Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 191:

  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. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.

  You plan to add a new Jupyter kernel that will be accessible from the same terminal session.

  You need to perform the task that must be completed before you can add the new kernel.

  Solution: Delete the Python 3.6 - AzureML kernel.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

• Question 192:

  DRAG DROP

  You need to implement an early stopping criteria policy for model training.

  Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments 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:

  

  

  ---

  You need to implement an early stopping criterion on models that provides savings without terminating promising jobs.

  Truncation selection cancels a given percentage of lowest performing runs at each evaluation interval. Runs are compared based on their performance on the primary metric and the lowest X% are terminated.

  Example:

  from azureml.train.hyperdrive import TruncationSelectionPolicy early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5)

  Incorrect Answers:

  Bandit is a termination policy based on slack factor/slack amount and evaluation interval. The policy early terminates any runs where the primary metric is not within the specified slack factor / slack amount with respect to the best performing training run.

  Example:

  from azureml.train.hyperdrive import BanditPolicy early_termination_policy = BanditPolicy(slack_factor = 0.1, evaluation_interval=1, delay_evaluation=5 References:

  https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-tune-hyperparameters

• Question 193:

  You are performing feature engineering on a dataset.

  You must add a feature named CityName and populate the column value with the text London.

  You need to add the new feature to the dataset.

  Which Azure Machine Learning Studio module should you use?

  A. Edit Metadata
  B. Filter Based Feature Selection
  C. Execute Python Script
  D. Latent Dirichlet Allocation
  C. Execute Python Script

• Question 194:

  You need to select a pre built development environment for a series of data science experiments. You must use the R language for the experiments.

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

  NOTE: Each correct selection is worth one point.

  A. MI.NET Library on a local environment
  B. Azure Machine Learning Studio
  C. Data Science Virtual Machine (OSVM)
  D. Azure Data bricks
  E. Azure Cognitive Services
  A. MI.NET Library on a local environment
  B. Azure Machine Learning Studio
  D. Azure Data bricks

• Question 195:

  HOTSPOT

  You have a dataset created for multiclass classification tasks that contains a normalized numerical feature set with 10,000 data points and 150 features.

  You use 75 percent of the data points for training and 25 percent for testing. You are using the scikit-learn machine learning library in Python. You use X to denote the feature set and Y to denote class labels.

  You create the following Python data frames:

  

  You need to apply the Principal Component Analysis (PCA) method to reduce the dimensionality of the feature set to 10 features in both training and testing sets.

  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: PCA(n_components = 10)

  Need to reduce the dimensionality of the feature set to 10 features in both training and testing sets.

  Example:

  from sklearn.decomposition import PCA pca = PCA(n_components=2) ;2 dimensions principalComponents = pca.fit_transform(x)

  Box 2: pca

  fit_transform(X[, y])fits the model with X and apply the dimensionality reduction on X.

  Box 3: transform(x_test)

  transform(X) applies dimensionality reduction to X.

  References:

  https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html

• Question 196:

  You make use of Azure Machine Learning Studio to develop a linear regression model. You perform an experiment to assess various algorithms.

  Which of the following is an algorithm that reduces the variances between actual and predicted values?

  A. Fast Forest Quantile Regression
  B. Poisson Regression
  C. Boosted Decision Tree Regression
  D. Linear Regression
  D. Linear Regression

• Question 197:

  HOTSPOT

  You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1.

  You must remove a kernel named kernel1 from compute1. You connect to compute1 by using a terminal window from workspace1.

  You need to enter a command in the terminal window to remove kernel.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: jupiter (sic!)

  Remove added kernels

  To remove an added Jupyter kernel from the compute instance, you must remove the kernelspec, and (optionally) the conda environment. You can also choose to keep the conda environment. You must remove the kernelspec, or your kernel will still be selectable and cause unexpected behavior.

  To remove the kernelspec:

  1. Use the terminal window to list and find the kernelspec:

  jupyter kernelspec list 2. Remove the kernelspec, replacing UNWANTED_KERNEL with the kernel you'd like to remove:

  jupyter kernelspec uninstall UNWANTED_KERNEL

  Box 2: kernelspec

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-access-terminal

• Question 198:

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

  To transform a categorical feature into a binary indicator, you should make use of the Clean Missing Data 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. Convert to Indicator Values
  C. Apply SQL Transformation
  D. Group Categorical Values
  B. Convert to Indicator Values

  ---

  Explanation

  Use the Convert to Indicator Values module in Azure Machine Learning Studio. The purpose of this module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.

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

• Question 199:

  You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

  from azureml.core import Run

  import pandas as pd

  run = Run.get_context()

  data = pd.read_csv('data.csv')

  label_vals = data['label'].unique()

  # Add code to record metrics here

  run.complete()

  The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

  You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

  Solution: Replace the comment with the following code:

  run.log_table('Label Values', label_vals)

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Instead use the run_log function to log the contents in label_vals:

  for label_val in label_vals:

  run.log('Label Values', label_val)

  https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

• Question 200:

  DRAG DROP

  You train and register a model by using the Azure Machine Learning SDK on a local workstation. Python 3.6 and Visual Studio Code are installed on the workstation.

  When you try to deploy the model into production as an Azure Kubernetes Service (AKS)-based web service, you experience an error in the scoring script that causes deployment to fail.

  You need to debug the service on the local workstation before deploying the service to production.

  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:

  

  

  ---

  Step 1: Install Docker on the workstation

  Prerequisites include having a working Docker installation on your local system.

  Build or download the dockerfile to the compute node.

  Step 2: Create an AksWebservice deployment configuration and deploy the model to it

  To deploy a model to Azure Kubernetes Service, create a deployment configuration that describes the compute resources needed.

  # If deploying to a cluster configured for dev/test, ensure that it was created with enough

  # cores and memory to handle this deployment configuration. Note that memory is also used by # things such as dependencies and AML components.

  deployment_config = AksWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1) service = Model.deploy(ws, "myservice", [model], inference_config, deployment_config, aks_target) service.wait_for_deployment(show_output = True) print(service.state) print(service.get_logs())

  Step 3: Create a LocalWebservice deployment configuration for the service and deploy the model to it

  To deploy locally, modify your code to use LocalWebservice.deploy_configuration() to create a deployment configuration. Then use Model.deploy() to deploy the service.

  Step 4: Debug and modify the scoring script as necessary. Use the reload() method of the service after each modification.

  During local testing, you may need to update the score.py file to add logging or attempt to resolve any problems that you've discovered. To reload changes to the score.py file, use reload(). For example, the following code reloads the script for the service, and then sends data to it.

  Incorrect Answers:

  AciWebservice: The types of web services that can be deployed are LocalWebservice, which will deploy a model locally, and AciWebservice and AksWebservice, which will deploy a model to Azure Container Instances (ACI) and Azure Kubernetes Service (AKS), respectively.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment-local