Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 351:

  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.

  An IT department creates the following Azure resource groups and resources:

  

  The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace.

  You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed.

  You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics.

  Solution: Attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace. Install the Azure ML SDK on the Surface Book and run Python code to connect to the workspace. Run the training script as an experiment on the mlvm remote compute resource.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  Use the VM as a compute target.

  Note: A compute target is a designated compute resource/environment where you run your training script or host your service deployment. This location may be your local machine or a cloud-based compute resource.

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

• Question 352:

  DRAG DROP

  You need to implement early stopping criteria as stated in the model training requirements.

  Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.

  NOTE: More than one order of answer choices is correct. You will receive the credit for any of the correct orders you select.

  Select and Place:

  

  

  ---

  Step 1: from azureml.train.hyperdrive

  Step 2: Import TruncationCelectionPolicy

  Truncation selection cancels a given percentage of lowest performing runs at each evaluation interval. Runs are compared based on their performance on the primary metric and the lowest X% are terminated.

  Scenario: You must configure hyperparameters in the model learning process to speed the learning phase. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.

  Step 3: early_terminiation_policy = TruncationSelectionPolicy..

  Example:

  from azureml.train.hyperdrive import TruncationSelectionPolicy early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5)

  In this example, the early termination policy is applied at every interval starting at evaluation interval 5. A run will be terminated at interval 5 if its performance at interval 5 is in the lowest 20% of performance of all runs at interval 5.

  Incorrect Answers:

  Median:

  Median stopping is an early termination policy based on running averages of primary metrics reported by the runs. This policy computes running averages across all training runs and terminates runs whose performance is worse than the median of the running averages.

  Slack:

  Bandit is a termination policy based on slack factor/slack amount and evaluation interval. The policy early terminates any runs where the primary metric is not within the specified slack factor / slack amount with respect to the best performing training run.

  References:

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

• Question 353:

  You manage an Azure Machine Learning workspace. You have an environment for training jobs which uses an existing Docker image. A new version of the Docker image is available.

  You need to use the latest version of the Docker image for the environment configuration by using the Azure Machine Learning SDK v2-What should you do?

  A. Modify the conda.file to specify the new version of the Docker image.
  B. Use the Environment class to create a new version of the environment.
  C. Use the create.or.update method to change the tag of the image.
  D. Change the description parameter of the environment configuration.
  B. Use the Environment class to create a new version of the environment.

• Question 354:

  HOTSPOT

  You need to consider the underlined segment to establish whether it is accurate.

  

  

  ---

  The box-plot algorithm can be used to display outliers.

  https://medium.com/analytics-vidhya/what-is-an-outliers-how-to-detect-and-remove-them-which-algorithm-are-sensitive-towards-outliers-2d501993d59

• Question 355:

  You plan to provision an Azure Machine Learning Basic edition workspace for a data science project.

  You need to identify the tasks you will be able to perform in the workspace.

  Which three tasks will you be able to perform? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Create a Compute Instance and use it to run code in Jupyter notebooks.
  B. Create an Azure Kubernetes Service (AKS) inference cluster.
  C. Use the designer to train a model by dragging and dropping pre-defined modules.
  D. Create a tabular dataset that supports versioning.
  E. Use the Automated Machine Learning user interface to train a model.
  A. Create a Compute Instance and use it to run code in Jupyter notebooks.
  C. Use the designer to train a model by dragging and dropping pre-defined modules.
  E. Use the Automated Machine Learning user interface to train a model.

• Question 356:

  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 False.

  Set the value of the token_auth_enabled property to True.

  Deploy the model to the service.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Instead use only auth_enabled = TRUE

  Note: 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 357:

  HOTSPOT

  You use an Azure Machine Learning workspace.

  You create the following Python 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: No

  Environment is a required parameter. The environment to use for the run. If no environment is specified, azureml.core.runconfig.DEFAULT_CPU_IMAGE will be used as the Docker image for the run.

  The following example shows how to instantiate a new environment. from azureml.core import Environment

  myenv = Environment(name="myenv")

  Box 2: Yes

  Parameter compute_target: The compute target where training will happen. This can either be a ComputeTarget object, the name of an existing ComputeTarget, or the string "local". If no compute target is specified, your local machine will be used.

  Box 3: Yes

  Parameter source_directory. A local directory containing code files needed for a run.

  Parameter script. The file path relative to the source_directory of the script to be run.

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

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

• Question 358:

  You create a multi-class image classification deep learning model that uses the PyTorch deep learning framework.

  You must configure Azure Machine Learning Hyperdrive to optimize the hyperparameters for the classification model.

  You need to define a primary metric to determine the hyperparameter values that result in the model with the best accuracy score.

  Which three actions must you perform? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to maximize.
  B. Add code to the bird_classifier_train.py script to calculate the validation loss of the model and log it as a float value with the key loss.
  C. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to minimize.
  D. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to accuracy.
  E. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to loss.
  F. Add code to the bird_classifier_train.py script to calculate the validation accuracy of the model and log it as a float value with the key accuracy.
  A. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to maximize.
  D. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to accuracy.
  F. Add code to the bird_classifier_train.py script to calculate the validation accuracy of the model and log it as a float value with the key accuracy.

  ---

  Explanation

  AD:

  primary_metric_name="accuracy", primary_metric_goal=PrimaryMetricGoal.MAXIMIZE

  Optimize the runs to maximize "accuracy". Make sure to log this value in your training script.

  Note:

  primary_metric_name: The name of the primary metric to optimize. The name of the primary metric needs to exactly match the name of the metric logged by the training script. primary_metric_goal: It can be either PrimaryMetricGoal.MAXIMIZE or PrimaryMetricGoal.MINIMIZE and determines whether the primary metric will be maximized or minimized when evaluating the runs.

  F: The training script calculates the val_accuracy and logs it as "accuracy", which is used as the primary metric.

• Question 359:

  DRAG DROP

  You create an Azure Machine Learning workspace and an Azure Synapse Analytics workspace with a Spark pool. The workspaces are contained within the same Azure subscription.

  You must manage the Synapse Spark pool from the Azure Machine Learning workspace.

  You need to attach the Synapse Spark pool in Azure Machine Learning by using the Python SDK v2.

  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:

  

  

  ---

  Explanation:

  Step 1: Create an instance of azure.ai.ml.MLClient class First create an instance of azure.ai.ml.MLClient class.

  Details:

  Attach and manage a Synapse Spark pool in Azure Machine Learning

  Azure Machine Learning Python SDK (preview) provides convenient functions for attaching and managing Synapse Spark pool, using Python code in Azure Machine Learning Notebooks.

  To attach a Synapse Compute using Python SDK, first create an instance of azure.ai.ml.MLClient class (Step 1). This provides convenient functions for interaction with Azure Machine Learning services. The following code sample uses azure.identity.DefaultAzureCredential for connecting to a workspace in resource group of a specified Azure subscription. In the following code sample, define the SynapseSparkCompute with the parameters (Step 2):

  name - user-defined name of the new attached Synapse Spark pool. resource_id - resource ID of the Synapse Spark pool created earlier in the Azure Synapse Analytics workspace.

  An azure.ai.ml.MLClient.begin_create_or_update() function call attaches the defined Synapse Spark pool to the Azure Machine Learning workspace. (Step 3)

  Step 2: Define the Spark pool with the SynapseSparkCompute class. from azure.ai.ml import MLClient (Step 1 prerequisite) from azure.ai.ml.entities import SynapseSparkCompute (Step 2 prerequisite) from azure.identity import DefaultAzureCredential subscription_id = "<SUBSCRIPTION_ID>" resource_group = "<RESOURCE_GROUP>" workspace_name = "<AML_WORKSPACE_NAME>"

  ml_client = MLClient( DefaultAzureCredential(), subscription_id, resource_group, workspace_name ) (Step 1) synapse_name = "<ATTACHED_SPARK_POOL_NAME>" synapse_resource = "/subscriptions/<SUBSCRIPTION_ID>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.Synapse/workspaces/<SYNAPSE_WORKSPACE_NAME>/bigDataPools/<SPARK_POOL_NAME>"

  synapse_comp = SynapseSparkCompute(name=synapse_name, resource_id=synapse_resource) (Step 2) ml_client.begin_create_or_update(synapse_comp) (Step 3)

  Step 3: Attach the Synapse Spark pool with the ml_client.begin_create_or_update() function

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-manage-synapse-spark-pool

• Question 360:

  You have an Azure Machine Learning workspace.

  You plan to use automated machine learning to train a natural language processing model for multi-class classification.

  You need to provide a dataset for training the model.

  How should you format the data?

  A. Exactly one text column and one or more label columns
  B. One or more text columns and exactly one label column
  C. One or more text columns and one or more label columns
  D. Exactly one text column and exactly one label column
  B. One or more text columns and exactly one label column