Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 11:

  You train and register an Azure Machine Learning model

  You plan to deploy the model to an online endpoint

  You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.

  Solution: Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

• Question 12:

  You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events.

  Model performance has degraded due to model input data changes.

  You need to trigger a remediation ML pipeline based on an Azure Machine Learning event.

  Which event should you use?

  A. RunStatusChanged

  B. DatasetDriftDetected

  C. ModelDeployed

  D. RunCompleted

  Correct Answer: B

• Question 13:

  DRAG DROP

  You need to define a process for penalty event detection.

  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 14:

  DRAG DROP

  You need to define a process for penalty event detection.

  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 15:

  DRAG DROP

  You need to modify the inputs for the global penalty event model to address the bias and variance issue.

  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 16:

  You need to define a modeling strategy for ad response.

  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:

  

  Step 1: Implement a K-Means Clustering model

  Step 2: Use the cluster as a feature in a Decision jungle model.

  Decision jungles are non-parametric models, which can represent non-linear decision boundaries.

  Step 3: Use the raw score as a feature in a Score Matchbox Recommender model

  The goal of creating a recommendation system is to recommend one or more "items" to "users" of the system. Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons, or other entity with

  item preferences.

  Scenario:

  Ad response rated declined.

  Ad response models must be trained at the beginning of each event and applied during the sporting event.

  Market segmentation models must optimize for similar ad response history.

  Ad response models must support non-linear boundaries of features.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/multiclass-decision-jungle

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/score-matchbox-recommender

• Question 17:

  DRAG DROP

  You need to define an evaluation strategy for the crowd sentiment models.

  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:

  

  Scenario:

  Experiments for local crowd sentiment models must combine local penalty detection data.

  Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.

  Note: Evaluate the changed in correlation between model error rate and centroid distance

  In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.

  References:

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

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

• Question 18:

  You need to define an evaluation strategy for the crowd sentiment models.

  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:

  

  Step 1: Define a cross-entropy function activation When using a neural network to perform classification and prediction, it is usually better to use cross-entropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network.

  Step 2: Add cost functions for each target state.

  Step 3: Evaluated the distance error metric.

  References: https://www.analyticsvidhya.com/blog/2018/04/fundamentals-deep-learning-regularization-techniques/

• Question 19:

  DRAG DROP

  You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.

  Which three Azure Machine Learning Studio modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

  Select and Place:

  

  Correct Answer:

  

  You can use the Clip Values module in Azure Machine Learning Studio, to identify and optionally replace data values that are above or below a specified threshold. This is useful when you want to remove outliers or replace them with a mean, a constant, or other substitute value.

  References: https://blogs.msdn.microsoft.com/azuredev/2017/05/27/data-cleansing-tools-in-azuremachine-learning/ https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clipvalues

• Question 20:

  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:

  

  Correct Answer:

  

  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