The latest Microsoft AI-900 Azure AI Fundamentals certification actual real practice exam question and answer (Q&A) dumps are available free, which are helpful for you to pass the Microsoft AI-900 Azure AI Fundamentals exam and earn Microsoft AI-900 Azure AI Fundamentals certification.
Table of Contents
Question 51
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Statement 1: Azure bot service can be integrated with the powerful AI capabilities with Azure Cognitive Services: Yes
Statement 2: Azure bot service engages with customers in a conversational manner: Yes
Statement 3: The QnA Maker service creates knowledge base, not question and answers sets: No
Note: You can use the QnA Maker service and a knowledge base to add question-and-answer support to your bot. When you create your knowledge base, you seed it with questions and answers.
Answer
Explanation
Box 1: Yes
Azure bot service can be integrated with the powerful AI capabilities with Azure Cognitive Services.
Box 2: Yes
Azure bot service engages with customers in a conversational manner.
Box 3: No
The QnA Maker service creates knowledge base, not question and answers sets.
Note: You can use the QnA Maker service and a knowledge base to add question-and-answer support to your bot. When you create your knowledge base, you seed it with questions and answers.
Question 52
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Statement 1: Forecasting housing prices based on historical data is an example of anomaly detection: No
Statement 2: Identifying suspicious sign-ins by looking for deviations from usual patterns is an example of anomaly detection: Yes
Statement 3: Predicting whether a patient will develop diabetes based on the patient’s medical history is an example of anomaly detection: No
Answer
Explanation
Anomaly detection encompasses many important tasks in machine learning:
- Identifying transactions that are potentially fraudulent.
- Learning patterns that indicate that a network intrusion has occurred.
- Finding abnormal clusters of patients.
- Checking values entered into a system.
Question 53
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.
You are developing a model to predict events by using classification.
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.
Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic.
Answer
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
Question 54
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.
Workloads Types:
- Anomaly detection
- Computer vision
- Conversational AI
- Knowledge mining
- Natural language processing
Answer Ares:
- An automated chat to answer questions about refunds and exchange.
- Determining whether a photo contains a person.
- Determining whether a review is positive or negative.
Answer
Conversational AI: An automated chat to answer questions about refunds and exchange
Computer Vision: Determining whether a photo contains a person
Natural language processing: Determining whether a review is positive or negative
Question 55
This is the foundation of AI and and is the way we teach a computer model to make predictions and draw conclusions from data
Answer
Machine Learning
Question 56
The capability to detect errors or unusual activity in a system
Answer
Anomaly detection
Question 57
The capability of software to interpret the world visually through cameras, video, and images
Answer
Computer Vision
Question 58
The capability of a software “agent” to participate in a conversation
Answer
Conversational AI
Question 59
In Azure Machine Learning Service, this feature enables non-experts to quickly create an effective machine learning model from data
Answer
Automated Machine Learning
Question 60
In Azure Machine Learning Service, this is a graphical interface enabling no-code development of machine learning solutions
Answer
Azure Machine Learning Designer