Download Microsoft Azure AI Fundamentals.DrunkMonk.AI-900.2021-04-25.4e.65q.vcex

Download Exam

File Info

Exam Microsoft Azure AI Fundamentals
Number AI-900
File Name Microsoft Azure AI Fundamentals.DrunkMonk.AI-900.2021-04-25.4e.65q.vcex
Size 3.09 Mb
Posted April 25, 2021
Downloads 95
Download Microsoft Azure AI Fundamentals.DrunkMonk.AI-900.2021-04-25.4e.65q.vcex

How to open VCEX & EXAM Files?

Files with VCEX & EXAM extensions can be opened by ProfExam Simulator.

Purchase

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?

  • 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: B

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: Exam simulator is required

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?

  • 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. 
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: Exam simulator is required

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

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: Exam simulator is required

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 7

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. 
Reference:
https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles




Question 8

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: Exam simulator is required

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 9

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: CD

C: Switch to the browser tab containing the Consume page for the predict-auto-price service, and copy the Primary Key for your service. The switch back to the tab containing the notebook and paste the key into the code, replacing YOUR_KEY.
D: Switch to the browser tab containing the Consume page for the predict-auto-price service, and copy the REST endpoint for your service. The switch back to the tab containing the notebook and paste the key into the code, replacing YOUR_ENDPOINT
Reference:
https://docs.microsoft.com/en-in/learn/modules/create-regression-model-azure-machine-learning-designer/deploy-service




Question 10

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

Correct Answer: Exam simulator is required

In the most basic sense, regression refers to prediction of a numeric target. 
Linear regression attempts to establish a linear relationship between one or more independent variables and a numeric outcome, or dependent variable. 
You use this module to define a linear regression method, and then train a model using a labeled dataset. The trained model can then be used to make predictions. Incorrect Answers:
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data. Clustering, in machine learning, is a method of grouping data points into similar clusters. It is also called segmentation. 
Over the years, many clustering algorithms have been developed. Almost all clustering algorithms use the features of individual items to find similar items. For example, you might apply clustering to find similar people by demographics. You might use clustering with text analysis to group sentences with similar topics or sentiment. 
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression










CONNECT US

Facebook

Twitter

PROFEXAM WITH A 20% DISCOUNT

You can buy ProfExam with a 20% discount!



HOW TO OPEN VCEX AND EXAM FILES

Use ProfExam Simulator to open VCEX and EXAM files