Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 81:

  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 new experiment in Azure Machine Learning Studio.

  One class has a much smaller number of observations than the other classes in the training set.

  You need to select an appropriate data sampling strategy to compensate for the class imbalance.

  Solution: You use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

  References:

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

• Question 82:

  HOTSPOT

  You need to build a feature extraction strategy for the local models.

  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 83:

  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 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.log_list('Label Values', label_vals)

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  run.log_list log a list of values to the run with the given name using log_list.

  Example: run.log_list("accuracies", [0.6, 0.7, 0.87])

  Note:

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

  Data is read into a pandas.DataFrame, which is a two-dimensional, size-mutable, potentially heterogeneous tabular data.

  label_vals =data['label'].unique label_vals contains a list of unique label values.

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

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)

  https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html

• Question 84:

  HOTSPOT

  You create a script for training a machine learning model in Azure Machine Learning service.

  You create an estimator by running 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.

  

  

  ---

  Box 1: Yes

  Parameter source_directory is a local directory containing experiment configuration and code files needed for a training job.

  Box 2: Yes

  script_params is a dictionary of command-line arguments to pass to the training script specified in entry_script.

  Box 3: No

  Box 4: Yes

  The conda_packages parameter is a list of strings representing conda packages to be added to the Python environment for the experiment.

• Question 85:

  You construct a machine learning experiment via Azure Machine Learning Studio.

  You would like to split data into two separate datasets.

  Which of the following actions should you take?

  A. You should make use of the Split Data module.
  B. You should make use of the Group Categorical Values module.
  C. You should make use of the Clip Values module.
  D. You should make use of the Group Data into Bins module.
  A. You should make use of the Split Data module.

• Question 86:

  You create a new Azure subscription. No resources are provisioned in the subscription.

  You need to create an Azure Machine Learning workspace.

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

  NOTE: Each correct selection is worth one point.

  A. Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, resource_group, and location parameters.
  B. Use an Azure Resource Management template that includes a Microsoft.MachineLearningServices/ workspaces resource and its dependencies.
  C. Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the az group create function with --name and --location parameters, and then the az ml workspace create function, specifying "w and "g parameters for the workspace name and resource group.
  D. Navigate to Azure Machine Learning studio and create a workspace.
  E. Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name, subscription_id, and resource_group parameters.
  A. Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, resource_group, and location parameters.
  C. Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the az group create function with --name and --location parameters, and then the az ml workspace create function, specifying "w and "g parameters for the workspace name and resource group.
  D. Navigate to Azure Machine Learning studio and create a workspace.

• Question 87:

  DRAG DROP

  You develop a flow for an Azure AI Foundry project.

  You plan to use outputs generated by running the flow to determine:

  1. The number of tokens used by each large language model (LLM) node

  2. The accuracy of the model used by the flow

  Which output type should you examine?

  Select and Place:

  

  

• Question 88:

  You train and register an Azure Machine Learning model.

  You plan to deploy the model to an online endpoint.

  You need to ensure that applications will be able to use an authentication method with a non-expiring artifact to access the model.

  Solution: Create a managed online endpoint and set the value of its auth_mode parameter to aml_token.

  Deploy the model to the online endpoint.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 89:

  HOTSPOT

  You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.

  You must test and deploy the trained model as a web service. The deployed model must meet the following requirements:

  1. An authenticated connection must not be required for testing.

  2. The deployed model must perform with low latency during inferencing.

  3. The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application.

  You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted.

  Which compute resources should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: ds-workstation notebook VM

  An authenticated connection must not be required for testing.

  On a Microsoft Azure virtual machine (VM), including a Data Science Virtual Machine (DSVM), you create local user accounts while provisioning the VM. Users then authenticate to the VM by using these credentials.

  Box 2: gpu-compute cluster

  Image classification is well suited for GPU compute clusters

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

  https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/ai/training-deep-learning

• Question 90:

  You have been tasked with ascertaining if two sets of data differ considerably. You will make use of Azure Machine Learning Studio to complete your task.

  You plan to perform a paired t-test.

  Which of the following are conditions that must apply to use a paired t-test? (Choose all that apply.)

  A. All scores are independent from each other.
  B. You have a matched pairs of scores.
  C. The sampling distribution of d is normal.
  D. The sampling distribution of x1- x2 is normal.
  B. You have a matched pairs of scores.
  C. The sampling distribution of d is normal.

  ---

  Explanation

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t-test