Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 291:

  HOTSPOT

  You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2. You create a General Purpose v2 Azure storage account named mlstorage1. The storage account includes a publicly accessible container named mlcontainer1.

  The container stores 10 blobs with files in the CSV format.

  You must develop Python SDK v2 code to create a data asset referencing all blobs in the container named mlcontainer1.

  You need to complete the Python SDK v2 code.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: abfss

  Supported paths

  When you provide a data input/output to a Job, you must specify a path parameter that points to the data location. This table shows both the different data locations that Azure Machine Learning supports, and examples for the path parameter:

  * A path on Azure Storage

  https://<account_name>.blob.core.windows.net/<container_name>/<path>

  abfss://<file_system>@<account_name>.dfs.core.windows.net/<path>

  * A path to a Data Asset

  azureml:<my_data>:<version>

  * Etc.

  Note:

  # Supported paths include:

  # local: './<path>'

  # blob: '

  https://<account_name>.blob.core.windows.net/<container_name>/<path>'

  # ADLS gen2: 'abfss://<file_system>@<account_name>.dfs.core.windows.net/<path>/'

  # Datastore: 'azureml://datastores/<data_store_name>/paths/<path>'

  Box 2: uri_folder

  You can create three data asset types:

  * Folder, Reference a single folder

  V2 API: uri_folder

  V1 API: FileDataset

  You must read/write a folder of parquet/CSV files into Pandas/Spark.

  Deep-learning with images, text, audio, video files located in a folder.

  Incorrect:

  Not mltable:

  * Table, Reference a data table

  V2 API: mltable

  V1 API: TabularDataset

  You have a complex schema subject to frequent changes, or you need a subset of large tabular data.

  AutoML with Tables.

  Not uri_file:

  * File, Reference a single file

  V2 API: uri_file

  V1 API: FileDataset

  Read/write a single file - the file can have any format.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-read-write-data-v2

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-data-assets

• Question 292:

  You have an Azure Machine Learning workspace.

  You plan to use the workspace to set up automated machine learning training for an image classification model.

  You need to choose the primary metric to optimize the model training.

  Which primary metric should you choose?

  A. r2_score
  B. mean_absolute_error
  C. iou
  D. median_absolute_error
  C. iou

• Question 293:

  HOTSPOT

  You need to replace the missing data in the AccessibilityToHighway columns.

  How should you configure the Clean Missing Data module? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Replace using MICE

  Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.

  Scenario: The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.

  Box 2: Propagate

  Cols with all missing values indicate if columns of all missing values should be preserved in the output.

  References:

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

• Question 294:

  You create a workspace by using Azure Machine Learning Studio.

  You must run a Python SDK v2 notebook in the workspace by using Azure Machine Learning Studio. You must preserve the current values of variables set in the notebook for the current instance.

  You need to maintain the state of the notebook.

  What should you do?

  A. Change the compute.
  B. Change the current kernel
  C. Stop the compute.
  D. Stop the current kernel.
  D. Stop the current kernel.

• Question 295:

  DRAG DROP

  You have several machine learning models registered in an Azure Machine Learning workspace.

  You must use the Fairlearn dashboard to assess fairness in a selected model.

  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:

  

  

  ---

  Step 1: Select a model feature to be evaluated.

  Step 2: Select a binary classification or regression model.

  Register your models within Azure Machine Learning. For convenience, store the results in a dictionary, which maps the id of the registered model (a string in name:version format) to the predictor itself.

  Example:

  model_dict = {} lr_reg_id = register_model("fairness_logistic_regression", lr_predictor) model_dict[lr_reg_id] = lr_predictor

  svm_reg_id = register_model("fairness_svm", svm_predictor) model_dict[svm_reg_id] = svm_predictor

  Step 3: Select a metric to be measured Precompute fairness metrics.

  Create a dashboard dictionary using Fairlearn's metrics package.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml

• Question 296:

  You retrain an existing model.

  You need to register the new version of a model while keeping the current version of the model in the registry.

  What should you do?

  A. Register a model with a different name from the existing model and a custom property named version with the value 2.
  B. Register the model with the same name as the existing model.
  C. Save the new model in the default datastore with the same name as the existing model. Do not register the new model.
  D. Delete the existing model and register the new one with the same name.
  B. Register the model with the same name as the existing model.

  ---

  Explanation

  Model version: A version of a registered model. When a new model is added to the Model Registry, it is added as Version 1. Each model registered to the same model name increments the version number.

  https://docs.microsoft.com/en-us/azure/databricks/applications/mlflow/model-registry

• Question 297:

  You use differential privacy to ensure your reports are private. The calculated value of the epsilon for your data is 1.8.

  You need to modify your data to ensure your reports are private.

  Which epsilon value should you accept for your data?

  A. between 0 and 1
  B. between 2 and 3
  C. between 3 and 10
  D. more than 10
  A. between 0 and 1

• Question 298:

  HOTSPOT

  You collect data from a nearby weather station. You have a pandas dataframe named <pdf2txt-u>weather_df</pdf2txt-u> that includes the following data:

  

  The data is collected every 12 hours: noon and midnight.

  You plan to use automated machine learning to create a time-series model that predicts temperature over the next seven days. For the initial round of training, you want to train a maximum of 50 different models.

  You must use the Azure Machine Learning SDK to run an automated machine learning experiment to train these models.

  You need to configure the automated machine learning run.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: forcasting

  Task: The type of task to run. Values can be 'classification', 'regression', or 'forecasting' depending on the type of automated ML problem to solve.

  Box 2: temperature

  The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column).

  Box 3: observation_time

  time_column_name: The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. This setting is being deprecated. Please use forecasting_parameters instead.

  Box 4: 7

  "predicts temperature over the next seven days" max_horizon: The desired maximum forecast horizon in units of time-series frequency. The default value is 1.

  Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster should predict out. When task type is forecasting, this parameter is required.

  Box 5: 50

  "For the initial round of training, you want to train a maximum of 50 different models."

  Iterations: The total number of different algorithm and parameter combinations to test during an automated ML experiment.

  https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig

• Question 299:

  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 a data scientist using Azure Machine Learning Studio.

  You need to normalize values to produce an output column into bins to predict a target column.

  Solution: Apply a Quantiles binning mode with a PQuantile normalization.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

• Question 300:

  HOTSPOT

  You arc I mating a deep learning model to identify cats and dogs. You have 25,000 color images.

  You must meet the following requirements:

  1. Reduce the number of training epochs.

  2. Reduce the size of the neural network.

  3. Reduce over-fitting of the neural network.

  You need to select the image modification values.

  Which value should you use? To answer, select the appropriate Options in the answer area;

  NOTE: Each correct selection is worth one point.