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| Section | Weight | Objectives |
|---|---|---|
| Explore data and train models | 35-40% | - Run experiments and train models
|
| Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
| Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
| Prepare a model for deployment | 20-25% | - Deploy machine learning models
|
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NEW QUESTION # 142
You have machine learning models produce unfair predictions across sensitive features.
You must use a post-processing technique to apply a constraint to the models to mitigate their unfairness.
You need to select a post-processing technique and model type.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 143
You create an Azure Machine Learning managed compute resource. The compute resource is configured as follows:
- Minimum nodes: 2
- Maximum nodes: 4
You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:
- Minimum nodes: 0
- Maximum nodes: 8
You need to reconfigure the compute resource.
Which three methods can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,C,E
Explanation:
The compute autoscales down to zero nodes when it isn't used. Dedicated VMs are created to run your jobs as needed. Use the following examples to create a compute cluster:
- Python SDK
- Azure CLI
- Studio
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute- cluster?view=azureml-api-2&tabs=python#create
NEW QUESTION # 144
You are performing feature scaling by using the scikit-learn Python library for x.1 x2, and x3 features.
Original and scaled data is shown in the following image.
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
Box 1: StandardScaler
The StandardScaler assumes your data is normally distributed within each feature and will scale them such that the distribution is now centred around 0, with a standard deviation of 1.
Example:
All features are now on the same scale relative to one another.
Box 2: Min Max Scaler
Notice that the skewness of the distribution is maintained but the 3 distributions are brought into the same scale so that they overlap.
Box 3: Normalizer
References:
http://benalexkeen.com/feature-scaling-with-scikit-learn/
NEW QUESTION # 145
You are retrieving data from a large datastore by using Azure Machine Learning Studio.
You must create a subset of the data for testing purposes using a random sampling seed based on the system clock.
You add the Partition and Sample module to your experiment.
You need to select the properties for the module.
Which values should you select? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Sampling
Create a sample of data
This option supports simple random sampling or stratified random sampling. This is useful if you want to create a smaller representative sample dataset for testing.
1. Add the Partition and Sample module to your experiment in Studio, and connect the dataset.
2. Partition or sample mode: Set this to Sampling.
3. Rate of sampling. See box 2 below.
Box 2: 0
3. Rate of sampling. Random seed for sampling: Optionally, type an integer to use as a seed value.
This option is important if you want the rows to be divided the same way every time. The default value is 0, meaning that a starting seed is generated based on the system clock. This can lead to slightly different results each time you run the experiment.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample
NEW QUESTION # 146
You have an Azure Machine Learning workspace named workspace1 that is accessible from a public endpoint. The workspace contains an Azure Blob storage datastore named store1 that represents a blob container in an Azure storage account named account1. You configure workspace1 and account1 to be accessible by using private endpoints in the same virtual network.
You must be able to access the contents of store1 by using the Azure Machine Learning SDK for Python. You must be able to preview the contents of store1 by using Azure Machine Learning studio.
You need to configure store1.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-access-data
NEW QUESTION # 147
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