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WorksheetsAI900
Total questions: 143
Worksheet time: 5hrs 46mins
Name
Class
Date
1.
.
a)
increased sales
b)
a reduced workload for the customer service agents
c)
improved product reliability
2.
.
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.
3.
There are [answer choice] correctly predicted positives.
a)
5
b)
11
c)
1033
d)
13951
4.
There are [answer choice] false negatives.
a)
5
b)
11
c)
1033
d)
13951
5.
.
a)
Set Validation type to Auto
b)
Enable Explain best model
c)
Set Primary metric to accuracy
d)
Set Max concurrent iterations to 0
6.
Statement 1
a)
Yes
b)
No
7.
Statement 2
a)
Yes
b)
No
8.
Statement 3
a)
Yes
b)
No
9.
.
a)
inclusiveness
b)
privacy and security
c)
reliability and safety
d)
transparency
10.
An automated chat to answer questions about refunds and exchange
a)
Anomaly detection
b)
Computer vision
c)
Conversational AI
d)
Knowledge mining
e)
Natural language processing
11.
Determining whether a photo contains a person
a)
Anomaly detection
b)
Computer vision
c)
Conversational AI
d)
Knowledge mining
e)
Natural language processing
12.
Determining whether a review is positive or negative
a)
Anomaly detection
b)
Computer vision
c)
Conversational AI
d)
Knowledge mining
e)
Natural language processing
13.
.
a)
fairness
b)
inclusiveness
c)
reliability and safety
d)
accountability
14.
Ensure that AI systems operate as they were originally designed, respond to unanticipated conditions, and resist harmful manipulation.
a)
Accountability
b)
Fairness
c)
Inclusiveness
d)
Privacy and security
e)
Reliability and safety
15.
Implementing processes to ensure that decisions made by AI systems can be overridden by humans.
a)
Accountability
b)
Fairness
c)
Inclusiveness
d)
Privacy and security
e)
Reliability and safety
16.
Provide consumers with information and controls over the collection, use, and storage of their data.
a)
Accountability
b)
Fairness
c)
Inclusiveness
d)
Privacy and security
e)
Reliability and safety
17.
.
a)
inclusiveness
b)
accountability
c)
reliability and safety
d)
fairness
18.
.
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.
19.
Identify handwritten letters.
a)
Anomaly detection
b)
Computer vision
c)
Machine Learning (Regression)
d)
Natural language processing
20.
Predict the sentiment of a social media post.
a)
Anomaly detection
b)
Computer vision
c)
Machine Learning (Regression)
d)
Natural language processing
21.
Identify a fraudulent credit card payment.
a)
Anomaly detection
b)
Computer vision
c)
Machine Learning (Regression)
d)
Natural language processing
22.
Predict next month's toy sales.
a)
Anomaly detection
b)
Computer vision
c)
Machine Learning (Regression)
d)
Natural language processing
23.
.
a)
accountability
b)
fairness
c)
inclusiveness
d)
privacy and security
24.
.
a)
knowledgeability
b)
decisiveness
c)
inclusiveness
d)
fairness
e)
reliability and safety
25.
.
a)
image classification
b)
object detection
c)
optical character recognizer (OCR)
d)
semantic segmentation
26.
.
a)
Feature engineering
b)
Feature selection
c)
Model evaluation
d)
Model training
27.
.
a)
the Verify operation in the Face service
b)
the Detect operation in the Face service
c)
the Describe Image operation in the Computer Vision service
d)
the Analyze Image operation in the Computer Vision service
28.
.
a)
true positive rate
b)
mean absolute error (MAE)
c)
coefficient of determination (R2)
d)
root mean squared error (RMSE)
29.
.
a)
dataset
b)
compute
c)
pipeline
d)
module
30.
.
a)
Select Columns in Dataset
b)
Add Rows
c)
Split Data
d)
Join Data
31.
[Learning Type] Predict how many minutes late a flight will arrivebasen on the amount of snowfall at an airpoint.
a)
Classification
b)
Clustering
c)
Regression
32.
[Learning Type] Segment customers into different groups to support a marketing department.
a)
Classification
b)
Clustering
c)
Regression
33.
[Learning Type] Predict whether a student will complete a university course.
a)
Classification
b)
Clustering
c)
Regression
34.
[Task] Examining the values of a confusion matrix
a)
Feature engineering
b)
Feature selection
c)
Model deployment
d)
Model evaluation
e)
Model training
35.
[Task] Splitting a date into month, day, and year fields
a)
Feature engineering
b)
Feature selection
c)
Model deployment
d)
Model evaluation
e)
Model training
36.
[Task] Picking temperature and pressure to train a weather model
a)
Feature engineering
b)
Feature selection
c)
Model deployment
d)
Model evaluation
e)
Model training
37.
.
a)
dependant variables
b)
features
c)
identifiers
d)
labels
38.
.
a)
Classification
b)
Regression
c)
Clustering
39.
.
a)
the number of taxi journeys in the dataset
b)
the trip distance of individual taxi journeys
c)
the fare of individual taxi journeys
d)
the trip ID of individual taxi journeys
40.
.
a)
Classification
b)
Regression
c)
Clustering
41.
Statement 1
a)
Yes
b)
No
42.
Statement 2
a)
Yes
b)
No
43.
Statement 3
a)
Yes
b)
No
44.
Statement 4
a)
Yes
b)
No
45.
.
a)
Classification
b)
Regression
c)
Clustering
46.
Statement 1
a)
Yes
b)
No
47.
Statement 2
a)
Yes
b)
No
48.
Statement 3
a)
Yes
b)
No
49.
.
a)
Form Recognizer
b)
Text Analytics
c)
Ink Recognizer
d)
Custom Vision
50.
.
a)
Custom Vision
b)
Form Recognizer
c)
Ink Recognizer
d)
Text Analytics
51.
.
a)
the model name
b)
the training endpoint
c)
the authentication key
d)
the REST endpoint
52.
.
a)
a local web service.
b)
Azure Container Instances
c)
Azure Kubernetes Service (AKS).
d)
Azure Machine Learning compute.
53.
.
a)
classification
b)
clustering
c)
regression
54.
Statement 1
a)
Yes
b)
No
55.
Statement 2
a)
Yes
b)
No
56.
Statement 3
a)
Yes
b)
No
57.
Household Income:
a)
A feature
b)
A label
58.
House Price Category:
a)
A feature
b)
A label
59.
.
a)
adding and connecting modules on a visual canvas.
b)
automatically performing common data preparation tasks.
c)
automatically selecting an algorithm to build the most accurate model.
d)
using a code-first notebook experience
60.
Statement 1
a)
Yes
b)
No
61.
Statement 2
a)
Yes
b)
No
62.
Statement 3
a)
Yes
b)
No
63.
.
a)
clustering
b)
regression
c)
classification
64.
.
a)
to train the model twice to attain better accuracy
b)
to train multiple models simultaneously to attain better performance
c)
to test the model by using data that was not used to train the model
65.
.
a)
Use a graphical user interface (GUI) to run automated machine learning experiments.
b)
Create a compute instance to use as a workstation.
c)
Use a graphical user interface (GUI) to define and run machine learning experiments from Azure Machine Learning designer.
d)
Create a dataset from a comma-separated value (CSV) file.
66.
.
a)
Education Level
b)
Last Name
c)
Age
d)
Income Range
e)
First Name
67.
.
a)
Custom Vision
b)
Form Recognizer
c)
Face
d)
Computer Vision
68.
Do two images of a face belong to the same person?
a)
grouping
b)
identification
c)
similarity
d)
verification
69.
Does this person look like other people?
a)
grouping
b)
identification
c)
similarity
d)
verification
70.
Do all the faces belong together?
a)
grouping
b)
identification
c)
similarity
d)
verification
71.
Who is this person in this group of people?
a)
grouping
b)
identification
c)
similarity
d)
verification
72.
Identify celebrities in images.
a)
Facial recognition
b)
Image classification
c)
Object detection
d)
Optical character recognition (OCR)
73.
Extract movie title names from movie poster images.
a)
Facial recognition
b)
Image classification
c)
Object detection
d)
Optical character recognition (OCR)
74.
Locate vehicles in images.
a)
Facial recognition
b)
Image classification
c)
Object detection
d)
Optical character recognition (OCR)
75.
.
a)
optical character recognition (OCR)
b)
object detection
c)
image classification
d)
face detection
76.
.
a)
Computer Vision
b)
Custom Vision
c)
Form Recognizer
d)
Video Indexer
77.
.
a)
object detection
b)
semantic segmentation
c)
optical character recognizer (OCR)
d)
image classification
78.
.
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.
79.
.
a)
predicting how many cups of coffee a person will drink based on how many hours the person slept the previous night.
b)
analyzing the contents of images and grouping images that have similar colors
c)
predicting whether someone uses a bicycle to travel to work based on the distance from home to work
d)
predicting how many minutes it will take someone to run a race based on past race times
80.
.
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.
81.
.
a)
anomaly detection
b)
conversational AI
c)
computer vision
d)
natural language processing
82.
Statement 1
a)
Yes
b)
No
83.
Statement 2
a)
Yes
b)
No
84.
Statement 3
a)
Yes
b)
No
85.
.
a)
Extract the invoice number from an invoice.
b)
Translate a form from French to English.
c)
Find image of product in a catalog.
d)
Identify the retailer from a receipt.
86.
Statement 1
a)
Yes
b)
No
87.
Statement 2
a)
Yes
b)
No
88.
Statement 3
a)
Yes
b)
No
89.
.
a)
semantic segmentation
b)
image classification
c)
object detection
d)
optical character recognition (OCR)
90.
.
a)
classify email messages as work-related or personal.
b)
predict the number of future car rentals.
c)
predict which website visitors will make a transaction.
d)
stop a process in a factory when extremely high temperatures are registered.
91.
.
a)
Translator Text
b)
Text Analytics
c)
Speech
d)
Language Understanding (LUIS)
92.
.
a)
Translator Text
b)
QnA Maker
c)
Speech
d)
Language Understanding (LUIS)
93.
.
a)
Translator Text
b)
Text Analytics
c)
Speech
d)
Language Understanding (LUIS)
94.
Statement 1
a)
Yes
b)
No
95.
Statement 2
a)
Yes
b)
No
96.
Statement 3
a)
Yes
b)
No
97.
Extracts persons, locations, and organizations from the text
a)
Entity recognition
b)
Natural language processing
c)
Sentiment analysis
d)
Speech recognition and speech synthesis
e)
Translation
98.
Evaluates text along a positive-negative scale
a)
Entity recognition
b)
Natural language processing
c)
Sentiment analysis
d)
Speech recognition and speech synthesis
e)
Translation
99.
Returns text translated to the specified target language
a)
Entity recognition
b)
Natural language processing
c)
Sentiment analysis
d)
Speech recognition and speech synthesis
e)
Translation
100.
Statement 1
a)
Yes
b)
No
101.
Statement 2
a)
Yes
b)
No
102.
Statement 3
a)
Yes
b)
No
103.
.
a)
language detection
b)
sentiment analysis
c)
key phrase extraction
d)
entity recognition
104.
.
a)
entity recognition
b)
key phrase extraction
c)
sentiment analysis
d)
translation
105.
[API Feature] Understand how upset a customer is based on the text contained in the support ticket
a)
Entity recognition
b)
key phrase extraction
c)
Language detection
d)
Sentiment analysis
106.
[API Feature] Summarize important information from the support ticket.
a)
Entity recognition
b)
key phrase extraction
c)
Language detection
d)
Sentiment analysis
107.
[API Feature] Extract key dates from the support ticket
a)
Entity recognition
b)
key phrase extraction
c)
Language detection
d)
Sentiment analysis
108.
.
a)
entity recognition
b)
key phrase extraction
c)
sentiment analysis
d)
language detection
109.
.
a)
an in-car system that reads text messages aloud
b)
providing closed captions for recorded or live videos
c)
creating an automated public address system for a train station
d)
creating a transcript of a telephone call or meeting
110.
.
a)
sentiment analysis
b)
speech recongnition
c)
speech synthesis
d)
translation
111.
.
a)
Text Analytics
b)
Translator Text
c)
Speech
d)
Language Understanding (LUIS)
112.
Statement 1
a)
Yes
b)
No
113.
Statement 2
a)
Yes
b)
No
114.
Statement 3
a)
Yes
b)
No
115.
.
a)
anomaly detection
b)
semantic segmentation
c)
regression
d)
natural language processing
116.
.
a)
QnA Maker
b)
Azure Bot Service
c)
Form Recognizer
d)
Anomaly Detector
117.
.
a)
Generate the questions and answers from an existing webpage.
b)
Use automated machine learning to train a model based on a file that contains the questions.
c)
Manually enter the questions and answers.
d)
Connect the bot to the Cortana channel and ask questions by using Cortana.
e)
Import chit-chat content from a predefined data source.
118.
.
a)
QnA Maker
b)
Text Analytics
c)
Computer Vision
d)
Language Understanding (LUIS)
119.
.
a)
Text Analytics
b)
QnA Maker
c)
Azure Bot Service
d)
Translator Text
120.
.
a)
a smart device in the home that responds to questions such as ג€What will the weather be like today?ג€
b)
a website that uses a knowledge base to interactively respond to usersג€™ 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
121.
.
a)
a sentiment analysis solution
b)
a chatbot
c)
a machine learning model
d)
a computer vision application
122.
.
a)
Custom Vision
b)
QnA Maker
c)
Translator Text
d)
Face
123.
.
a)
QnA Maker
b)
Language Understanding (LUIS)
c)
Text Analytics
d)
Speech
124.
.
a)
anomaly detection
b)
Computer vision
c)
Conversational AI
d)
foreasting
125.
.
a)
Determine whether reviews entered on a website for a concert are positive or negative, and then add a thumbs up or thumbs down emoji to the review
b)
Translate into English questions entered by customers at a kiosk so that the appropriate person can call the customers back.
c)
Accept questions through email, and then route the email messages to the correct person based on the content of the message.
d)
From a website interface, answer common questions about scheduled events and ticket purchases for a music festival.
126.
Statement 1
a)
Yes
b)
No
127.
Statement 2
a)
Yes
b)
No
128.
Statement 3
a)
Yes
b)
No
129.
Statement 1
a)
Yes
b)
No
130.
Statement 2
a)
Yes
b)
No
131.
Statement 3
a)
Yes
b)
No
132.
Statement 1
a)
Yes
b)
No
133.
Statement 2
a)
Yes
b)
No
134.
Statement 3
a)
Yes
b)
No
135.
.
a)
a telephone answering service that has a pre-recorder 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
136.
Statement 1
a)
Yes
b)
No
137.
Statement 2
a)
Yes
b)
No
138.
Statement 3
a)
Yes
b)
No
139.
.
a)
key phrase extraction
b)
sentiment analysis
c)
business logic
d)
active learning
140.
.
a)
Text Analytics
b)
Azure Bot Service
c)
Translator
d)
Form Recognizer
141.
Statement 1
a)
Yes
b)
No
142.
Statement 2
a)
Yes
b)
No
143.
Statement 3
a)
Yes
b)
No
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