Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 311:

  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 a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.

  You must run the script as an Azure ML experiment on a compute cluster named aml-compute.

  You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.

  Example:

  from azureml.train.sklearn import SKLearn

  }

  estimator = SKLearn(source_directory=project_folder,

  compute_target=compute_target,

  entry_script='train_iris.py'

  )

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn

• Question 312:

  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 a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.

  You must run the script as an Azure ML experiment on a compute cluster named aml-compute.

  You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.

  Example: from azureml.train.sklearn import SKLearn }

  estimator = SKLearn(source_directory=project_folder,

  compute_target=compute_target,

  entry_script='train_iris.py'

  )

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn

• Question 313:

  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 create a model to forecast weather conditions based on historical data.

  You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  train_step is missing.

  Reference:

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

• Question 314:

  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 create a model to forecast weather conditions based on historical data.

  You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Note: Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.

  Compare with this example, the pipeline train step depends on the process_step_output output of the pipeline process step:

  from azureml.pipeline.core import Pipeline, PipelineData from azureml.pipeline.steps import PythonScriptStep

  datastore = ws.get_default_datastore()

  process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output],

  outputs=[process_step_output],

  compute_target=aml_compute,

  source_directory=process_directory)

  train_step = PythonScriptStep(script_name="train.py",

  arguments=["--data_for_train", process_step_output],

  inputs=[process_step_output],

  compute_target=aml_compute,

  source_directory=train_directory)

  pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])

  Reference:

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

• Question 315:

  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 create a model to forecast weather conditions based on historical data.

  You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  The two steps are present: process_step and train_step

  The training data input is not setup correctly.

  Note:

  Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.

  PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.

  For example, the pipeline train step depends on the process_step_output output of the pipeline process step:

  from azureml.pipeline.core import Pipeline, PipelineData from azureml.pipeline.steps import PythonScriptStep

  datastore = ws.get_default_datastore()

  process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output],

  outputs=[process_step_output],

  compute_target=aml_compute,

  source_directory=process_directory)

  train_step = PythonScriptStep(script_name="train.py",

  arguments=["--data_for_train", process_step_output],

  inputs=[process_step_output],

  compute_target=aml_compute,

  source_directory=train_directory)

  pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])

  Reference:

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

• Question 316:

  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, Accuracy, Precision, Recall, F1 score, and AUC.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Accuracy, Precision, Recall, F1 score, and AUC are metrics for evaluating classification models.

  Note: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error are OK for the linear regression model.

  Reference:

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

• Question 317:

  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

  Correct Answer: ABD

  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.

  Reference:

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

• Question 318:

  You are a data scientist building a deep convolutional neural network (CNN) for image classification.

  The CNN model you build shows signs of overfitting.

  You need to reduce overfitting and converge the model to an optimal fit.

  Which two actions should you perform? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Add an additional dense layer with 512 input units.

  B. Add L1/L2 regularization.

  C. Use training data augmentation.

  D. Reduce the amount of training data.

  E. Add an additional dense layer with 64 input units.

  Correct Answer: BD

  B: Weight regularization provides an approach to reduce the overfitting of a deep learning neural network model on the training data and improve the performance of the model on new data, such as the holdout test set.

  Keras provides a weight regularization API that allows you to add a penalty for weight size to the loss function.

  Three different regularizer instances are provided; they are:

  1.

  L1: Sum of the absolute weights.

  2.

  L2: Sum of the squared weights.

  3.

  L1L2: Sum of the absolute and the squared weights.

  D: Because a fully connected layer occupies most of the parameters, it is prone to overfitting. One method to reduce overfitting is dropout. At each training stage, individual nodes are either "dropped out" of the net with probability 1-p or kept

  with probability p, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed.

  By avoiding training all nodes on all training data, dropout decreases overfitting.

  Reference: https://machinelearningmastery.com/how-to-reduce-overfitting-in-deep-learning-with-weight-regularization/

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

• Question 319:

  You are with a time series dataset in Azure Machine Learning Studio.

  You need to split your dataset into training and testing subsets by using the Split Data module.

  Which splitting mode should you use?

  A. Recommender Split

  B. Regular Expression Split

  C. Relative Expression Split

  D. Split Rows with the Randomized split parameter set to true

  Correct Answer: D

  Split Rows: Use this option if you just want to divide the data into two parts. You can specify the percentage of data to put in each split, but by default, the data is divided 50-50. Incorrect Answers:

  B: Regular Expression Split: Choose this option when you want to divide your dataset by testing a single column for a value.

  C: Relative Expression Split: Use this option whenever you want to apply a condition to a number column.

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

• Question 320:

  You are performing clustering by using the K-means algorithm.

  You need to define the possible termination conditions.

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

  NOTE: Each correct selection is worth one point.

  A. Centroids do not change between iterations.

  B. The residual sum of squares (RSS) rises above a threshold.

  C. The residual sum of squares (RSS) falls below a threshold.

  D. A fixed number of iterations is executed.

  E. The sum of distances between centroids reaches a maximum.

  Correct Answer: ACD

  AD: The algorithm terminates when the centroids stabilize or when a specified number of iterations are completed.

  C: A measure of how well the centroids represent the members of their clusters is the residual sum of squares or RSS, the squared distance of each vector from its centroid summed over all vectors. RSS is the objective function and our goal is to minimize it.

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

  https://nlp.stanford.edu/IR-book/html/htmledition/k-means-1.html