Download Microsoft Azure AI Fundamentals.AI-900.NewDumps.2021-07-28.73q.tqb
| Vendor: | Microsoft |
| Exam Code: | AI-900 |
| Exam Name: | Microsoft Azure AI Fundamentals |
| Date: | Jul 28, 2021 |
| File Size: | 4 MB |
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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?
- increased sales
- a reduced workload for the customer service agents
- improved product reliability
Correct answer: B
Question 2
For a machine learning progress, how should you split data for training and evaluation?
- Use features for training and labels for evaluation.
- Randomly split the data into rows for training and rows for evaluation.
- Use labels for training and features for evaluation.
- Randomly split the data into columns for training and columns for evaluation.
Correct answer: D
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.
Correct answer: To display the answer, ProfExam Simulator is required.
Explanation:
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 - Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance 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 -
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
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