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Share Microsoft AI-900 exam questions and answers online practice test

QUESTION 1 #

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.

https://azure.microsoft.com/en-gb/services/cognitive-services/speech-services/

QUESTION 2 #

DRAG-DROP
Match the machine learning tasks to the appropriate scenarios.
To answer, drag the appropriate task from the column on the left to its scenario on the right. Each task may be used
once, more than once, or not at all.
NOTE: Each correct selection is worth one point.

Box 1: Model evaluation
The Model evaluation module outputs a confusion matrix showing the number of true positives, false negatives, false
positives, and true negatives, as well as ROC, Precision/Recall and Lift curves.

Box 2: Feature engineering
Feature engineering is the process of using domain knowledge of the data to create features that help ML algorithms
learn better. In Azure Machine Learning, scaling and normalization techniques are applied to facilitate feature
engineering.

Collectively, these techniques and feature engineering are referred to as featurization.
Note: Often, features are created from raw data through a process of feature engineering. For example, a timestamp in
itself might not be useful for modeling until the information is transformed into units of days, months, or categories that are relevant to the problem, such as holiday versus working day.

Box 3: Feature selection
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://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance
https://docs.microsoft.com/en-us/azure/machine-learning/concept-automated-ml

QUESTION 3 #

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

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

QUESTION 4 #

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

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. At Microsoft,
we are continuing to research privacy and security breakthroughs (see next unit) and invest in robust compliance
processes to ensure that data collected and used by our AI systems is handled responsibly.

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

QUESTION 5 #

You have the process shown in the following exhibit.

Which type of AI solution is shown in the diagram?

A. a sentiment analysis solution
B. a chatbot
C. a machine learning model
D. a computer vision application
Correct Answer: B

QUESTION 6 #

What are two tasks that can be performed by using the Computer Vision service? Each correct answer presents a
complete solution. NOTE: Each correct selection is worth one point.

A. Train a custom image classification model.
B. Detect faces in an image.
C. Recognize handwritten text.
D. Translate the text in an image between languages.
Correct Answer: BC

B: Azure\\’s Computer Vision service provides developers with access to advanced algorithms that process images and
return information based on the visual features you\\’re interested in. For example, Computer Vision can determine
whether an image contains adult content, find specific brands or objects, or find human faces.

C: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to
extract printed and handwritten text from images and documents.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/home

QUESTION 7 #

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

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
the model can then be used to make predictions.

Incorrect Answers:
1. Classification is a machine learning method that uses data to determine the category, type, or class of an item or row of data.

2. 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 sentiments.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/linear-regression https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/machine-learning-initialize-modelclustering

QUESTION 8 #

DRAG-DROP
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.

Reference: https://docs.microsoft.com/en-us/learn/paths/get-started-with-artificial-intelligence-on-azure/

QUESTION 9 #

What are two tasks that can be performed by using computer vision? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Predict stock prices.
B. Detect brands in an image.
C. Detect the color scheme in an image
D. Translate text between languages.
E. Extract key phrases.
Correct Answer: BE

B: Azure\\’s Computer Vision service gives you access to advanced algorithms that process images and return
information based on the visual features you\\’re interested in. For example, Computer Vision can determine whether an image contains adult content, find specific brands or objects, or find human faces.

E: Computer Vision includes Optical Character Recognition (OCR) capabilities. You can use the new Read API to
extract printed and handwritten text from images and documents. It uses the latest models and works with text on a
variety of surfaces and backgrounds. These include receipts, posters, business cards, letters, and whiteboards. The two
OCR APIs support extracting printed text in several languages.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/overview

QUESTION 10 #

HOTSPOT
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.

Hot Area:

Correct Answer:

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

Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/studio/evaluate-model-performance

QUESTION 11 #

In which two scenarios can you use the Form Recognizer service? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.

A. Extract the invoice number from an invoice.
B. Translate a form from French to English.
C. Find an image of the product in a catalog.
D. Identity the retailer from a receipt.
Correct Answer: AD
Reference: https://azure.microsoft.com/en-gb/services/cognitive-services/form-recognizer/#features

QUESTION 12 #

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:

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/custom-vision-service/get-started-build-detector

QUESTION 13 #

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

Hot Area:

Correct Answer:

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-designer

QUESTION 14 #

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 smart device in the home that responds to questions such as “What will the weather be like today?”
B. a website that uses a knowledge base to interactively respond to users\’ questions
C. assembly line machinery that autonomously inserts headlamps into cars
D. monitoring the temperature of machinery to turn on a fan when the temperature reaches a specific threshold
Correct Answer: AB

QUESTION 15 #

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 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

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