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Microsoft AI-900 Online Exam Practice Questions
QUESTION 1
DRAG DROP
You plan to apply Text Analytics API features to a technical support ticketing system.
Match the Text Analytics API features to the appropriate natural language processing scenarios.
To answer, drag the appropriate feature from the column on the left to its scenario on the right. Each feature may be
used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
Box1: Sentiment analysis
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative, or neutral.
Box 2: Broad entity extraction
Broad entity extraction: Identify important concepts in the text, including key
Key phrase extraction/ Broad entity extraction: Identify important concepts in the text, including key phrases and named
entities such as people, places, and organizations.
Box 3: Entity Recognition
Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations, date/time,
quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more information
on the
web.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics
QUESTION 2
DRAG DROP
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 principal may
be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
Correct Answer:
Box 1: Reliability and safety To build trust, it\\’s critical that AI systems operate reliably, safely, and consistently under
normal circumstances and 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: Fairness Fairness: AI systems should treat everyone fairly and avoid affecting similarly situated groups of people
in different ways. For example, when AI systems provide guidance on medical treatment, loan applications, or
employment, they should make the same recommendations to everyone with similar symptoms, financial
circumstances, or professional qualifications.
We believe that mitigating bias starts with people understanding the implications and limitations of AI predictions and
recommendations. Ultimately, people should supplement AI decisions with sound human judgment and be held
accountable for consequential decisions that affect others.
Box 3: Privacy and security As AI becomes more prevalent, protecting privacy and securing important personal and
business information are 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 3
HOTSPOT
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:
Correct Answer:
The Text Analytics API is a cloud-based service that provides advanced natural language processing over raw text, and
includes four main functions: sentiment analysis, key phrase extraction, named entity recognition, and language
detection.
Box 1: Yes You can detect which language the input text is written in and report a single language code for every
document submitted on the request in a wide range of languages, variants, dialects, and some regional/cultural
languages. The language code is paired with a score indicating the strength of the score.
Box 2: No
Box 3: Yes Named Entity Recognition: Identify and categorize entities in your text as people, places, organizations,
date/time, quantities, percentages, currencies, and more. Well-known entities are also recognized and linked to more
information on the web.
Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
QUESTION 4
Which type of machine learning should you use to predict the number of gift cards that will be sold next month?
A. classification
B. regression
C. clustering
Correct Answer: C
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/studio-module-reference/machine-learninginitialize-model-clustering
QUESTION 5
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
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-configure-cross-validation-data-splits2
QUESTION 6
HOTSPOT
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:
Correct Answer:
Box 1: Yes
In machine learning, if you have labeled data, that means your data is marked up or annotated, to show the target,
which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription,
or processing.
Box 2: No
Box 3: No
Accuracy is simply the proportion of correctly classified instances. It is usually the first metric you look at when
evaluating a classifier. However, when the test data is unbalanced (where most of the instances belong to one of the
classes), or
you are more interested in the performance on either one of the classes, accuracy doesn\\’t really capture the
effectiveness of a classifier.
Reference:
https://www.cloudfactory.com/data-labeling-guide
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
QUESTION 7
Your website has a chatbot to assist customers.
You need to detect when a customer is upset based on what the customer types in the chatbot.
Which type of AI workload should you use?
A. anomaly detection
B. semantic segmentation
C. regression
D. natural language processing
Correct Answer: D
Natural language processing (NLP) is used for tasks such as sentiment analysis, topic detection, language detection,
keyphrase extraction, and document categorization.
Sentiment Analysis is the process of determining whether a piece of writing is positive, negative, or neutral.
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/natural-language-processing
QUESTION 8
A company employs a team of customer service agents to provide telephone and email support to customers.
The company develops a web chatbot to provide automated answers to common customer queries.
Which business benefit should the company expect as a result of creating the web chatbot solution?
A. increased sales
B. a reduced workload for the customer service agents
C. improved product reliability
Correct Answer: B
QUESTION 9
HOTSPOT
To complete the sentence, select the appropriate option in the answer area.
Hot Area:
In machine learning, if you have labeled data, that means your data is marked up or annotated, to show the target,
which is the answer you want your machine learning model to predict.
In general, data labeling can refer to tasks that include data tagging, annotation, classification, moderation, transcription,
or processing.
Incorrect Answers:
Not features: 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.
Reference:
https://www.cloudfactory.com/data-labeling-guide
QUESTION 10
HOTSPOT
You have the following dataset.
You plan to use the dataset to train a model that will predict the house price categories of houses.
What are Household Income and House Price Category? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
Hot Area:
Reference: https://docs.microsoft.com/en-us/azure/machine-learning/studio/interpret-model-results
QUESTION 11
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?
A ROC curve that approaches the top left corner with a 100% true positive rate and a 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.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#classification
QUESTION 12
DRAG DROP
Match the types of machine learning to the appropriate scenarios.
To answer, drag the appropriate machine learning type from the column on the left to its scenario on the right. Each
machine learning type may be used once, more than once, or not at all.
NOTE: Each correct selection is worth one point.
Select and Place:
Correct Answer:
Box 1: Regression
In the most basic sense, regression refers to the 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.
Box 2: Classification
Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of
data.
Box 3: Clustering
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/studio-module-reference/linear-regression
QUESTION 13
Which two scenarios are examples of a conversational AI workload? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. a telephone answering service that has a pre-recorded message
B. a chatbot that provides users with the ability to find answers on a website by themselves
C. telephone voice menus to reduce the load on human resources
D. a service that creates frequently asked questions (FAQ) documents by crawling public websites
Correct Answer: BC
B: A bot is an automated software program designed to perform a particular task. Think of it as a robot without a body.
C: Automated customer interaction is essential to a business of any size. In fact, 61% of consumers prefer to
communicate via speech, and most of them prefer self-service. Because customer satisfaction is a priority for all
businesses, self-service is a critical facet of any customer-facing communications strategy.
Incorrect Answers:
D: Early bots were comparatively simple, handling repetitive and voluminous tasks with relatively straightforward
algorithmic logic. An example would be web crawlers used by search engines to automatically explore and catalog web
content.
Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/ai-overview
https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/interactive-voice-response-bot
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