File Info
| Exam | Microsoft Azure AI Fundamentals |
| Number | AI-900 |
| File Name | Microsoft.AI-900.NewDumps.2021-06-28.91q.vcex |
| Size | 4 MB |
| Posted | Jun 28, 2021 |
| Download | Microsoft.AI-900.NewDumps.2021-06-28.91q.vcex |
How to open VCEX & EXAM Files?
Files with VCEX & EXAM extensions can be opened by ProfExam Simulator.
Coupon: MASTEREXAM
With discount: 20%
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: A
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
Explanation:
Reference:https://www.sqlshack.com/prediction-in-azure-machine-learning/ Reference:
https://www.sqlshack.com/prediction-in-azure-machine-learning/
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 work with this question, an Exam 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: 1,033FN = False Negative 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 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?
- Set Validation type to Auto.
- Enable Explain best model.
- Set Primary metric to accuracy.
- Set Max concurrent iterations to 0.
Correct 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-service/ 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-service/
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.
Correct answer: To work with this question, an Exam Simulator is required.
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. Reference:https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection 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.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/anomaly-detection
Question 6
To complete the sentence, select the appropriate option in the answer area.
Correct answer: To work with this question, an Exam Simulator is required.
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.
Correct answer: To work with this question, an Exam Simulator is required.
Explanation:
Box 3: Natural language processingNatural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization. Reference:https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing Box 3: Natural language processing
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection, key phrase extraction, and document categorization.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
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?
- Fairness
- Inclusiveness
- Reliability and safety
- Accountability
Correct 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 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
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.
Correct answer: To work with this question, an Exam Simulator is required.
Explanation:
Box 1: Reliability and safetyTo build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. Box 2: AccountabilityThe people who design and deploy AI systems must be accountable for how their systems operate. Organizations should draw upon industry standards to develop accountability norms. These norms can ensure that AI systems are not the final authority on any decision that impacts people's lives and that humans maintain meaningful control over otherwise highly autonomous AI systems. Box 3: Privacy and securityAs AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex. With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people. AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used.Reference:https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles Box 1: Reliability and safety
To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions.
These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Box 2: Accountability
The people who design and deploy AI systems must be accountable for how their systems operate.
Organizations should draw upon industry standards to develop accountability norms. These norms can ensure that AI systems are not the final authority on any decision that impacts people's lives and that humans maintain meaningful control over otherwise highly autonomous AI systems.
Box 3: Privacy and security
As AI becomes more prevalent, protecting privacy and securing important personal and business information is becoming more critical and complex.
With AI, privacy and data security issues require especially close attention because access to data is essential for AI systems to make accurate and informed predictions and decisions about people.
AI systems must comply with privacy laws that require transparency about the collection, use, and storage of data and mandate that consumers have appropriate controls to choose how their data is used.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
Question 10
To complete the sentence, select the appropriate option in the answer area.
Correct answer: To work with this question, an Exam Simulator is required.
Explanation:
Reliability and safety: To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions. These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation. Reference:https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles Reliability and safety: To build trust, it's critical that AI systems operate reliably, safely, and consistently under normal circumstances and in unexpected conditions.
These systems should be able to operate as they were originally designed, respond safely to unanticipated conditions, and resist harmful manipulation.
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
