Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 181:

  You use Azure Machine Learning designer to load the following datasets into an experiment:

  Dataset1

  

  Dataset2

  

  You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

  Solution: Use the Execute Python Script module.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 182:

  HOTSPOT

  You manage an Azure Machine Learning workspace. You submit a training job with the Azure Machine Learning Python SDK v2. You must use MLflow to log metrics, model parameters, and model artifacts automatically when training a model.

  You start by writing the following code segment:

  import mlflow

  mlflow.autolog(log_models=False, exclusive=True)

  For each of the following statements, select Yes if the statement is true. Otherwise, select No.

  

  

• Question 183:

  You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.

  You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module.

  Which two hyperparameters should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Number of hidden nodes
  B. Learning Rate
  C. The type of the normalizer
  D. Number of learning iterations
  E. Hidden layer specification
  D. Number of learning iterations
  E. Hidden layer specification

  ---

  Explanation

  D: For Number of learning iterations, specify the maximum number of times the algorithm should process the training cases.

  E: For Hidden layer specification, select the type of network architecture to create. Between the input and output layers you can insert multiple hidden layers. Most predictive tasks can be accomplished easily with only one or a few hidden layers.

  References:

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

  network

• Question 184:

  You have been tasked with constructing a machine learning model that translates language text into a different language text.

  The machine learning model must be constructed and trained to learn the sequence of the.

  Recommendation: You make use of Recurrent Neural Networks (RNNs).

  Will the requirements be satisfied?

  A. Yes
  B. No
  A. Yes

• Question 185:

  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 preview the data profile in Azure Machine Learning studio once the dataset is created.

  You need to train the model.

  Which four 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 a file dataset.

  Create and load dataset

  Before you configure your experiment, upload your data file to your workspace in the form of an Azure Machine Learning dataset. Doing so, allows you to ensure that your data is formatted appropriately for your experiment.

  Step 2: Create a compute cluster

  Configure job

  After you load and configure your data, you can set up your experiment. This setup includes experiment design tasks such as, selecting the size of your compute environment and specifying what column you want to predict.

  1. Select the Create new radio button.

  2. Populate the Configure Job form as follows:

  a. Enter this experiment name: my-1st-automl-experiment b. Select y as the target column, what you want to predict. This column indicates whether the client subscribed to a term deposit or not. c. Select compute cluster as your compute type. (Step 2) d. *Details how to configure a Compute cluster omitted* iii. Select Create to create your compute target.

  This takes a couple minutes to complete.

  After creation, select your new compute target from the drop-down list.

  e. Select Next.

  3. On the Select task and settings form, complete the setup for your automated ML experiment by specifying the machine learning task type and configuration settings. (Step 3)

  * Details omitted* 4. Select Finish to run the experiment. The Job Detail screen opens with the Job status at the top as the experiment preparation begins. This status updates as the experiment progresses. Notifications also appear in the top right corner of the studio to inform you of the status of your experiment. (Step 4)

  Step 3: Create an experiment.

  Step 4: Create an automated ML Job.

  https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-first-experiment-automated-ml

• Question 186:

  You need to resolve the local machine learning pipeline performance issue.

  What should you do?

  A. Increase Graphic Processing Units (GPUs).
  B. Increase the learning rate.
  C. Increase the training iterations.
  D. Increase Central Processing Units (CPUs).
  A. Increase Graphic Processing Units (GPUs).

• Question 187:

  HOTSPOT

  You train classification and regression models by using automated machine learning. You must evaluate automated machine learning experiment results. The results include how a classification model is making systematic errors in its predictions and the relationship between the target feature and the regression model's predictions. You must use charts generated by automated machine learning.

  You need to choose a chart type for each model type.

  Which chart types should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 188:

  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 create a model to forecast weather conditions based on historical data.

  You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Note: Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.

  Compare with this example, the pipeline train step depends on the process_step_output output of the pipeline process step:

  from azureml.pipeline.core import Pipeline, PipelineData from azureml.pipeline.steps import PythonScriptStep datastore = ws.get_default_datastore()

  process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])

  https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py

• Question 189:

  You are implementing hyperparameter tuning for a model training from a notebook. The notebook is in an Azure Machine Learning workspace.

  You must configure a grid sampling method over the search space for the num_hidden_layers and batch_size hyperparameters.

  You need to identify the hyperparameters for the grid sampling.

  Which hyperparameter sampling approach should you use?

  A. uniform
  B. qlognormal
  C. choice
  D. normal
  C. choice

  ---

  Explanation

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

• Question 190:

  HOTSPOT

  You use an Azure Machine Learning workspace, Azure Data Factory pipeline, and a dataset monitor that runs on a schedule to detect data drift.

  You need to implement an automated workflow to trigger when the dataset monitor detects data drift and launch the Azure Data Factory pipeline to update the dataset. The solution must minimize the effort to configure the workflow.

  How should you configure the workflow? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: Azure Event Grid subscription

  Trigger applications, processes, or CI/CD workflows based on Azure Machine Learning events

  You can set up event-driven applications, processes, or CI/CD workflows based on Azure Machine Learning events, such as failure notification emails or ML pipeline runs, when certain conditions are detected by Azure Event Grid.

  You can use Event Grid to react to Azure Machine Learning events, such as the completion of training runs, the registration and deployment of models, and the detection of data drift, by using modern serverless architectures.

  Box 2: Azure Logic App

  An Azure Logic App allows for more complex triggering logic or behavior.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-event-grid

  https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-trigger-published-pipeline