Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 121:

  HOTSPOT

  You create a binary classification model to predict whether a person has a disease.

  You need to detect possible classification errors.

  Which error type should you choose for each description? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: True Positive

  A true positive is an outcome where the model correctly predicts the positive class

  Box 2: True Negative

  A true negative is an outcome where the model correctly predicts the negative class.

  Box 3: False Positive

  A false positive is an outcome where the model incorrectly predicts the positive class.

  Box 4: False Negative

  A false negative is an outcome where the model incorrectly predicts the negative class.

  Note: Let's make the following definitions:

  "Wolf" is a positive class.

  "No wolf" is a negative class.

  We can summarize our "wolf-prediction" model using a 2x2 confusion matrix that depicts all four possible outcomes:

  Reference:

  https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative

• Question 122:

  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.

  Hot Area:

  

  Correct Answer:

  

  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.

  Reference:

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

• Question 123:

  You are working on a classification task. You have a dataset indicating whether a student would like to play soccer and associated attributes. The dataset includes the following columns:

  

  You need to classify variables by type.

  Which variable should you add to each category? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  References: https://www.edureka.co/blog/classification-algorithms/

• Question 124:

  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.

  Hot Area:

  

  Correct Answer:

  

  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 125:

  You are retrieving data from a large datastore by using Azure Machine Learning Studio.

  You must create a subset of the data for testing purposes using a random sampling seed based on the system clock.

  You add the Partition and Sample module to your experiment.

  You need to select the properties for the module.

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

  NOTE: Each correct selection is worth one point.

  Hot Area:

  

  Correct Answer:

  

  Box 1: Sampling Create a sample of data This option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.

  1.

  Add the Partition and Sample module to your experiment in Studio, and connect the dataset.

  2.

  Partition or sample mode: Set this to Sampling.

  3.

  Rate of sampling. See box 2 below.

  Box 2: 0

  3. Rate of sampling. Random seed for sampling: Optionally, type an integer to use as a seed value.

  This option is important if you want the rows to be divided the same way every time. The default value is 0, meaning that a starting seed is generated based on the system clock. This can lead to slightly different results each time you run the experiment.

  References: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

• Question 126:

  You have a Python data frame named salesData in the following format:

  

  The data frame must be unpivoted to a long data format as follows:

  

  You need to use the pandas.melt() function in Python to perform the transformation.

  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.

  Hot Area:

  

  Correct Answer:

  

  Box 1: dataFrame

  Syntax: pandas.melt(frame, id_vars=None, value_vars=None, var_name=None, value_name='value', col_level=None)[source]

  Where frame is a DataFrame

  Box 2: shop

  Paramter id_vars id_vars : tuple, list, or ndarray, optional

  Column(s) to use as identifier variables.

  Box 3: ['2017','2018']

  value_vars : tuple, list, or ndarray, optional

  Column(s) to unpivot. If not specified, uses all columns that are not set as id_vars.

  Example:

  df = pd.DataFrame({'A': {0: 'a', 1: 'b', 2: 'c'},

  ... 'B': {0: 1, 1: 3, 2: 5},

  ... 'C': {0: 2, 1: 4, 2: 6}})

  pd.melt(df, id_vars=['A'], value_vars=['B', 'C']) A variable value

  References: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.melt.html

• Question 127:

  DRAG DROP

  You need to implement source control for scripts in an Azure Machine Learning workspace. You use a terminal window in the Azure Machine Learning Notebook tab

  You must authenticate your Git account with SSH.

  You need to generate a new SSH key.

  Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them m the correct order.

  Select and Place:

  

  Correct Answer:

  

• Question 128:

  DRAG DROP

  You create an Azure Machine Learning workspace.

  You must implement dedicated compute for model training in the workspace by using Azure Synapse compute resources. The solution must attach the dedicated compute and start an Azure Synapse session.

  You need to implement the compute resources.

  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:

  

  Correct Answer:

  

• Question 129:

  DRAG DROP

  You have an existing GitHub repository containing Azure Machine Learning project files.

  You need to clone the repository to your Azure Machine Learning shared workspace file system.

  Which four 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.

  NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

  Select and Place:

  

  Correct Answer:

  

• Question 130:

  DRAG DROP

  You use a training pipeline in the Azure Machine Learning designer. You register a datastore named ds1. The datastore contains multiple training data files. You use the Import Data module with the configured datastore.

  You need to retrain a model on a different set of data files.

  Which four 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:

  

  Correct Answer: