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NEW QUESTION 28
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. Cluster Centroids
- B. Synthetic Minority Oversampling Technique (SMOTE)
- C. Partition and Sample
- D. Tomek links
Answer: B
NEW QUESTION 29
You plan to use automated machine learning to train a regression model. You have data that has features which have missing values, and categorical features with few distinct values.
You need to configure automated machine learning to automatically impute missing values and encode categorical features as part of the training task.
Which parameter and value pair should you use in the AutoMLConfig class?
- A. enable_tf = True
- B. featurization = 'auto'
- C. enable_voting_ensemble = True
- D. task = 'classification'
- E. exclude_nan_labels = True
Answer: B
Explanation:
Explanation
Featurization str or FeaturizationConfig
Values: 'auto' / 'off' / FeaturizationConfig
Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used.
Column type is automatically detected. Based on the detected column type preprocessing/featurization is done as follows:
Categorical: Target encoding, one hot encoding, drop high cardinality categories, impute missing values.
Numeric: Impute missing values, cluster distance, weight of evidence.
DateTime: Several features such as day, seconds, minutes, hours etc.
Text: Bag of words, pre-trained Word embedding, text target encoding.
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.auto
NEW QUESTION 30
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 analyzing a numerical dataset which contains missing values in several columns.
You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.
You need to analyze a full dataset to include all values.
Solution: Calculate the column median value and use the median value as the replacement for any missing value in the column.
Does the solution meet the goal?
- A. No
- B. Yes
Answer: A
Explanation:
Explanation
Use the Multiple Imputation by Chained Equations (MICE) method.
References:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
NEW QUESTION 31
You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Service (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.
You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a 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:
1 - Deploy a real-time endpoint on the inference cluster.
2 - Create and run a batch inference pipeline on the compute cluster.
3 - Create and run a real-time inference pipeline on the compute cluster.
4 - Use the automated ML user interface to train a classification model on the compute cluster.
NEW QUESTION 32
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