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| Section | Objectives |
|---|---|
| Topic 1: Explore and analyze data | - Ingest and prepare data for modeling - Perform exploratory data analysis |
| Topic 2: Train machine learning models | - Tune hyperparameters and evaluate models - Train models using Azure Machine Learning |
| Topic 3: Design and prepare a machine learning solution | - Manage compute and data assets - Select appropriate Azure services for machine learning workloads - Plan and configure Azure Machine Learning workspace |
| Topic 4: Deploy and consume models | - Deploy models to endpoints - Monitor deployed models and endpoints |
| Topic 5: Optimize and manage models | - Track experiments and manage model lifecycle - Improve model performance |
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NEW QUESTION # 247
You create a training pipeline using the Azure Machine Learning designer. You upload a CSV file that contains the data from which you want to train your model.
You need to use the designer to create a pipeline that includes steps to perform the following tasks:
* Select the training features using the pandas filter method.
* Train a model based on the naive_bayes.GaussianNB algorithm.
* Return only the Scored Labels column by using the query SELECT [Scored Labels] FROM t1; Which modules should you use? To answer, drag the appropriate modules to the appropriate locations. Each module name may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation
NEW QUESTION # 248
You are evaluating a completed binary classification machine learning model.
You need to use the precision as the valuation metric.
Which visualization should you use?
Answer: C
Explanation:
Reference:
https://machinelearningknowledge.ai/confusion-matrix-and-performance-metrics-machine-learning/
NEW QUESTION # 249
You train a machine learning model by using Aunt Machine Learning.
You use the following training script m Python to log an accuracy value.
You must use a Python script to define a sweep job.
You need to provide the primary metric and goal you want hyper parameter tuning to optimize.
How should you complete the Python script? To answer select the appropriate options in the answer area NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 250
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?
Answer: C
Explanation:
Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper.
In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript." Scenario:
Local penalty detection models must be written by using BrainScript.
Reference:
https://docs.microsoft.com/en-us/cognitive-toolkit/post-batch-normalization-statistics
NEW QUESTION # 251
You have a dataset that includes home sales data for a city. The dataset includes the following columns.
Each row in the dataset corresponds to an individual home sales transaction.
You need to use automated machine learning to generate the best model for predicting the sales price based on the features of the house.
Which values should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Box 1: Regression
Regression is a supervised machine learning technique used to predict numeric values.
Box 2: Price
Reference:
https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer
NEW QUESTION # 252
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