File Info
| Exam | Microsoft Azure AI Fundamentals |
| Number | AI-900 |
| File Name | Microsoft Azure AI Fundamentals.DrunkMonk.AI-900.2021-06-28.4e.91q.vcex |
| Size | 4.34 Mb |
| Posted | June 28, 2021 |
| Downloads | 434 |
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Demo Questions
Question 1
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?
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
Question 2
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.
Question 3
You are developing a model to predict events by using classification.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
You have a confusion matrix for the model scored on test data as shown in the following exhibit.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Correct Answer: Exam simulator is required
Box 1: 11
TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative.
The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively.
Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
TP = True Positive.
The class labels in the training set can take on only two possible values, which we usually refer to as positive or negative.
The positive and negative instances that a classifier predicts correctly are called true positives (TP) and true negatives (TN), respectively.
Similarly, the incorrectly classified instances are called false positives (FP) and false negatives (FN).
Box 2: 1,033
FN = False Negative
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?
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.
Question 5
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
NOTE: Each correct selection is worth one point.
Question 6
To complete the sentence, select the appropriate option in the answer area.
Question 7
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.
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.
Question 8
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?
This is an example of which Microsoft guiding principle for responsible AI?
- A: Fairness
- B: Inclusiveness
- C: Reliability and safety
- D: Accountability
Question 9
Match the Microsoft guiding principles for responsible AI to the appropriate descriptions.
To answer, drag the appropriate principle from the column on the left to its description on the right.
Each principle may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
To answer, drag the appropriate principle from the column on the left to its description on the right.
Each principle may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Question 10
To complete the sentence, select the appropriate option in the answer area.
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