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WorksheetsEInfochips Evaluation Test
Total questions: 75
Worksheet time: 1hrs 15mins
What are the main components of RPA platform?
Bot Creator
Control Room
Bot Runner
All of them
RPA is not suitable for processes that are?
Rules-Based
Require constant human intervention
Repetitive
Digital Data
What is RPA primarily used for?
Automating a business process
Automating a BPO process
Automating a banking process
Automate any process which has set of sequential steps
Which below App is RPA platform?
iPad
UiPad
UiPath
UiRPA
What are the industries adopting RPA?
Human Resource
Marketing & Sales
Banking & Finance
All of them
RPA bots can self-study knowledge of procedures over time.
True
False
What is RPA?
Can you give an example of a task that RPA can automate?
Making phone calls
Answering emails
Writing reports
Data entry
True or false: RPA requires programming skills to set up.
What are some popular RPA tools available in the market?
Automation Anywhere
UiPath
Can you give an example of a repetitive task that RPA can automate?
What are the benefits of using RPA?
Increased efficiency and accuracy
Reduced operational costs
Improved compliance
All of the above
How does RPA differ from traditional automation?
RPA requires human intervention
RPA is more cost-effective
RPA can handle complex tasks
RPA is slower than traditional automation
What industries can benefit the most from implementing RPA?
Healthcare
Finance
Retail
All of the above
How can companies measure the success of their RPA implementation?
Number of tasks automated
Reduction in processing time
Cost savings achieved
All of the above
What are the potential risks associated with RPA implementation?
Data security concerns
Job displacement for employees
Dependency on technology
All of the above
What is the purpose of bots in RPA?
What are the benefits of integrating RPA with other technologies like AI and machine learning?
Can you give examples of tasks that are not suitable for automation with RPA?
What are the main components of an RPA system?Bot
Bot
Controller
Orchestrator
All of the above
What are the key considerations for selecting an RPA tool?
What are the potential benefits of RPA implementation?
Increased efficiency and accuracy
Reduced operational costs
Improved compliance
All of the above
How can RPA contribute to improving customer service?
By reducing response time to customer queries
By providing personalized customer interactions
By automating repetitive customer service tasks
All of the above
How can RPA enhance data security in organizations?
By encrypting all data
By restricting access to sensitive information
By automating data handling processes to reduce human errors
By implementing multi-factor authentication
What are the key challenges companies may face during RPA implementation?
Resistance from employees
Integration with existing systems
Ensuring data security
All of the above
2. NLP is concerned with the interactions between computers and human (natural) languages.
TRUE
FALSE
NOT SURE
4. What is Natural Language Processing good for?
Automatically generate keyword tags
Summarize blocks of text
Identify the type of entity extracted
All of the above
None
5 Natural Language Processing (NLP) is the field of
Artificial Intelligence (AI)
AI and
Computer Science
Linguistics
All of the above
6. One of the main challenge/s of NLP Is _________________ .
Handling Tokenization
Handling POS-Tagging
Handling Ambiguity of Sentences
All of the above
7. Morphological Segmentation
Separate words into individual morphemes and identify the class of the morphemes
Does Discourse Analysis
Is an extension of propositional logic
None of the mentioned
8.In linguistic morphology, _____________ is the process for reducing inflected words to their root form.
Text-Proofing
Rooting
Stemming
Both b & c
9.Natural language processing is divided into the two subfields of -
time and motion
symbolic and numeric
understanding and generation
algorithmic and heuristic
10. The natural language is also known as .....................
First Generation
2nd Generation
3rd Generation language
4th Generation language
5th Generation language
11. One example of a natural language programming software program used with the iphone is called siri.
True
False
12 Natural language processing (nlp) is associated with which of the following areas?
computational linguistics
text mining
artificial intelligence
All of the above
13. All of the following are challenges associated with natural language processing except
distinguishing between words that have more than one meaning
understanding the context in which something is said.
.
dividing up a text into individual words in English.
recognizing typographical or grammatical errors in texts
14. What is Natural Language Processing good for?
Identify the type of entity extracted
Automatically generate keyword tags
Summarize blocks of text
All of the above
None
broken down of text is
Divide and Conquer
Tokinization
RemovaL OF STOPING WORDS
STEMMING
3. Machine Translation is that converts -
Human language to machine language
Any human language to English
One human language to another
Machine language to human language
Which of the following tasks is not typically considered a part of NLP?
Machine Translation
Sentiment Analysis
Image Recognition
Speech Recognition
What does stemming refer to in NLP?
The process of splitting text into sentences
The removal of stop words from a text
The reduction of words to their base or root form
The conversion of speech to text
In the context of NLP, what does NER stand for?
Natural Entity Resolution
Named Entity Recognition
Normalized Error Rate
Neural Entity Regression
What is an n-gram in the context of NLP?
A single word
A sequence of 'n' words
A phrase with at least one stop word
A type of neural network for processing text
Given the scenario, "Sneha and Naira are discussing the sentence 'Natural Language Processing is fascinating,' what is the bigram representation?
["Natural", "Language", "Processing", "is", "fascinating"]
["Natural Language", "Language Processing", "Processing is", "is fascinating"]
["Natural", "Language", "is", "fascinating"]
["Natural Language Processing", "is fascinating"]
6. Why is text normalization important in the field of customer reviews analysis?
To translate customer reviews into another language
To predict the sentiment of the next customer review
To identify the sentiment of a customer review
To convert customer reviews to a standard format
Given the scenario, "Rohan is playing in the park," what would be the output after tokenization?
"rohan is playing in the park"
["Rohan playing park"]
["rohan", "is", "play", "in", "the", "park"]
["Rohan", "is", "playing", "in", "the", "park"]
In a research paper, why is it important to remove stop words from the text?
To eliminate common but less meaningful words
To convert text to a different format
To enhance text readability
To ensure all words are spelled correctly
Why is lowercasing often performed during text pre-processing?
To ensure consistency by treating "Language" and "language" as the same word
To remove all numerical values from the text
To reduce the dimensionality of the text data
To make the text grammatically correct
Which of the following is not typically a part of the text pre-processing pipeline?
Lowercasing
Stop Words Removal
Sentiment Analysis
Text Tokenization
Which of the following best describes the predictions made by a machine learning model?
Absolutely correct values based on conditional logic.
Probabilistic values based on correlations found in training data.
Randomly selected values with an equal chance of selection.
A data scientist has used Azure Machine Learning to train a machine learning model. How can you use the model in your application?
Use Azure Machine Learning to publish the model as a web service.
Export the model as a cognitive service.
You must build your application using the Azure Machine Learning designer.
In which format are message exchanged between a client app and a cognitive services resource when using a REST API?
HTML
XML
JSON
You need to regenerate the primary subscription key for a Cognitive Services resource that an app uses. What should you do first to minimize service interruption for the app?
Switch the app to use the secondary key
Change the resource endpoint
Enable a firewall
You want to store the subscription keys for a Cognitive Services resource securely, so that authorized apps can retrieve them when needed. What kind of Azure resource should you provision.
Azure Storage
Azure Key Vault
Azure App Service
How should you collect telemetry for your Azure Cognitive Services resource for later analysis?
Create an alert.
Configure diagnostic settings.
Create a dashboard.
Which of the following parameters must you specify when deploying a Cognitive Services container image?
EULA
ResourceGroup
SubscriptionName
What is the effect of the smartCropping option when using Computer Vision to generate a thumbnail?
The aspect ratio of the original image is maintained.
The thumbnail is skewed to fit the specified proportions.
The region of interest is centered in the thumbnail.
You want Video Analyzer for Media to recognize colleagues in videos recorded from conference calls. What should you do?
Create a custom model containing a Person for each colleague, with example images of their faces.
Edit the conference call videos to include a caption of each person's name on their first appearance
Embed the Video Analyzer for Media widgets in a custom web site that employees access using their own user credentials.
Check your answers
You want Video Analyzer for Media to analyze a video. What must you do first?
Use the Computer Vision service to extract key frames from the video.
Upload the video to Video Analyzer for Media and index it.
Store the video file in an Azure blob store container.
Which of the following is an example of a deterministic algorithm?
K-Means
PCA
Both of these
None of these
A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college.
Which of the following statement is true in following case?
Feature F1 is an example of nominal variable.
Feature F1 is an example of ordinal variable.
It doesn’t belong to any of the above category.
Both (a) and (b)
What type of machine learning algorithm makes predictions when you have a set of input data and you know the possible responses?
Unsupervised
Reinforcement
Supervised
Deep Learning
When would you reduce dimensions in your data?
When data comes from sensor
When you are using a Linux machine
When your data set is larger than 500GB
When you have larger set of features with similar characteristics
Which feature selection technique uses shrinkage estimators to remove redundant features from data?
Stepwise regression
Sequential feature selection
Neighborhood component selection
Regularization
What is overfitting?
When a predictive model is accurate but takes too long to run
When the model learns specifics of the training data that can't be generalized to a larger data set
When you perform hyperparameter tuning and performance degrades
When you apply a powerful deep learning algorithm to a simple machine learning problem
What kind of table compares classifications predicted by the model with the actual class labels?
Chaos table
Confusion Matrix
Prediction plot
Residual plot
What kind of learning algorithm for "Future stock prices or currency exchange rates"?
Prediction
Recognizing Anomalies
Generating Patterns
Recognition Patterns
What kind of learning algorithm for "Facial identities or facial expressions"?
Recognizing Anomalies
Prediction
Generating Patterns
Recognition Patterns
Targeted marketing, Recommended Systems, and Customer Segmentation are applications in
Unsupervised Learning: Clustering
Supervised Learning: Classification
Reinforcement Learning
Unsupervised Learning: Regression
Machine Learning has various function representation, which of the following is not numerical functions?
Linear Regression
Support Vector Machines
Neural Network
Case-based
ML is a field of AI consisting of learning algorithms that?
Improve their performance
At executing some task
Over time with experience
All of the above
To find the minimum or the maximum of a function, we set the gradient to zero because:
The value of the gradient at extrema of a function is always zero
Depends on the type of problem
Both A and B
None of the above
How do you handle missing or corrupted data in a dataset?
Drop missing rows or columns
Replace missing values with mean/median/mode
Assign a unique category to missing values
All of the above
When performing regression or classification, which of the following is the correct way to preprocess the data?
Normalize the data -> PCA -> training
PCA -> normalize PCA output -> training
Normalize the data -> PCA -> normalize PCA output -> training
None of the above
High entropy means that the partitions in classification are
pure
not pure
useful
useless
