Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 571:

  HOTSPOT

  You are performing sentiment analysis using a CSV file that includes 12,000 customer reviews written in a short sentence format. You add the CSV file to Azure Machine Learning Studio and configure it as the starting point dataset of an experiment. You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset.

  You must create a new n-gram dictionary from the customer review text and set the maximum n-gram size to trigrams.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Vocabulary mode: Create

  For Vocabulary mode, select Create to indicate that you are creating a new list of n-gram features.

  N-Grams size: 3

  For N-Grams size, type a number that indicates the maximum size of the n-grams to extract and store. For example, if you type 3, unigrams, bigrams, and trigrams will be created.

  Weighting function: Leave blank

  The option, Weighting function, is required only if you merge or update vocabularies. It specifies how terms in the two vocabularies and their scores should be weighted against each other.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/extract-n-gram-features-from-text

• Question 572:

  You need to select an environment that will meet the business and data requirements.

  Which environment should you use?

  A. Azure HDInsight with Spark MLlib
  B. Azure Cognitive Services
  C. Azure Machine Learning Studio
  D. Microsoft Machine Learning Server
  D. Microsoft Machine Learning Server

• Question 573:

  HOTSPOT

  You are creating a machine learning model in Python. The provided dataset contains several numerical columns and one text column. The text column represents a product's category. The product category will always be one of the following:

  1. Bikes

  2. Cars

  3. Vans

  4. Boats

  You are building a regression model using the scikit-learn Python package.

  You need to transform the text data to be compatible with the scikit-learn Python package.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: pandas as df

  Pandas takes data (like a CSV or TSV file, or a SQL database) and creates a Python object with rows and columns called data frame that looks very similar to table in a statistical software (think Excel or SPSS for example. Box 2: transpose[ProductCategoryMapping]

  Reshape the data from the pandas Series to columns.

  https://datascienceplus.com/linear-regression-in-python/

• Question 574:

  You are planning to make use of Azure Machine Learning designer to train models.

  You need choose a suitable compute type.

  Recommendation: You choose Inference cluster.

  Will the requirements be satisfied?

  A. Yes
  B. No
  B. No

  ---

  Explanation

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

• Question 575:

  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

• Question 576:

  DRAG DROP

  You have an Azure Machine Learning workspace. You are running an experiment on your local computer.

  You need to use MLflow Tracking to store metrics and artifacts from your local experiment runs in the workspace.

  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:

  

  

• Question 577:

  You create a binary classification model.

  You need to evaluate the model performance.

  Which two metrics can you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. relative absolute error
  B. precision
  C. accuracy
  D. mean absolute error
  E. coefficient of determination
  B. precision
  C. accuracy

  ---

  Explanation

  The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.

  Note: A very natural question is: `Out of the individuals whom the model, how many were classified correctly (TP)?'

  This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.

  https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

• Question 578:

  HOTSPOT

  You are using the Azure Machine Learning designer to transform a dataset by using an Execute Python Script component and custom code.

  You need to define the method signature for the Execute Python Script component and return value type.

  What should you define? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: azureml_main(dataframe1 = None, dataframe2 = None)

  Run Python code in Azure Machine Learning designer

  Write your Python code

  The designer provides an initial entry point script for you to edit and enter your own Python code.

  In this example, you use Pandas to combine two of the automobile dataset columns - Price and Horsepower - to create a new column, Dollars per horsepower. This column represents how much you pay for each horsepower unit, which could become a useful information point to decide if a specific car is a good deal for its price.

  Select the Execute Python Script component.

  In the pane that appears to the right of the canvas, select the Python script text box.

  import pandas as pd

  def azureml_main(dataframe1 = None, dataframe2 = None):

  dataframe1['Dollar/HP'] = dataframe1.price / dataframe1.horsepower return dataframe1

  Box 2: Pandas Dataframe

  The return value must be a Pandas Dataframe. You can return at most two dataframes as component outputs.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-designer-python

• Question 579:

  You are developing a machine learning model by using Azure Machine Learning. You are using multiple text files in tabular format for model data. You have the following requirements:

  1. You must use AutoML jobs to train the model.

  2. You must use data from specified columns.

  3. The data concept must support lazy evaluation.

  You need to load data into a Pandas dataframe.

  Which data concept should you use?

  A. Data asset
  B. URI
  C. Datastore
  D. MLTable
  D. MLTable

• Question 580:

  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 have an Azure Machine Learning workspace that includes an AmlCompute cluster and a batch endpoint.

  You clone a repository that contains an MLflow model to your local computer.

  You need to ensure that you can deploy the model to the batch endpoint.

  Solution: Add a compute resource to the workspace.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No