Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 81:

  HOTSPOT

  You plan to use Hyperdrive to optimize the hyperparameters selected when training a model. You create the following code to define options for the hyperparameter experiment:

  

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

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: No

  max_total_runs (50 here)

  The maximum total number of runs to create. This is the upper bound; there may be fewer runs when the sample space is smaller than this value.

  Box 2: Yes

  Policy EarlyTerminationPolicy

  The early termination policy to use. If None - the default, no early termination policy will be used.

  Box 3: No

  Discrete hyperparameters are specified as a choice among discrete values. choice can be:

  1.

  one or more comma-separated values

  2.

  a range object

  3.

  any arbitrary list object

  Reference: https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.hyperdriveconfig https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

• Question 82:

  HOTSPOT

  You are hired as a data scientist at a winery. The previous data scientist used Azure Machine Learning.

  You need to review the models and explain how each model makes decisions.

  Which explainer modules should you use? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Meta explainers automatically select a suitable direct explainer and generate the best explanation info based on the given model and data sets. The meta explainers leverage all the libraries (SHAP, LIME, Mimic, etc.) that we have integrated

  or developed. The following are the meta explainers available in the SDK:

  Tabular Explainer: Used with tabular datasets.

  Text Explainer: Used with text datasets.

  Image Explainer: Used with image datasets.

  Box 1: Tabular

  Box 2: Text

  Box 3: Image

  Incorrect Answers:

  Hierarchical Attention Network (HAN)

  HAN was proposed by Yang et al. in 2016. Key features of HAN that differentiates itself from existing approaches to document classification are (1) it exploits the hierarchical nature of text data and (2) attention mechanism is adapted for

  document classification.

  Reference:

  https://medium.com/microsoftazure/automated-and-interpretable-machine-learning-d07975741298

• Question 83:

  HOTSPOT

  Your Azure Machine Learning workspace has a dataset named real_estate_data. A sample of the data in the dataset follows.

  

  You want to use automated machine learning to find the best regression model for predicting the price column.

  You need to configure an automated machine learning experiment using the Azure Machine Learning SDK.

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

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: training_data

  The training data to be used within the experiment. It should contain both training features and a label column (optionally a sample weights column). If training_data is specified, then the label_column_name parameter must also be specified.

  Box 2: validation_data

  Provide validation data: In this case, you can either start with a single data file and split it into training and validation sets or you can provide a separate data file for the validation set. Either way, the validation_data parameter in your

  AutoMLConfig object assigns which data to use as your validation set.

  Example, the following code example explicitly defines which portion of the provided data in dataset to use for training and validation.

  dataset = Dataset.Tabular.from_delimited_files(data)

  training_data, validation_data = dataset.random_split(percentage=0.8, seed=1)

  automl_config = AutoMLConfig(compute_target = aml_remote_compute,

  task = 'classification', primary_metric = 'AUC_weighted',

  training_data = training_data,

  validation_data = validation_data,

  label_column_name = 'Class' )

  Box 3: label_column_name

  label_column_name:

  The name of the label column. If the input data is from a pandas.DataFrame which doesn't have column names, column indices can be used instead, expressed as integers.

  This parameter is applicable to training_data and validation_data parameters.

  Incorrect Answers:

  X: The training features to use when fitting pipelines during an experiment. This setting is being deprecated. Please use training_data and label_column_name instead.

  Y: The training labels to use when fitting pipelines during an experiment. This is the value your model will predict. This setting is being deprecated. Please use training_data and label_column_name instead.

  X_valid: Validation features to use when fitting pipelines during an experiment. If specified, then y_valid or sample_weight_valid must also be specified. Y_valid: Validation labels to use when fitting pipelines during an experiment.

  Both X_valid and y_valid must be specified together.

  exclude_nan_labels: Whether to exclude rows with NaN values in the label. The default is True.

  y_max: y_max (float)

  Maximum value of y for a regression experiment. The combination of y_min and y_max are used to normalize test set metrics based on the input data range. If not specified, the maximum value is inferred from the data.

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig?view=azure-ml-py

• Question 84:

  HOTSPOT

  You have a dataset that includes home sales data for a city. The dataset includes the following columns.

  

  Each row in the dataset corresponds to an individual home sales transaction.

  You need to use automated machine learning to generate the best model for predicting the sales price based on the features of the house.

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

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Regression

  Regression is a supervised machine learning technique used to predict numeric values.

  Box 2: Price

  Reference:

  https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer

• Question 85:

  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.

  Hot Area:

  

  Correct Answer:

  

• Question 86:

  HOTSPOT

  You publish a batch inferencing pipeline that will be used by a business application. The application developers need to know which information should be submitted to and returned by the REST interface for the published pipeline.

  You need to identify the information required in the REST request and returned as a response from the published pipeline.

  Which values should you use in the REST request and to expect in the response? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: JSON containing an OAuth bearer token

  Specify your authentication header in the request. To run the pipeline from the REST endpoint, you need an OAuth2 Bearer-type authentication header.

  Box 2: JSON containing the experiment name

  Add a JSON payload object that has the experiment name.

  Example:

  rest_endpoint = published_pipeline.endpoint

  response = requests.post(rest_endpoint,

  headers=auth_header,

  json={"ExperimentName": "batch_scoring",

  "ParameterAssignments": {"process_count_per_node": 6}}) run_id = response.json()["Id"]

  Box 3: JSON containing the run ID

  Make the request to trigger the run. Include code to access the Id key from the response dictionary to get the value of the run ID.

• Question 87:

  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.

  Hot Area:

  

  Correct Answer:

  

  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].

  Reference:

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

• Question 88:

  HOTSPOT

  You register the following versions of a model.

  

  You use the Azure ML Python SDK to run a training experiment. You use a variable named run to reference the experiment run. After the run has been submitted and completed, you run 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.

  Hot Area:

  

  Correct Answer:

  

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

• Question 89:

  HOTSPOT

  You are using Azure Machine Learning to train machine learning models. You need to compute target on which to remotely run the training script.

  You run 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.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Yes

  The compute is created within your workspace region as a resource that can be shared with other users.

  Box 2: Yes

  It is displayed as a compute cluster.

  View compute targets

  1.

  To see all compute targets for your workspace, use the following steps:

  2.

  Navigate to Azure Machine Learning studio.

  3.

  Under Manage, select Compute.

  4.

  Select tabs at the top to show each type of compute target.

  

  Box 3: Yes

  min_nodes is not specified, so it defaults to 0.

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute.amlcomputeprovisioningconfiguration

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-studio

• Question 90:

  HOTSPOT

  You are running a training experiment on remote compute in Azure Machine Learning.

  The experiment is configured to use a conda environment that includes the mlflow and azureml-contrib-run packages.

  You must use MLflow as the logging package for tracking metrics generated in the experiment.

  You need to complete the script for the 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.

  Hot Area:

  

  Correct Answer:

  

  Box 1: import mlflow

  Import the mlflow and Workspace classes to access MLflow's tracking URI and configure your workspace.

  Box 2: mlflow.start_run()

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

  Box 3: mlflow.log_metric(' ..')

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

  Box 4: mlflow.end_run()

  Close the run:

  run.endRun()

  Reference:

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