Exam: AI-900: Microsoft Azure AI Fundamentals

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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?
A. increased sales
B. a reduced workload for the customer service agents
C. improved product reliability

Answer:B
✅ Explanation:
-A webchat bot is typically implemented to handle common and repetitive customer queries, such as FAQs, order tracking, or basic troubleshooting. By automating these tasks, the bot can answer many customer questions without human intervention.
-As a result, customer service agents will have fewer repetitive inquiries to manage, allowing them to focus on more complex or high-value interactions.
-Why not the others?
A. Increased sales: While a chatbot might contribute indirectly to sales (by improving customer experience), its primary purpose in this case is support, not sales.
C. Improved product reliability: A chatbot doesn't directly affect the reliability or quality of the product itself.
-So, the main benefit here is:
✅ Reduced workload for the customer service agents

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.

Answer:B
✅ Explanation:
-In machine learning, you want to train your model on one part of the data and then evaluate its performance on unseen data to check how well it generalizes.
-You have a dataset with rows as data samples (examples/instances) and columns as features (independent variables) and labels (dependent variable).
-The correct way to split is to randomly select some rows for training and other rows for evaluation (testing/validation).
-This way, the model learns from a portion of the data and is tested on separate data to measure its performance.
-Why not the others?
A. Use features for training and labels for evaluation: Incorrect. You always use both features and labels in training. Labels are needed to supervise the learning.
C. Use labels for training and features for evaluation: Incorrect. You cannot train on labels alone. Features are your input; labels are the output you predict.
D. Randomly split the data into columns for training and columns for evaluation: Incorrect. Splitting columns would mean you train without certain features, which is not standard practice for evaluating model performance.

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 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?
A. Set Validation type to Auto.
B. Enable Explain best model.
C. Set Primary metric to accuracy.
D. Set Max concurrent iterations to 0

Answer:B
✅ Explanation:
-The Microsoft transparency principle for responsible AI emphasizes that AI systems should be understandable and interpretable by humans. Users should be able to know how the model makes decisions.
-The "Enable Explain best model" option in Azure Automated Machine Learning (AutoML) allows you to generate explanations for the best model.
-This feature shows feature importance and helps users understand why the model made certain predictions, which directly supports the transparency principle.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Hot Area:

To complete the sentence, select the appropriate option in the answer area.
Hot Area:

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:

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?
A. fairness
B. inclusiveness
C. reliability and safety
D. accountability

Answer:B
✅ Explanation:
-The inclusiveness principle in Microsoft's Responsible AI guidelines focuses on empowering everyone, including people with disabilities or impairments. It ensures that AI systems are designed to be accessible and usable by all people, regardless of their abilities.
-Why not the others?
A. Fairness: Fairness focuses on preventing bias and ensuring equitable treatment across different demographics (e.g., race, gender), but it's not specifically about accessibility for people with impairments.
C. Reliability and safety: This principle is about ensuring the AI system functions correctly and safely, not about inclusiveness.
D. Accountability: Accountability is about having human oversight and responsibility for AI systems, not about accessibility or inclusion.

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.
Select and Place:

To complete the sentence, select the appropriate option in the answer area.
Hot Area:

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