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| Section | Weight | Objectives |
|---|---|---|
| Prepare a model for deployment | 20-25% | - Manage deployment assets
|
| Design and prepare a machine learning solution | 20-25% | - Prepare development environments
|
| Deploy and retrain models | 10-15% | - Implement retraining pipelines
|
| Explore data and train models | 35-40% | - Run experiments and train models
|
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질문 # 343
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.
정답：
설명：
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/
질문 # 344
A biomedical research company plans to enroll people in an experimental medical treatment trial.
You create and train a binary classification model to support selection and admission of patients to the trial.
The model includes the following features: Age, Gender, and Ethnicity.
The model returns different performance metrics for people from different ethnic groups.
You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.
Which technique and constraint should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Box 1: Grid Search
Fairlearn open-source package provides postprocessing and reduction unfairness mitigation algorithms:
ExponentiatedGradient, GridSearch, and ThresholdOptimizer.
Note: The Fairlearn open-source package provides postprocessing and reduction unfairness mitigation algorithms types:
Reduction: These algorithms take a standard black-box machine learning estimator (e.g., a LightGBM model) and generate a set of retrained models using a sequence of re-weighted training datasets.
Post-processing: These algorithms take an existing classifier and the sensitive feature as input.
Box 2: Demographic parity
The Fairlearn open-source package supports the following types of parity constraints: Demographic parity, Equalized odds, Equal opportunity, and Bounded group loss.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml
질문 # 345
You need to build a feature extraction strategy for the local models.
How should you complete the code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation
질문 # 346
You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.
You have the following data available for model building:
* Video recordings of sporting events
* Transcripts of radio commentary about events
* Logs from related social media feeds captured during sporting events
You need to select an environment for creating the model.
Which environment should you use?
정답：C
설명：
Explanation
Azure Cognitive Services expand on Microsoft's evolving portfolio of machine learning APIs and enable developers to easily add cognitive features - such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding - into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure Cognitive Services can be categorized into five main pillars - Vision, Speech, Language, Search, and Knowledge.
References:
https://docs.microsoft.com/en-us/azure/cognitive-services/welcome
질문 # 347
You are working on a classification task. You have a dataset indicating whether a student would like to play soccer and associated attributes. The dataset includes the following columns:
You need to classify variables by type.
Which variable should you add to each category? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation
References:
https://www.edureka.co/blog/classification-algorithms/
질문 # 348
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