.

  You need to complete the section of script that defines the job parameters.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: command

  Upgrade script run to SDK v2

  In SDK v2, "experiments" and "runs" are consolidated into jobs.

  A job has a type. Most jobs are command jobs that run a command, like python main.py.

  Example:

  # define the command command_job = command( code="./src", command="train.py", environment=env, compute="cpu-cluster", )

  Box 2: code

  https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-command-job

• Question 156:

  You are performing feature engineering on a dataset.

  You must add a feature named CityName and populate the column value with the text London.

  You need to add the new feature to the dataset.

  Which Azure Machine Learning Studio module should you use?

  A. Extract N-Gram Features from Text
  B. Edit Metadata
  C. Preprocess Text
  D. Apply SQL Transformation
  D. Apply SQL Transformation

• Question 157:

  You manage an Azure Machine Learning workspace.

  You plan to irain a natural language processing (NLP) tew classification model in multiple languages by using Azure Machine learning Python SDK v2.

  You need to configure the language of the text classification job by using automated machine learning.

  Which method of the TextClassifkationlob class should you use?

  A. set.data
  B. set_featurization
  C. set_ sweep
  D. set_training_parameters
  B. set_featurization

• Question 158:

  HOTSPOT

  You have an Azure Machine Learning workspace.

  You plan to use Azure Machine Learning Python SDK v2 to register a component in the workspace. The component definition is stored in the local file ./components/train/train.yml.

  You write code to connect to the workspace by using the ml_client object and import all required libraries.

  You need to complete the remaining code.

  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 159:

  You create a binary classification model. The model is registered in an Azure Machine Learning workspace. You use the Azure Machine Learning Fairness SDK to assess the model fairness.

  You develop a training script for the model on a local machine.

  You need to load the model fairness metrics into Azure Machine Learning studio.

  What should you do?

  A. Implement the download_dashboard_by_upload_id function
  B. Implement the create_group_metric_set function
  C. Implement the upload_dashboard_dictionary function
  D. Upload the training script
  C. Implement the upload_dashboard_dictionary function

  ---

  Explanation

  import azureml.contrib.fairness package to perform the upload:

  from azureml.contrib.fairness import upload_dashboard_dictionary, download_dashboard_by_upload_id

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml

• Question 160:

  DRAG DROP

  You are creating a machine learning model that can predict the species of a penguin from its measurements. You have a file that contains measurements for three species of penguin in comma-delimited format.

  The model must be optimized for area under the received operating characteristic curve performance metric, averaged for each class.

  You need to use the Automated Machine Learning user interface in Azure Machine Learning studio to run an experiment and find the best performing model.

  Which five 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:

  

  

  ---

  Step 1:Create and select a new dataset by uploading he command-delimited file of penguin data.

  Step 2: Select the Classification task type

  Step 3: Set the Primary metric configuration setting to Accuracy.

  The available metrics you can select is determined by the task type you choose.

  Primary metrics for classification scenarios: Post thresholded metrics, like accuracy, average_precision_score_weighted, norm_macro_recall, and precision_score_weighted may not optimize as well for datasets which are very small, have very large class skew (class imbalance), or when the expected metric value is very close to 0.0 or 1.0. In those cases, AUC_weighted can be a better choice for the primary metric.

  Step 4: Configure the automated machine learning run by selecting the experiment name, target column, and compute target

  Step 5: Run the automated machine learning experiment and review the results.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-auto-train