Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 381:

  You register a model in an Azure Machine Learning workspace by running the following code:

  

  You are creating a scoring script to use in a real-time service for the model.

  You need to write code in the scoring script to set the path of the registered model so that it can be loaded by the service. You include the necessary import statements.

  Which code segment should you use?

  A. path = Model.get_model_path(`loan_model')
  B. path = `model.pkl'
  C. path = ws.models(`loan_model')
  D. path = `outputs/model.pkl'
  A. path = Model.get_model_path(`loan_model')

  ---

  Explanation

  Registering a model creates a logical container for the one or more files that make up your model. In addition to the content of the model file itself, a registered model also stores model metadata, including model description, tags, and framework information, that is useful when managing and deploying the model in your workspace.

  get_model_path

  Return the path to model.

  Note: The function will search for the model in the following locations.

  If version is None:

  Download from remote to cache (if workspace is provided)

  Load from cache azureml-models/$MODEL_NAME/$LATEST_VERSION/ ./$MODEL_NAME If version is not

  None:

  Load from cache azureml-models/$MODEL_NAME/$SPECIFIED_VERSION/ Download from remote to cache (if workspace is provided) static get_model_path(model_name, version=None, _workspace=None)

  Parameters

  model_name

  str

  Required

  The name of the model to retrieve.

  version

  int

  default value: None

  The version of the model to retrieve. Defaults to the latest version.

  _workspace

  Workspace

  default value: None

  The workspace to retrieve a model from. Can't use remotely. If not specified only local cache is searched.

  Returns

  The path on disk to the model.

  https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.core.model.model

• Question 382:

  HOTSPOT

  You are using a decision tree algorithm. You have trained a model that generalizes well at a tree depth equal to 10.

  You need to select the bias and variance properties of the model with varying tree depth values.

  Which properties should you select for each tree depth? To answer, select the appropriate options in the answer area.

  

  

  ---

  In decision trees, the depth of the tree determines the variance. A complicated decision tree (e.g. deep) has low bias and high variance.

  Note: In statistics and machine learning, the bias-variance tradeoff is the property of a set of predictive models whereby models with a lower bias in parameter estimation have a higher variance of the parameter estimates across samples, and vice versa. Increasing the bias will decrease the variance. Increasing the variance will decrease the bias.

  References:

  https://machinelearningmastery.com/gentle-introduction-to-the-bias-variance-trade-off-in-machine-learning/

• Question 383:

  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 are creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.

  You start by creating a linear regression model.

  You need to evaluate the linear regression model.

  Solution: Use the following metrics: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.

  Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.

  Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

  Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.

  Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.

  Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words:

  ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.

  Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.

  AUC.

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

• Question 384:

  DRAG DROP

  You configure a Deep Learning Virtual Machine for Windows.

  You need to recommend tools and frameworks to perform the following:

  1. Build deep neural network (DNN) models

  2. Perform interactive data exploration and visualization

  Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

  NOTE: Each correct selection is worth one point.

  Select and Place:

  

  

  ---

  Box 1: Vowpal Wabbit

  Use the Train Vowpal Wabbit Version 8 module in Azure Machine Learning Studio (classic), to create a machine learning model by using Vowpal Wabbit.

  Box 2: PowerBI Desktop

  Power BI Desktop is a powerful visual data exploration and interactive reporting tool BI is a name given to a modern approach to business decision making in which users are empowered to find, explore, and share insights from data across the enterprise.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/train-vowpal-wabbit-version-8-model

  https://docs.microsoft.com/en-us/azure/architecture/data-guide/scenarios/interactive-data-exploration

• Question 385:

  You create a classification model with a dataset that contains 100 samples with Class A and 10,000 samples with Class B.

  The variation of Class B is very high.

  You need to resolve imbalances.

  Which method should you use?

  A. Partition and Sample
  B. Cluster Centroids
  C. Tomek links
  D. Synthetic Minority Oversampling Technique (SMOTE)
  D. Synthetic Minority Oversampling Technique (SMOTE)

• Question 386:

  HOTSPOT

  You manage an Azure Machine Learning workspace. You create an experiment named experiment by using the Azure Machine Learning Python SDK v2 and MLflow.

  You are reviewing the results of experiment by using the following code segment:

  

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

  

  

• Question 387:

  You manage an Azure AI Foundry project. You build a multi-turn chatbot application.

  You plan to filter your traces to identify issues while observing how the application is responding. The solution must not use an external knowledge base.

  You need to select an evaluation metric.

  Which built-in evaluator should you use?

  A. GroundednessEvaluator
  B. SelfHarmEvaluator
  C. FIScoreEvaluator
  D. IndirectAttackEvaluator
  A. GroundednessEvaluator

• Question 388:

  You have been tasked with evaluating your model on a partial data sample via k-fold cross-validation.

  You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice.

  Recommendation: You configure the use of the value k=3.

  Will the requirements be satisfied?

  A. Yes
  B. No
  B. No

• Question 389:

  HOTSPOT

  You download a .csv file from a notebook in an Azure Machine Learning workspace to a data/sample.csv folder on a compute instance. The file contains 10,000 records.

  You must generate the summary statistics for the data in the file. The statistics must include the following for each numerical column:

  number of non-empty values average value standard deviation minimum and maximum values 25th, 50th, and 75th percentiles

  You need to complete the Python code that will generate the summary statistics.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: pandas

  Pandas read_csv() - How to read a csv file in Python

  Most of the data is available in a tabular format of CSV files. It is very popular. You can convert them to a pandas DataFrame using the read_csv function. The pandas.read_csv is used to load a CSV file as a pandas dataframe.

  Step 1: pandas

  import pandas as pd

  Step 2: Read the CSV

  # Read the csv file df = pd.read_csv("data1.csv")

  Box 2: describe

  The pandas.describe function is used to get a descriptive statistics summary of a given dataframe. This includes mean, count, std deviation, percentiles, and min-max values of all the features.

  Pandas Describe function on DataFrame

  On applying pandas describe function to a dataframe, the result is also returned as a dataframe . This dataframe will consist of a statistics summary for all the numeric features of the dataframe.

  # Create a dataframe

  df = pd.DataFrame({

  'Subject_1_Marks': [14, 42, 21, 12, 45],

  'Subject_2_Marks': [32, 43, 23, 50, 21],

  'Subject_3_Marks': [45.0, 34.0, 23.0, 8.0, 21.0],

  'Names': ['Saksham', 'Ayushi', 'Abhishek', 'Saksham', 'Saumya']

  }

  )

  # Apply describe function

  df.describe()

  

  Incorrect:

  * rank Reference:

  https://www.machinelearningplus.com/pandas/pandas-read_csv-completed/

  https://www.machinelearningplus.com/pandas/pandas-describe/

• Question 390:

  You plan to run a Python script as an Azure Machine Learning experiment.

  The script must read files from a hierarchy of folders. The files will be passed to the script as a dataset argument.

  You must specify an appropriate mode for the dataset argument.

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

  NOTE: Each correct selection is worth one point.

  A. to_pandas_dataframe ()
  B. as_download()
  C. as_upload()
  D. as mount ()
  D. as mount ()

  ---

  Explanation

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset?view=azure-ml-py