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NEW QUESTION 32 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. Answer: ** Explanation: 1 - Import the global model and build the local model using PyTorch. 2 - Build the global model using PyTorch. 3 - Build the global model using TensorFlow. 4 - Import the global model and build the local model using TensorFlow.   **NEW QUESTION 33 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 new experiment in Azure Machine Learning Studio. One class has a much smaller number of observations than tin- other classes in the training set. You need to select an appropriate data sampling strategy to compensate for the class imbalance. Solution: You use the Principal Components Analysis (PCA) sampling mode. Does the solution meet the goal?

• A. No
• B. Yes

Answer: A Explanation: Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode. Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases. Incorrect Answers: The Principal Component Analysis module in Azure Machine Learning Studio (classic) is used to reduce the dimensionality of your training data. The module analyzes your data and creates a reduced feature set that captures all the information contained in the dataset, but in a smaller number of features. Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/principal-component-analysis   NEW QUESTION 34 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. Answer: ** Explanation: Explanation 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-recommende   **NEW QUESTION 35 You plan to use Hyperdrive to optimize the hyperparameters selected when training a model. You create the following code to define options for the hyperparameter experiment For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point. Answer: ** Explanation: Explanation: Box 1: No maxtotalruns (50 here) The maximum total number of runs to create. This is the upper bound; there may be fewer runs when the sample space is smaller than this value. Box 2: Yes Policy EarlyTerminationPolicy The early termination policy to use. If None - the default, no early termination policy will be used. Box 3: No Discrete hyperparameters are specified as a choice among discrete values. choice can be: one or more comma-separated values a range object any arbitrary list object Reference: https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.hyperdriveconfig https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters   **NEW QUESTION 36 ......