AI-900 Microsoft Azure AI Fundamentals exams demo

Exam A
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?
A. increased sales
B. a reduced workload for the customer service agents
C. improved product reliability
Correct Answer: B
Section: Describe Artificial Intelligence workloads and considerations
Explanation
Explanation/Reference:
QUESTION 2
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.
Correct Answer: D
Section: Describe Artificial Intelligence workloads and considerations
Explanation
Explanation/Reference:
Explanation:
In Azure Machine Learning, the percentage split is the available technique to split the data. In this technique,
random data of a given percentage will be split to train and test data.
Reference:
https://www.sqlshack.com/prediction-in-azure-machine-learning/
.

.

.

.
You build a machine learning model by using the automated machine learning user interface (UI).
96CE4376707A97CE80D4B1916F054522
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.
Correct Answer: B
Section: Describe Artificial Intelligence workloads and considerations
Explanation
Explanation/Reference:
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-machinelearning-
service/
.

.

.

.

.
QUESTION 11
You are building an AI system.
Which task should you include to ensure that the service meets the Microsoft transparency principle for
responsible AI?
A. Ensure that all visuals have an associated text that can be read by a screen reader.
B. Enable autoscaling to ensure that a service scales based on demand.
C. Provide documentation to help developers debug code.
D. Ensure that a training dataset is representative of the population.
Correct Answer: C
.

.

QUESTION 13
Your company is exploring the use of voice recognition technologies in its smart home devices. The company
wants to identify any barriers that might unintentionally leave out specific user groups.
This an example of which Microsoft guiding principle for responsible AI?
A. accountability
96CE4376707A97CE80D4B1916F054522
B. fairness
C. inclusiveness
D. privacy and security
Correct Answer: C
Section: Describe Artificial Intelligence workloads and considerations
Explanation
Explanation/Reference:
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
QUESTION 14
What are three Microsoft guiding principles for responsible AI? Each correct answer presents a complete
solution.
NOTE: Each correct selection is worth one point.
A. knowledgeability
B. decisiveness
C. inclusiveness
D. fairness
E. opinionatedness
F. reliability and safety
Correct Answer: CDF
Section: Describe Artificial Intelligence workloads and considerations
Explanation
Explanation/Reference:
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles
.

.

.

.
QUESTION 19
Which two components can you drag onto a canvas in Azure Machine Learning designer? Each correct answer
presents a complete solution.
NOTE: Each correct selection is worth one point.
A. dataset
B. compute
C. pipeline
D. module
Correct Answer: AD
Section: Describe fundamental principles of machine learning on Azure
Explanation
Explanation/Reference:
Explanation:
You can drag-and-drop datasets and modules onto the canvas.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer
QUESTION 20
You need to create a training dataset and validation dataset from an existing dataset.
Which module in the Azure Machine Learning designer should you use?
A. Select Columns in Dataset
B. Add Rows
C. Split Data
D. Join Data
Correct Answer: C
Section: Describe fundamental principles of machine learning on Azure
Explanation
Explanation/Reference:
Explanation:
A common way of evaluating a model is to divide the data into a training and test set by using Split Data, and
then validate the model on the training data.
Use the Split Data module to divide a dataset into two distinct sets.
The studio currently supports training/validation data splits
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits2


Buy full version for more questions