Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 161:

  You need to consider the underlined segment to establish whether it is accurate.

  To improve the amount of low incidence cases in a dataset, you should make use of the SMOTE module.

  Select "No adjustment required" if the underlined segment is accurate. If the underlined segment is inaccurate, select the accurate option.

  A. No adjustment required.
  B. Remove Duplicate Rows
  C. Join Data
  D. Edit Metadata
  A. No adjustment required.

  ---

  Explanation

  Use the SMOTE module in Azure Machine Learning Studio to increase the number of underrepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

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

• Question 162:

  You manage an Azure Machine Learning workspace.

  The Python script named script.py reads an argument named training_data.

  The training_data argument specifies the path to the training data in a file named dataset1.csv.

  You plan to run the script.py Python script as a command job that trains a machine learning model.

  You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.

  Solution: python script.py --training_data dataset1,csv

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

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• Question 163:

  HOTSPOT

  You plan to preprocess text from CSV files. You load the Azure Machine Learning Studio default stop words list.

  You need to configure the Preprocess Text module to meet the following requirements:

  1. Ensure that multiple related words from a single canonical form.

  2. Remove pipe characters from text.

  3. Remove words to optimize information retrieval.

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

  NOTE: Each correct selection is worth one point.

  

  

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  Box 1: Remove stop words

  Remove words to optimize information retrieval.

  Remove stop words: Select this option if you want to apply a predefined stopword list to the text column. Stop word removal is performed before any other processes.

  Box 2: Lemmatization

  Ensure that multiple related words from a single canonical form.

  Lemmatization converts multiple related words to a single canonical form

  Box 3: Remove special characters

  Remove special characters: Use this option to replace any non-alphanumeric special characters with the pipe | character.

  References:

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

• Question 164:

  You create a batch inference pipeline by using the Azure ML SDK. You run the pipeline by using the following code:

  from azureml.pipeline.core import Pipeline from azureml.core.experiment import Experiment

  pipeline = Pipeline(workspace=ws, steps=[parallelrun_step]) pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)

  You need to monitor the progress of the pipeline execution.

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

  NOTE: Each correct selection is worth one point.

  

  A. Option A
  B. Option B
  C. Option C
  D. Option D
  E. Option E
  D. Option D
  E. Option E

  ---

  Explanation

  A batch inference job can take a long time to finish. This example monitors progress by using a Jupyter widget. You can also manage the job's progress by using:

  1. Azure Machine Learning Studio.

  2. Console output from the PipelineRun object.

  from azureml.widgets import RunDetails

  RunDetails(pipeline_run).show()

  pipeline_run.wait_for_completion(show_output=True)

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-parallel-run-step#monitor-the-parallel-run-job

• Question 165:

  HOTSPOT

  You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.

  The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:

  

  You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.

  You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.

  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 166:

  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 Generative Adversarial Networks (GANs).

  Will the requirements be satisfied?

  A. Yes
  B. No
  B. No

• Question 167:

  You create a binary classification model. You use the Fairlearn package to assess model fairness.

  You must eliminate the need to retrain the model.

  You need to implement the Fairlearn package.

  Which algorithm should you use?

  A. fairlearn.reductions.ExponentiatedGradient
  B. fairlearn.postprocessing.ThresholdOptimizer
  C. fairlearn.preprocessing.CorrelationRemover
  D. fairlearn.reductions.GridSearch
  B. fairlearn.postprocessing.ThresholdOptimizer

• Question 168:

  DRAG DROP

  You have a dataset that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifier.

  You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset.

  In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.

  Select and Place:

  

  

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  Step 1: Add a Two-Class Support Vector Machine module to initialize the SVM classifier.

  Step 2: Add a dataset to the experiment

  Step 3: Add a Split Data module to create training and test dataset.

  To generate a set of feature scores requires that you have an already trained model, as well as a test dataset.

  Step 4: Add a Permutation Feature Importance module and connect to the trained model and test dataset.

  Step 5: Set the Metric for measuring performance property to Classification - Accuracy and then run the experiment.

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

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

• Question 169:

  You are analyzing a dataset in Azure Machine Learning Studio.

  You need to generate a statistical summary containing p-values and unique value counts.

  Which two modules can you use? (Choose two.)

  A. Execute Python Script
  B. Export Count Table
  C. Convert to Indicator Values
  D. Summarize Data
  E. Compute linear Correlation
  B. Export Count Table
  E. Compute linear Correlation

• Question 170:

  HOTSPOT

  Complete the sentence by selecting the correct option in the answer area.

  

  

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  Use the Convert to ARFF module in Azure Machine Learning Studio, to convert datasets and results in Azure Machine Learning to the attribute-relation file format used by the Weka toolset. This format is known as ARFF.

  The ARFF data specification for Weka supports multiple machine learning tasks, including data preprocessing, classification, and feature selection. In this format, data is organized by entities and their attributes, and is contained in a single text file.

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/convert-to-arff