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NEW QUESTION 1

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
AI-900 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-designer-python https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

NEW QUESTION 2

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
AI-900 dumps exhibit

  • A. Mastered
  • B. Not Mastered

Answer: A

Explanation:
Box 1: Yes
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
Box 2: Yes
With the designer you can connect the modules to create a pipeline draft.
As you edit a pipeline in the designer, your progress is saved as a pipeline draft. Box 3: No
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

NEW QUESTION 3

You are designing an AI system that empowers everyone, including people who have hearing, visual, and other impairments.
This is an example of which Microsoft guiding principle for responsible AI?

  • A. fairness
  • B. inclusiveness
  • C. reliability and safety
  • D. accountability

Answer: B

Explanation:
Inclusiveness: At Microsoft, we firmly believe everyone should benefit from intelligent technology, meaning it must incorporate and address a broad range of human needs and experiences. For the 1 billion people with disabilities around the world, AI technologies can be a game-changer.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

NEW QUESTION 4

A company employs a team of customer service agents to provide telephone and email support to customers. The company develops a webchat bot to provide automated answers to common customer queries.
Which business benefit should the company expect as a result of creating the webchat bot solution?

  • A. increased sales
  • B. a reduced workload for the customer service agents
  • C. improved product reliability

Answer: B

NEW QUESTION 5

For a machine learning progress, how should you split data for training and evaluation?

  • A. Use features for training and labels for evaluation.
  • B. Randomly split the data into rows for training and rows for evaluation.
  • C. Use labels for training and features for evaluation.
  • D. Randomly split the data into columns for training and columns for evaluation.

Answer: D

Explanation:
In Azure Machine Learning, the percentage split is the available technique to split the data. In this technique, random data of a given percentage will be split to train and test data.
Reference:
https://www.sqlshack.com/prediction-in-azure-machine-learning/

NEW QUESTION 6

Which metric can you use to evaluate a classification model?

  • A. true positive rate
  • B. mean absolute error (MAE)
  • C. coefficient of determination (R2)
  • D. root mean squared error (RMSE)

Answer: A

Explanation:
What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification

NEW QUESTION 7

You build a machine learning model by using the automated machine learning user interface (UI). You need to ensure that the model meets the Microsoft transparency principle for responsible AI. What should you do?

  • A. Set Validation type to Auto.
  • B. Enable Explain best model.
  • C. Set Primary metric to accuracy.
  • D. Set Max concurrent iterations to 0.

Answer: B

Explanation:
Model Explain Ability.
Most businesses run on trust and being able to open the ML “black box” helps build transparency and trust. In heavily regulated industries like healthcare and banking, it is critical to comply with regulations and best practices. One key aspect of this is understanding the relationship between input variables (features) and model output. Knowing both the magnitude and direction of the impact each feature (feature importance) has on the predicted value helps better understand and explain the model. With model explain ability, we enable you to understand feature importance as part of automated ML runs.
Reference:
https://azure.microsoft.com/en-us/blog/new-automated-machine-learning-capabilities-in-azure-machine-learning

NEW QUESTION 8

Which type of machine learning should you use to predict the number of gift cards that will be sold next month?

  • A. classification
  • B. regression
  • C. clustering

Answer: C

Explanation:
Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation.
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-m

NEW QUESTION 9
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