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 701
When you design an AI system to assess whether loans should be approved, the factors used to make the decision should be explainable. This is an example of which Microsoft guiding principle for responsible AI?
A. transparency
B. inclusiveness
C. fairness
D. privacy and security
Answer
A. transparency
Explanation
Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Incorrect Answers:
B: Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.
C: Fairness is a core ethical principle that all humans aim to understand and apply. This principle is even more important when AI systems are being developed. Key checks and balances need to make sure that the system’s decisions don’t discriminate or run a gender, race, sexual orientation, or religion bias toward a group or individual.
D: A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn’t compromise an individual’s privacy.
Question 702
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: Providing an explanation of the outcome of a credit loan application is an example of the Microsoft transparency principle for responsible AI.
Statement 2: A triage bot that prioritizes insurance claims based on injuries is an example of the Microsoft reliability and safety principle for responsible AI.
Statement 3: An AI solution that is offered at different prices for different sales territories is an example of the Microsoft inclusiveness principle for responsible AI.
Answer
Statement 1: Providing an explanation of the outcome of a credit loan application is an example of the Microsoft transparency principle for responsible AI. Yes
Statement 2: A triage bot that prioritizes insurance claims based on injuries is an example of the Microsoft reliability and safety principle for responsible AI. No
Statement 3: An AI solution that is offered at different prices for different sales territories is an example of the Microsoft inclusiveness principle for responsible AI. No
Explanation
Box 1: Yes -Achieving transparency helps the team to understand the data and algorithms used to train the model, what transformation logic was applied to the data, the final model generated, and its associated assets. This information offers insights about how the model was created, which allows it to be reproduced in a transparent way.
Box 2: No -A data holder is obligated to protect the data in an AI system, and privacy and security are an integral part of this system. Personal needs to be secured, and it should be accessed in a way that doesn’t compromise an individual’s privacy.
Box 3: No -Inclusiveness mandates that AI should consider all human races and experiences, and inclusive design practices can help developers to understand and address potential barriers that could unintentionally exclude people. Where possible, speech-to-text, text-to-speech, and visual recognition technology should be used to empower people with hearing, visual, and other impairments.
Question 703
DRAG DROP -Match the principles of responsible AI to appropriate requirements. To answer, drag the appropriate principles from the column on the left to its requirement on the right. Each principle may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Select and Place:
Principles:
- Fairness
- Privacy and security
- Reliability and safety
- Transparency
Answer Area:
- The system must not discriminate based on gender, race.
- Personal data must be visible only to approve
- Automatically decision-making processes must be reordered so that approved users can identify why a decision was made.
Answer
- Fairness: The system must not discriminate based on gender, race.
- Privacy and security: Personal data must be visible only to approve
- Transparency: Automatically decision-making processes must be reordered so that approved users can identify why a decision was made.
Question 704
DRAG DROP
You plan to deploy an Azure Machine Learning model as a service that will be used by client applications. Which three processes should you perform in sequence before you deploy the model? To answer, move the appropriate processes from the list of processes to the answer area and arrange them in the correct order. Select and Place:
- data encryption
- model retaining
- model training
- data preparation
- model evaluation
Answer
- data preparation
- model training
- model evaluation
Question 705
You are building an AI-based app.
You need to ensure that the app uses the principles for responsible AI.
Which two principles should you follow? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Implement an Agile software development methodology
B. Implement a process of AI model validation as part of the software review process
C. Establish a risk governance committee that includes members of the legal team, members of the risk management team, and a privacy officer
D. Prevent the disclosure of the use of AI-based algorithms for automated decision making
Answer
B. Implement a process of AI model validation as part of the software review process
C. Establish a risk governance committee that includes members of the legal team, members of the risk management team, and a privacy officer
Question 706
To complete the sentence, select the appropriate option in the answer area. Hot Area:
According to Microsoft’s __________ principle of responsible AI, AI system should NOT reflect biases from the data sets that are used to train the systems.
A. accountability
B. fairness
C. inclusiveness
D. transparency
Answer
B. fairness
Question 707
Select the answer that correctly completes the sentence.
According to Microsoft’s __________ principle of responsible AI, AI system should NOT reflect biases from the data sets that are used to train the systems.
A. accountability
B. fairness
C. inclusiveness
D. transparency
Answer
B. fairness
Question 708
DRAG DROP
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. Select and Place:
Workload Types:
- Anomaly detection
- Computer vision
- Knowledge mining
- Natural language processing
Answer Area:
- An automated chatbot to answer questions about refunds and exchanges.
- Determining whether a photo contains a person.
- Determining whether a review is positive or negative.
Answer
- Knowledge mining: An automated chatbot to answer questions about refunds and exchanges.
- Computer vision: Determining whether a photo contains a person.
- Natural language processing (NLP): Determining whether a review is positive or negative.
Explanation
Box 1: Knowledge mining -You can use Azure Cognitive Search’s knowledge mining results and populate your knowledge base of your chatbot.
Box 2: Computer vision
Box 3: Natural language processing (NLP) is used for tasks such as sentiment analysis.
Question 709
DRAG DROP
Match the machine learning tasks to the appropriate scenarios. To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point. Select and Place:
Learning Types
- Feature engineering
- Feature selection
- Model deployment
- Model evaluation
- Model training
Answer Area:
- Examining the values of a confusion matrix
- Splitting a date into month, day, and year fields.
- Pricing temperature and pressure to train a weather model.
Answer
- Model evaluation: Examining the values of a confusion matrix
- Feature engineering: Splitting a date into month, day, and year fields.
- Feature selection: Pricing temperature and pressure to train a weather model.
Explanation
Box 1: Model evaluation -The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false positives, and true negatives, as well as ROC, Precision/Recall, and Lift curves.
Box 2: Feature engineering -Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature engineering. Collectively, these techniques and feature engineering are referred to as featurization. Note: Often, features are created from raw data through a process of feature engineering. For example, a time stamp in itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.
Box 3: Feature selection -In machine learning and statistics, feature selection is the process of selecting a subset of relevant, useful features to use in building an analytical model. Feature selection helps narrow the field of data to the most valuable inputs. Narrowing the field of data helps reduce noise and improve training performance.
Question 710
To complete the sentence, select the appropriate option in the answer area.
Data values that influence the prediction of a model are called __________.
A. dependent variables.
B. features.
C. identifiers.
D. labels.
Answer
B. features.