Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 601:

  You create an Azure Machine Learning workspace. The workspace contains a dataset named sample_dataset, a compute instance, and a compute cluster.

  You must create a two-stage pipeline that will prepare data in the dataset and then train and register a model based on the prepared data.

  The first stage of the pipeline contains the following code:

  

  You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage.

  Which storage location should you use?

  A. workspaceblobstore datastore
  B. workspacefilestore datastore
  C. compute instance
  D. compute_cluster
  A. workspaceblobstore datastore

  ---

  Explanation

  The OutputFileDatasetConfig allows you to specify how you want a particular local path on the compute target to be uploaded to the specified destination.

  Parameters

  Name

  destination

  The destination to copy the output to. If set to None, we will copy the output to the workspaceblobstore datastore, under the path /dataset/{run-id}/{output-name}, where run-id is the Run's ID and the output-name is the output name from the name parameter above. T

  https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.data.output_dataset_config.outputfiledatasetconfig

• Question 602:

  You have an Azure Machine Learning workspace.

  You plan to tune a model hyperparameter when you train the model.

  You need to define a search space that returns a normally distributed value.

  Which parameter should you use?

  A. QUniform
  B. LogUniform
  C. Uniform
  D. LogNormal
  D. LogNormal

  ---

  
  

• Question 603:

  HOTSPOT

  You use Azure Machine Learning to train and register a model.

  You must deploy the model into production as a real-time web service to an inference cluster named service-compute that the IT department has created in the Azure Machine Learning workspace.

  Client applications consuming the deployed web service must be authenticated based on their Azure Active Directory service principal.

  You need to write a script that uses the Azure Machine Learning SDK to deploy the model. The necessary modules have been imported.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 604:

  You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes.

  The code implements a Bandit termination policy with slack_factor set to 0.2 and a sweep job with max_concurrent_trials set to 10.

  You must increase the effectiveness of the tuning process by improving sampling convergence.

  You need to select which sampling convergence to use.

  What should you select?

  A. Set the value of slack_factor of early_termination policy to 0.1.
  B. Set the value of max_concurrent_trials to 4.
  C. Set the value of slack_factor of early_termination policy to 0.9.
  D. Set the value of max_concurrent_trials to 20.
  B. Set the value of max_concurrent_trials to 4.

• Question 605:

  HOTSPOT

  You are running Python code interactively in a Conda environment. The environment includes all required Azure Machine Learning SDK and MLflow packages.

  You must use MLflow to log metrics in an Azure Machine Learning experiment named mlflow-experiment.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())

  In the following code, the get_mlflow_tracking_uri() method assigns a unique tracking URI address to the workspace, ws, and set_tracking_uri() points the MLflow tracking URI to that address.

  mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())

  Box 2: mlflow.set_experiment(experiment_name)

  Set the MLflow experiment name with set_experiment() and start your training run with start_run().

  Box 3: mlflow.start_run()

  Box 4: mlflow.log_metric

  Then use log_metric() to activate the MLflow logging API and begin logging your training run metrics.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-mlflow

• Question 606:

  You use Azure Machine Learning to train a model.

  You must use a sampling method for tuning hyperparameters. The sampling method must pick samples based on how the model performed with previous samples.

  You need to select a sampling method.

  Which sampling method should you use?

  A. Grid
  B. Bayesian
  C. Random
  B. Bayesian

• Question 607:

  You use Azure Machine Learning studio to analyze a dataset containing a decimal column named column1. You need to verity that the column1 values are normally distributed.

  Which static should you use?

  A. Profile
  B. Type
  C. Max
  D. Mean
  A. Profile

• Question 608:

  DRAG DROP

  You are in the process of constructing a regression model.

  You would like to make it a Poisson regression model. To achieve your goal, the feature values need to meet certain conditions.

  Which of the following are relevant conditions with regards to the label data? Answer by dragging the correct options from the list to the answer area.

  Select and Place:

  

  

  ---

  Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions: The response variable has a Poisson distribution.

  Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.

  A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with non-whole numbers.

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

• Question 609:

  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 Join Data module.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Correct Solution: Use the Add Rows module.

  Note: Add Rows component

  Use this component to concatenate two datasets. In concatenation, the rows of the second dataset are added to the end of the first dataset.

  Concatenation of rows is useful in scenarios such as these:

  You have generated a series of evaluation statistics, and you want to combine them into one table for easier reporting.

  You have been working with different datasets, and you want to combine the datasets to create a final dataset.

  https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/add-rows

• Question 610:

  HOTSPOT

  You manage an Azure Machine Learning workspace. The development environment is configured with a Serverless Spark compute in Azure Machine Learning Notebooks.

  You perform interactive data wrangling to clean up the Titanic dataset and store it as a new dataset (Line numbers are used for reference only.)

  

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

  NOTE: Bach correct selection is worth one point.