NEW QUESTION 1

Match the types of AI workloads to the appropriate scenarios.
To answer, drag the appropriate workload type from the column on the left to its scenario on the right. Each workload type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
Reference:
https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/

NEW QUESTION 2

To complete the sentence, select the appropriate option in the answer area.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/create-features

NEW QUESTION 3

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

• 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 4

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 5

You need to predict the income range of a given customer by using the following dataset.

Which two fields should you use as features? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

• A. Education Level
• B. Last Name
• C. Age
• D. Income Range
• E. First Name

Answer: AC

Explanation:
First Name, Last Name, Age and Education Level are features. Income range is a label (what you want to predict). First Name and Last Name are irrelevant in that they have no bearing on income. Age and Education level are the features you should use.

NEW QUESTION 6

To complete the sentence, select the appropriate option in the answer area.

• A. Mastered
• B. Not Mastered

Answer: A

Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

NEW QUESTION 7

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 8

What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

• A. knowledgeability
• B. decisiveness
• C. inclusiveness
• D. fairness
• E. opinionatedness
• F. reliability and safety

Answer: CDF

Explanation:
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

NEW QUESTION 9
......