Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 521:

  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 analyzing a numerical dataset which contains missing values in several columns.

  You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.

  You need to analyze a full dataset to include all values.

  Solution: Calculate the column median value and use the median value as the replacement for any missing value in the column.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 522:

  HOTSPOT

  You create a binary classification model to predict whether a person has a disease.

  You need to detect possible classification errors.

  Which error type should you choose for each description? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: True Positive

  A true positive is an outcome where the model correctly predicts the positive class

  Box 2: True Negative

  A true negative is an outcome where the model correctly predicts the negative class.

  Box 3: False Positive

  A false positive is an outcome where the model incorrectly predicts the positive class.

  Box 4: False Negative

  A false negative is an outcome where the model incorrectly predicts the negative class.

  Note: Let's make the following definitions:

  "Wolf" is a positive class.

  "No wolf" is a negative class.

  We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:

  https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative

• Question 523:

  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 train and register a machine learning model.

  You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.

  You need to deploy the web service.

  Solution:

  Create an AciWebservice instance.

  Set the value of the auth_enabled property to True.

  Deploy the model to the service.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  Key-based authentication.

  Web services deployed on AKS have key-based auth enabled by default. ACI-deployed services have key-based auth disabled by default, but you can enable it by setting auth_enabled = TRUE when creating the ACI web service. The following is an example of creating an ACI deployment configuration with key-based auth enabled.

  deployment_config <- aci_webservice_deployment_config(cpu_cores = 1, memory_gb = 1, auth_enabled = TRUE)

  https://azure.github.io/azureml-sdk-for-r/articles/deploying-models.html

• Question 524:

  HOTSPOT

  You are using the Hyperdrive feature in Azure Machine Learning to train a model.

  You configure the Hyperdrive experiment 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

  In random sampling, hyperparameter values are randomly selected from the defined search space. Random sampling allows the search space to include both discrete and continuous hyperparameters.

  Box 2: Yes

  learning_rate has a normal distribution with mean value 10 and a standard deviation of 3.

  Box 3: No

  keep_probability has a uniform distribution with a minimum value of 0.05 and a maximum value of 0.1.

  Box 4: No

  number_of_hidden_layers takes on one of the values [3, 4, 5].

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

• Question 525:

  You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.

  You must deploy the model as an Azure Machine Learning real-time web service using the Model.deploy method in the Azure Machine Learning SDK. The deployed web service must return real-time predictions of fraud based on transaction data input.

  You need to create the script that is specified as the entry_script parameter for the InferenceConfig class used to deploy the model.

  What should the entry script do?

  A. Register the model with appropriate tags and properties.
  B. Create a Conda environment for the web service compute and install the necessary Python packages.
  C. Load the model and use it to predict labels from input data.
  D. Start a node on the inference cluster where the web service is deployed.
  E. Specify the number of cores and the amount of memory required for the inference compute.
  C. Load the model and use it to predict labels from input data.

  ---

  Explanation

  The entry script receives data submitted to a deployed web service and passes it to the model. It then takes the response returned by the model and returns that to the client. The script is specific to your model. It must understand the data that the model expects and returns.

  The two things you need to accomplish in your entry script are: Loading your model (using a function called init())

  Running your model on input data (using a function called run())

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

• Question 526:

  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 hyperpara meters.

  Solution: Use the Sobol sampling method over the hyperpara meter space.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 527:

  DRAG DROP

  An organization uses Azure Machine Learning service and wants to expand their use of machine learning.

  You have the following compute environments. The organization does not want to create another compute environment.

  

  You need to determine which compute environment to use for the following scenarios.

  Which compute types should you use? To answer, drag the appropriate compute environments to the correct scenarios. Each compute environment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

  ---

  Box 1: nb_server

  

  Box 2: mlc_cluster

  With Azure Machine Learning, you can train your model on a variety of resources or environments, collectively referred to as compute targets. A compute target can be a local machine or a cloud resource, such as an Azure Machine Learning Compute, Azure HDInsight or a remote virtual machine.

  https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-set-up-training-targets

• Question 528:

  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 datastore in the workspace.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 529:

  HOTSPOT

  You use Data Science Virtual Machines (DSVMs) for Windows and Linux in Azure.

  You need to access the DSVMs.

  Which utilities should you use? To answer, select the appropriate options in the answer area;

  NOTE: Each correct selection is worth one point.

  

  

• Question 530:

  You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events.

  Model performance has degraded due to model input data changes.

  You need to trigger a remediation ML pipeline based on an Azure Machine Learning event.

  Which event should you use?

  A. RunStatusChanged
  B. DatasetDriftDetected
  C. ModelDeployed
  D. RunCompleted
  B. DatasetDriftDetected