Microsoft AZ900 Exam Dumps|QAs Page -13

Microsoft AZ900 Exam Dumps post contains real and latest questions for Microsoft Azure Fundamentals.

Microsoft AZ900 Exam Dumps

Microsoft AZ900 Exam Dumps – QAs 61-65

Q61. 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 using Azure Machine Learning Studio to perform feature engineering on a dataset.
You need to normalize values to produce a feature column grouped into bins.
Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.
Does the solution meet the goal?

  1. Yes
  2. No
Correct Answer

1. Yes

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins

Q62. 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 a data scientist using Azure Machine Learning Studio.
You need to normalize values to produce an output column into bins to predict a target column.
Solution: Apply a Quantiles normalization with a QuantileIndex normalization.
Does the solution meet the goal?

  1. Yes
  2. No
Correct Answer

2. No

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins

Q63. You are analyzing a dataset by using Azure Machine Learning Studio.
You need to generate a statistical summary that contains the p-value and the unique count for each feature column.
Which two modules can you use? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

  1. Computer Linear Correlation
  2. Export Count Table
  3. Execute Python Script
  4. Convert to Indicator Values
  5. Summarize Data
Correct Answer

2. Export Count Table
5. Summarize Data

Q64. HOTSPOT –
You are creating a machine learning model in Python. The provided dataset contains several numerical columns and one text column. The text column represents a product’s category. The product category will always be one of the following:
✑ Bikes
✑ Cars
✑ Vans
✑ Boats
You are building a regression model using the scikit-learn Python package.
You need to transform the text data to be compatible with the scikit-learn Python package.
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.
Hot Area:

az900_question 64
Correct Answer
az900_answer 64

Q65. 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: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method.
Does the solution meet the goal?

  1. Yes
  2. No
Correct Answer

1. Yes

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

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