Question # 1
You are solving a classification task.
The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use? | A. Fisher Linear Discriminant Analysis. | B. Filter Based Feature Selection | C. Synthetic Minority Oversampling Technique (SMOTE) | D. Permutation Feature Importance |
C. Synthetic Minority Oversampling Technique (SMOTE)
Explanation:
Use the SMOTE module in Azure Machine Learning Studio (classic) 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.
You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect. Typically, you use SMOTE when the class you want to analyze is under-represented.
[Reference:, https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote, , ]
Question # 2
You manage an Azure Machine learning workspace. The workspace includes an Azure Machine Learning kubernetes compute target configured as an Azure Kubemetes Service (AKS) cluster named AKS1 AKS1 is configured to enable the targeting of different nodes to train workloads.
You must run a command job on AK51 by using the Azure ML Python SDK v2? The command job must select different types of compute nodes. The compare node types must be specified by using a command parameter.
You need to configure the command parameter.
Which parameter should you use?
| A. compute | B. environment | C. instance_type | D. limits |
Explanation:
from azure.ai.ml import command
# define the command
command_job = command(
command="python -c "print('Hello world!')"",
environment="AzureML-lightgbm-3.2-ubuntu18.04-py37-cpu@latest",
compute="",
instance_type=""
Question # 3
You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data module to handle the missing data.
You need to select a data cleaning method.
Which method should you use? | A. Synthetic Minority Oversampling Technique (SMOTE) | B. Replace using MICE | C. Replace using; Probabilistic PCA | D. Normalization |
C. Replace using; Probabilistic PCA
Explanation:
Replace using Probabilistic PCA: Compared to other options, such as Multiple Imputation using Chained Equations (MICE), this option has the advantage of not requiring the application of predictors for each column. Instead, it approximates the covariance for the full dataset. Therefore, it might offer better performance for datasets that have missing values in many columns.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data
Question # 4
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do? | A. Apply an analysis of variance (ANOVA). | B. Apply a Pearson correlation coefficient. | C. Apply a Spearman correlation coefficient. | D. Apply a linear discriminant analysis. |
D. Apply a linear discriminant analysis.
Explanation:
The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data.
All shared features for local models are continuous variables.
Question # 5
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 |
B. No
Question # 6
You use the Azure Machine learning SDK v2 tor Python and notebooks to tram a model. You use Python code to create a compute target, an environment, and a taring script. You need to prepare information to submit a training job.
Which class should you use? | A. MLClient | B. command | C. BuildContext | D. EndpointConnection |
B. command
Question # 7
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 data set using under sampling or oversampling | C. Generate synthetic samples in the minority class. | D. Use accuracy as the evaluation metric of the model. | E. Normalize the training feature set. |
A. Penalize the classification B. Resample the data set using under sampling or oversampling D. Use accuracy as the evaluation metric of the model.
Explanation:
References:
https://machinelearningmastery.com/tactics -to-combat-imbalanced-classes-in-your-machine-learning-dataset/
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