Download Microsoft Azure AI Fundamentals.VCEplus.AI-900.2020-09-08.1e.36q.vcex

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Exam Microsoft Azure AI Fundamentals
Number AI-900
File Name Microsoft Azure AI Fundamentals.VCEplus.AI-900.2020-09-08.1e.36q.vcex
Size 1.06 Mb
Posted September 08, 2020
Downloads 237
Download Microsoft Azure AI Fundamentals.VCEplus.AI-900.2020-09-08.1e.36q.vcex

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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?

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

Correct Answer: B

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

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.

Question 3

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.

Correct Answer: B

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.

Question 4

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

Correct Answer: B

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.

Question 5

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

  • A:

Correct Answer: Exam simulator is required

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.

Question 6

Which service should you use to extract text, key/value pairs, and table data automatically from scanned documents?

  • A: Form Recognizer
  • B: Text Analytics
  • C: Ink Recognizer
  • D: Custom Vision

Correct Answer: A

Accelerate your business processes by automating information extraction. Form Recognizer applies advanced machine learning to accurately extract text, key/value pairs, and tables from documents. With just a few samples, Form Recognizer tailors its understanding to your documents, both on-premises and in the cloud. Turn forms into usable data at a fraction of the time and cost, so you can focus more time acting on the information rather than compiling it.

Question 7

You use Azure Machine Learning designer to publish an inference pipeline.
Which two parameters should you use to consume the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

  • A: the model name
  • B: the training endpoint
  • C: the authentication key
  • D: the REST endpoint

Correct Answer: AD

A: The trained model is stored as a Dataset module in the module palette. You can find it under My Datasets.
Azure Machine Learning designer lets you visually connect datasets and modules on an interactive canvas to create machine learning models.
D: You can consume a published pipeline in the Published pipelines page. Select a published pipeline and find the REST endpoint of it.

Question 8

Which metric can you use to evaluate a classification model?

  • A: true positive rate
  • B: mean absolute error (MAE)
  • C: coefficient of determination (R2)
  • D: root mean squared error (RMSE)

Correct Answer: A

What does a good model look like?
An ROC curve that approaches the top left corner with 100% true positive rate and 0% false positive rate will be the best model. A random model would display as a flat line from the bottom left to the top right corner. Worse than random would dip below the y=x line.

Question 9

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

You can drag-and-drop datasets and modules onto the canvas.

Question 10

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

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





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