Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 531:

  HOTSPOT

  You are designing a machine learning solution.

  You have the following requirements:

  Use a training script to train a machine learning model.

  Build a machine learning proof of concept without the use of code or script.

  You need to select a development tool for each requirement.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: Azure Machine Learning SDK for Python (Run Configuration) Use a training script to train a machine learning model.

  Use the ScriptRunConfig class to attach the compute target configuration, and to specify the path/file to the training script train.py.

  The following example shows where you would use ScriptRunConfig as your wrapper object.

  from azureml.core import ScriptRunConfig, Experiment from azureml.core.environment import Environment exp = Experiment(name="myexp", workspace = ws)

  # Instantiate environment

  myenv = Environment(name="myenv")

  # Add training script to run config runconfig = ScriptRunConfig(source_directory=".", script="train.py")

  # Attach compute target to run config runconfig.run_config.target = "local"

  # Attach environment to run config runconfig.run_config.environment = myenv

  # Submit run

  run = exp.submit(runconfig)

  Box 2: Designer

  Build a machine learning proof of concept without the use of code or script.

  Azure Machine Learning designer

  Azure Machine Learning designer is a drag-and-drop interface used to train and deploy models in Azure Machine Learning.

  Incorrect:

  * Azure Machine Learning SDK for Python (Automated Machine learning) It does not use a training script.

  Automated machine learning, also referred to as automated ML or AutoML, is the process of automating the time-consuming, iterative tasks of machine learning model development. It allows data scientists, analysts, and developers to build ML models with high scale, efficiency, and productivity all while sustaining model quality.

  How does AutoML work?

  During training, Azure Machine Learning creates a number of pipelines in parallel that try different algorithms and parameters for you. The service iterates through ML algorithms paired with feature selections, where each iteration produces a model with a training score. The better the score for the metric you want to optimize for, the better the model is considered to "fit" your data. It will stop once it hits the exit criteria defined in the experiment.

  Authoring AutoML models for vision tasks is supported via the Azure Machine Learning Python SDK. The resulting experimentation jobs, models, and outputs can be accessed from the Azure Machine Learning studio UI.

  https://learn.microsoft.com/en-us/azure/machine-learning/concept-designer

  https://learn.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

• Question 532:

  You deploy a model as an Azure Machine Learning real-time web service using the following code.

  

  The deployment fails.

  You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed.

  Which code segment should you run?

  A. service.get_logs()
  B. service.state
  C. service.serialize()
  D. service.update_deployment_state()
  A. service.get_logs()

  ---

  Explanation

  You can print out detailed Docker engine log messages from the service object. You can view the log for ACI, AKS, and Local deployments. The following example demonstrates how to print the logs.

  # if you already have the service object handy print(service.get_logs())

  # if you only know the name of the service (note there might be multiple services with the same name but different version number) print(ws.webservices['mysvc'].get_logs())

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

• Question 533:

  You manage an Azure Machine Learning workspace.

  You experiment with an MLflow model that trains interactively by using a notebook in the workspace.

  You need to log dictionary type artifacts of the experiments in Azure Machine Learning by using MLflow.

  Which syntax should you use?

  A. mlflow.log_artifact(my_dict)
  B. mlflow.log_metric("my_metric", my_dict)
  C. mlflow.log_artifacts(my_dict>
  D. mlflow.log metrics(my dict)
  D. mlflow.log metrics(my dict)

• Question 534:

  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.

  

  

  ---

  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.

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

• Question 535:

  You use the following code to define the steps for a pipeline:

  from azureml.core import Workspace, Experiment, Run
  from azureml.pipeline.core import Pipeline
  from azureml.pipeline.steps import PythonScriptStep
  
  ws = Workspace.from_config()
  
  . . .
  
  step1 = PythonScriptStep(name="step1", ...)
  step2 = PythonScriptStep(name="step2", ...)
  pipeline_steps = [step1, step2]

  You need to add code to run the steps.

  Which two code segments can you use to achieve this goal? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. experiment = Experiment(workspace=ws, name='pipeline-experiment') run = experiment.submit(config=pipeline_steps)
  B. run = Run(pipeline_steps)
  C. pipeline = Pipeline(workspace=ws, steps=pipeline_steps) experiment = Experiment(workspace=ws, name='pipeline-experiment') run = experiment.submit(pipeline)
  D. pipeline = Pipeline(workspace=ws, steps=pipeline_steps) run = pipeline.submit(experiment_name='pipeline-experiment')
  C. pipeline = Pipeline(workspace=ws, steps=pipeline_steps) experiment = Experiment(workspace=ws, name='pipeline-experiment') run = experiment.submit(pipeline)
  D. pipeline = Pipeline(workspace=ws, steps=pipeline_steps) run = pipeline.submit(experiment_name='pipeline-experiment')

  ---

  Explanation

  After you define your steps, you build the pipeline by using some or all of those steps.

  # Build the pipeline.

  Example:

  pipeline1 = Pipeline(workspace=ws, steps=[compare_models])

  # Submit the pipeline to be run pipeline_run1 = Experiment(ws, 'Compare_Models_Exp').submit(pipeline1)

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

• Question 536:

  DRAG DROP

  You need to define an evaluation strategy for the crowd sentiment models.

  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:

  

  

  ---

  Scenario:

  Experiments for local crowd sentiment models must combine local penalty detection data.

  Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.

  Note: Evaluate the changed in correlation between model error rate and centroid distance

  In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.

  References:

  https://en.wikipedia.org/wiki/Nearest_centroid_classifier

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

• Question 537:

  You are building a binary classification model by using a supplied training set.

  The training set is imbalanced between two classes.

  You need to resolve the data imbalance.

  What are three possible ways to achieve this goal? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Penalize the classification
  B. Resample the dataset using undersampling or oversampling
  C. Normalize the training feature set
  D. Generate synthetic samples in the minority class
  E. Use accuracy as the evaluation metric of the model
  A. Penalize the classification
  B. Resample the dataset using undersampling or oversampling
  D. Generate synthetic samples in the minority class

  ---

  Explanation

  A: Try Penalized Models

  You can use the same algorithms but give them a different perspective on the problem. Penalized classification imposes an additional cost on the model for making classification mistakes on the minority class during training. These penalties can bias the model to pay more attention to the minority class.

  B: You can change the dataset that you use to build your predictive model to have more balanced data. This change is called sampling your dataset and there are two main methods that you can use to even-up the classes:

  1. Consider testing under-sampling when you have an a lot data (tens- or hundreds of thousands of instances or more) 2. Consider testing over-sampling when you don't have a lot of data (tens of thousands of records or less)

  D: Try Generate Synthetic Samples

  A simple way to generate synthetic samples is to randomly sample the attributes from instances in the minority class.

  https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/

• Question 538:

  This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.

  You are in the process of carrying out feature engineering on a dataset.

  You want to add a feature to the dataset and fill the column value.

  Recommendation: You must make use of the Edit Metadata Azure Machine Learning Studio module.

  Will the requirements be satisfied?

  A. Yes
  B. No
  B. No

• Question 539:

  HOTSPOT

  You have an Azure blob container that contains a set of TSV files. The Azure blob container is registered as a datastore for an Azure Machine Learning service workspace. Each TSV file uses the same data schema.

  You plan to aggregate data for all of the TSV files together and then register the aggregated data as a dataset in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.

  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.

  

  

  ---

  Box 1: No

  FileDataset references single or multiple files in datastores or from public URLs. The TSV files need to be parsed.

  Box 2: Yes

  to_path() gets a list of file paths for each file stream defined by the dataset.

  Box 3: Yes

  TabularDataset.to_pandas_dataframe loads all records from the dataset into a pandas DataFrame.

  TabularDataset represents data in a tabular format created by parsing the provided file or list of files.

  Note: TSV is a file extension for a tab-delimited file used with spreadsheet software. TSV stands for Tab Separated Values. TSV files are used for raw data and can be imported into and exported from spreadsheet software. TSV files are essentially text files, and the raw data can be viewed by text editors, though they are often used when moving raw data between spreadsheets.

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

• Question 540:

  You need to select a feature extraction method.

  Which method should you use?

  A. Mutual information
  B. Pearson's correlation
  C. Spearman correlation
  D. Mann-Whitney test
  C. Spearman correlation

  ---

  Explanation

  Spearman's rank correlation coefficient assesses how well the relationship between two variables can be described using a monotonic function.

  Note: Both Spearman's and Kendall's can be formulated as special cases of a more general correlation coefficient, and they are both appropriate in this scenario.

  Scenario: The MedianValue and AvgRoomsInHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

  Incorrect Answers:

  B: The Spearman correlation between two variables is equal to the Pearson correlation between the rank values of those two variables; while Pearson's correlation assesses linear relationships, Spearman's correlation assesses monotonic relationships (whether linear or not).

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/feature-selection-modules