Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 11:

  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 plan to tune model hyperparameters by using a sweep job.

  You need to find a sampling method that supports early termination of low-performance jobs and continuous hyperparameters.

  Solution: Use the Random sampling method over the hyperparameter space.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 12:

  You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

  from azureml.core import Run

  import pandas as pd

  run = Run.get_context()

  data = pd.read_csv('data.csv')

  label_vals = data['label'].unique()

  # Add code to record metrics here

  run.complete()

  The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

  You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

  Solution: Replace the comment with the following code:

  run.upload_file('outputs/labels.csv', './data.csv')

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  label_vals has the unique labels (from the statement label_vals = data['label'].unique()), and it has to be logged.

  Note:

  Instead use the run_log function to log the contents in label_vals:

  for label_val in label_vals:

  run.log('Label Values', label_val)

  https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

• Question 13:

  HOTSPOT

  You use Azure Machine Learning to train a machine learning model.

  You use the following training script in Python to perform logging:

  import mlflow

  mlflow.log_metric("accuracy", float(vel_accuracy))

  You must use a Python script to define a sweep job.

  You need to provide the primary metric and goal you want hyperparameter tuning to optimize.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: accuracy

  In our case we have mlflow.log_metric("accuracy", float(vel_accuracy))

  The primary_metric must also be accuracy.

  Note: Specify the objective of the sweep

  Define the objective of your sweep job by specifying the primary metric and goal you want hyperparameter tuning to optimize. Each training job is evaluated for the primary metric. The early termination policy uses the primary metric to identify low-performance jobs.

  primary_metric: The name of the primary metric needs to exactly match the name of the metric logged by the training script goal: It can be either Maximize or Minimize and determines whether the primary metric will be maximized or minimized when evaluating the jobs.

  Box 2: maximize

  This sample maximizes "accuracy".

  from azure.ai.ml.sweep import Uniform, Choice command_job_for_sweep = command_job( learning_rate=Uniform(min_value=0.05, max_value=0.1), batch_size=Choice(values=[16, 32, 64, 128]), )

  sweep_job = command_job_for_sweep.sweep( compute="cpu-cluster", sampling_algorithm = "bayesian", primary_metric="accuracy", goal="Maximize", )

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

• Question 14:

  You have an Azure Machine Learning workspace. You are connecting an Azure Data Lake Storage Gen2 account to the workspace as a data store.

  You need to authorize access from the workspace to the Azure Data Lake Storage Gen2 account.

  What should you use?

  A. Service principal
  B. SAS token
  C. Managed identity
  D. Account key
  C. Managed identity

  ---

  Explanation

  Roles and requirements for Azure Data Share

  With Azure Data Share service, you can share data without exchanging credentials between data provider and consumer. For snapshot-based sharing, Azure Data Share service uses Managed Identities (previously known as MSIs) to authenticate to Azure data store. Azure Data Share resource's managed identity needs to be granted access to Azure data store to read or write data.

  https://learn.microsoft.com/en-us/azure/data-share/concepts-roles-permissions

• Question 15:

  DRAG DROP

  You create an Azure Machine Learning workspace. You are training a classification model with no-code AutoML in Azure Machine Learning studio.

  The model must predict if a client of a financial institution will subscribe to a fixed-term deposit. You must identify the feature that has the most influence on the predictions of the model for the second highest scoring algorithm. You must minimize the effort and time to identify the feature.

  You need to complete the identification.

  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 16:

  You create a workspace to include a compute instance by using Azure Machine Learning Studio. You are developing a Python SDK v2 notebook in the workspace.

  You need to use Intellisense in the notebook.

  What should you do?

  A. Stop the compute instance.
  B. Start the compute instance.
  C. Run a %pip magic function on the compute instance.
  D. Run a !pip magic function on the compute instance.
  B. Start the compute instance.

• Question 17:

  You are using the Azure Machine Learning SDK to run a training experiment that trains a classification model and calculates its accuracy metric.

  The model will be retrained each month as new data becomes available.

  You must register the model for use in a batch inference pipeline.

  You need to register the model by using MLflow and ensure that models created by subsequent retraining experiments are registered only if their accuracy is higher than the currently registered model.

  What should you do?

  A. Register a metric named accuracy with the accuracy metric as a value when registering the model, and only register subsequent models if their accuracy is higher than the accuracy tag value of the currently registered model.
  B. Specify the model framework version when registering the model, and only register subsequent models if this value is higher.
  C. Register the model with the same name each time regardless of accuracy, and always use the latest version of the model in the batch inferencing pipeline.
  D. Specify a different name for the model each time you register it.
  B. Specify the model framework version when registering the model, and only register subsequent models if this value is higher.

• Question 18:

  HOTSPOT

  You manage an Azure Machine Learning workspace.

  The titanic.csv file is available in an Azure Blob Storage account named storage1. The container name is container1. The folder name is data.

  You perform interactive data wrangling by using a serverless Spark compute.

  You need to load the data from Blob Storage into a pandas DataFrame.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 19:

  You train and register a model in your Azure Machine Learning workspace.

  You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.

  You need to create the inferencing script for the ParallelRunStep pipeline step.

  Which two functions should you include? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. run(mini_batch)
  B. main()
  C. batch()
  D. init()
  E. score(mini_batch)
  A. run(mini_batch)
  D. init()

  ---

  Explanation

  https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learning-pipelines/parallel-run

• Question 20:

  HOTSPOT

  You manage an Azure Machine Learning workspace and a GitHub repository.

  The repository contains a CSV file located at:

  https://raw.githubusercontent.com/account1/repo1/main/doc1/data1.csv

  The CSV file includes embedded newlines.

  You need to create a data asset that minimizes the risk of misaligned field values when reading the file.

  Which data asset configuration values should you use?