Trending December 2023 # A Test Plan Tool For Simpler Test Case Management # Suggested January 2024 # Top 13 Popular

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Vincent Kurutza Head of QA

Our QA team was lacking the time to write proper regression tests. We already had a much more complex and expensive tool which we never used. Then we gave Testpad a trial and the results were great.

Testpad customer for 6 years

Luke Hefson Engineering Manager

Testpad has really made writing test plans a breeze for our team. It helps us to dive into our tests, isolate defects, and provide context around the results.

Testpad customer for 9 years

Eric Wolf Senior Solutions Architect

We use Testpad to track all of our testing. It offers the depth and flexibility to model our entire test plan, but remains simple enough that onboarding new testers is effortless. The import and export facilities are really helpful for migrating test plans from other test management tools.

Testpad customer for 7 years

Jason Pritchard QA Engineer

Testpad is great! We use it every day testing embedded software, making light work of our many environments and configurations. I haven’t tried every QA tool out there but if I had the choice, I’d choose Testpad 10/10 times.

Testpad customer for 5 years

Stewart Warner Managing Director, Foxhole QA

Testpad customer for 4 years

Jason Hamilton Founder and CEO

Testpad is flexible enough to create and reuse test plans lightning fast. This helps our team spend more time preparing to test, do the actual testing and report the final results.

Testpad customer for 8 years

Brian Grau Head of Production

Testpad allows for rapid testing to fix issues in our software before it reaches our customers. The simplicity makes the testing process straight forward, gets the results to the engineering team, and is fun for the tester.

Testpad customer for 8 years

Nick Kenyon Head of Operations

Testpad customer for 11 years

Ashleigh Lodge Dev Team Lead

We couldn’t have ramped up our development schedule without Testpad. It’s a core part of our QA and the Testpad team are exceptionally responsive.

Testpad customer for 9 years

Adrian Wright Business Process and IT Strategy

I was looking at 16 different tools before I stumbled across Testpad. It’s simple and quick to use (easy enough to have business people use it with very little training). Reporting is simple and effective, making it clear who has tested what, where the bottlenecks are, and how much you still have left to do. Take a look – highly recommended.

Testpad customer for 11 years

Mike Andrews QA Manager

Testpad has become a core tool for our QA team. We’re expanding this year and will continue to use and support Testpad. We love it.

Testpad customer for 7 years

Phillip Cave VP Product Development

We rely on Testpad to help us organize our test plans and help coordinate testing between multiple people within our organization. Testpad is a valued part our release testing process at Jackson River.

Testpad customer for 10 years

Stephen Stillings Lead QA Engineer

Testpad is a crucial part of our QA toolset. The flexibility of Testpad allows us to rapidly document and execute tests and the reporting capabilities are simple yet detailed and informative. Testpad is swiftly becoming our default test management tool.

Testpad customer for 6 years

Anastasia Lutsenko Senior Tester

Great test tool! Simple and usable, it makes tester’s life easier and more productive.

Testpad customer for 10 years

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Developing A Test And Learn Programme

How to utilise continuous improvements and ‘big bets’ to achieve operational and strategic excellence

To drive meaningful digital transformation at scale, businesses must therefore be open to the adoption of a test and learn culture, which will enable marketers to optimise digital media activation, create first-class digital experiences and develop learning across the organisation. Much of this will be dependent on each organisation’s stage in the digital transformation journey:

Three big digital trends

The importance of developing a test and learn culture is reflected in the numerous digital trends impacting businesses today. However, I’d like to highlight three that I believe are particularly significant and should influence test and learn planning for digital marketers across all types of businesses:

‘Big data’ has grown up

The concept of ‘big data’ has been a familiar theme within the marketing world for at least the last five years, and yet with some predicting that 2023 would be the ‘year of the customer’ there has been an increased emphasis on customer-centric marketing, meaning data must be used intelligently to drive results.

Advanced analytics, better consumer profiles and the right market and customer insights are becoming essential in tying marketing campaigns together to create more integrated experiences.

Mobile continues to dominate

Source: Benedict Evans, 2023

More than half of our waking time is spent on media and much of that time is now consumed on mobile devices. Mobile is an increasingly ubiquitous presence in our lives (49% of Millennials check their phones within 5 minutes of waking up in the morning!) – we’re now in a truly ‘mobile-first’ world.

Mobile accounts for over a half of ecommerce traffic and a third of sales

More than half of Facebook’s base is mobile-only

App usage (90% of time) dominates browsers in mobile usage

As Benedict Evans from venture capital firm Andreessen Horowitz demonstrated last year, ‘mobile’ no longer means mobile – consumers regularly consume media on mobile devices more within the home than outside, highlighting the importance of developing a first-class mobile customer experience.

‘Content shock’ is real

Source: business2community

‘Content shock’ (coined by Mark Schaefer in 2014), is the result of consumers rejecting brand content due to exponentially increasing volumes intersecting our limited consumption capacity. Basically, people are fed up of poor quality content and are finding ways to filter this out so they can consume what really matters to them.

We can see examples of content shock in Facebook’s decision to dramatically reduce organic reach and the continuing rise of adblocking across all device types:

Source: KPCB, 2023

The average person is exposed to over 400 messages a day. Attention is at a premium and brands must respond accordingly by producing genuinely high quality, relevant content to earn this increasingly precious commodity.

Introducing a test and learn approach

Every test and learn programme will differ based on each business’s appetite to testing and their use of digital marketing in general. However one approach I’m currently adopting takes into account two aspects:

Continuous improvements – running trials to optimise day-to-day activations)

‘Big bets’ – to gain operational lessons and strategic insight to apply across the business

According to Rosabeth Moss Kanter writing in HBR in 2006, successful innovators use an ‘innovation pyramid’, with several big bets at the top that get most of the investment; a portfolio of promising mid-range ideas in test stage; and a broad base of early stage ideas or incremental innovations. This concept allows ideas and influence to flow up or down the pyramid.

For the purpose of this post, I thought I’d bring this approach to life by providing examples of two areas of continuous improvements and one ‘big bet’ to demonstrate:

a) how these areas relate to the digital trends highlighted earlier in the post

b) how these areas could fit into a digital test and learn programme

The following examples are just a sample of many others that could be used and I’ve tried to keep these fairly broad so they can be applied to different businesses, small and large, across B2C and B2B.

Two areas of continuous improvement 1. Mobile optimisation

Although 2023 cannot be regarded as ‘the year of mobile’ (that came and went at least two years ago!), mobile strategies are maturing and it is essential that brands take a ‘mobile-first’ approach to meet consumer expectations. Mobile cannot be a less-than-web experience – it has to be the experience.

Source: Google, 2014

Test and learn opportunity:

Mobile has fractured the consumer journey into hundreds of real-time, intent-driven micro-moments. Consumers are more loyal to their need rather than a brand so it’s important to test being present at the right moments, e.g.:

Prompt potential consumers ‘in-store’ with targeted promotions

Provide help and guidance when it’s needed, e.g. specific searches on YouTube or Google

Reach consumers where they’re spending their time

Source: KPCB, 2023

Test and learn opportunity:

Establish a presence across multiple platforms

Source: Benedict Evans, 2023

Mobile has led to an ‘unbundling’ of the web. We now consume content across browsers and apps, although the trend is moving more and more towards native apps which could signal the death of the hyperlink.

A mobile-first approach means that mobile must be the ultimate experience, with the web becoming merely an add-on (a complete 180° shift from where this was before).

Test and learn opportunity:

Establish a presence across multiple platforms, including responsive/ adaptive design that works across desktop and mobile, as well as native mobile apps. How is your audience consuming mobile content? What channels and platforms work for them? Where are the optimisation opportunities? Mobile apps don’t work for everyone but if there is an opportunity to test without too much risk it may be worth looking into.

2. Measurement and analytics

Effective analysis and insight should underpin everything we work on as digital marketers. Without a thorough understanding of what is and isn’t performing, you will not have the right actionable insights to make correct decisions.

Establish a measurement framework

A measurement framework/ model is a way to structure your thinking, prioritise goals and organise the KPIs and metrics you’ll use to measure performance.

Avinash Kaushik is a leading thought leader on this subject and as he explains:

“The root cause of failure in most digital marketing campaigns is not the lack of creativity in the banner ad or TV spot or the sexiness of the website. It is not even (often) the people involved. It is quite simply the lack of structured thinking about what the real purpose of the campaign is and a lack of an objective set of measures with which to identify success or failure”.

Test and learn opportunity:

Before jumping into a digital campaign or project, consider creating a measurement framework to structure how all of the following work together:

Business objectives

Macro and micro goals

Key performance indicators (KPIs)



The example above shows how goals, KPIs and segments are flow from the high-level business objectives that have been set out. The key is to understand how this framework might apply to your business and test a similar approach.

Run regular A/B testing to improve relevance

A/B or multivariate testing should form a key part of your ongoing test and learn programme. By experimenting with different types of content versus a current experience across web pages, social channels and/ or apps, you’ll be able to more accurately determine which variant performs better for a given conversion or goal.

Using the data from A/B testing removes guesswork and subjectivity, giving you the confidence to make more informed, data-driven decisions.

Test and learn opportunity:

A/B testing doesn’t have to be a complex or expensive process. Start small and experiment using particular pieces of content that have generated discussion or debate internally.

If you’re testing something more experimental or risky, take ‘controlled risks’ by showing the new content to only a small proportion of the audience (e.g. 10% of traffic). As you build an understanding and confidence in your hypothesis you can begin to increase the scope of your testing ‘landscape’.

One big bet Dynamic content and personalisation

‘Big bets’ can be anything that has the potential to enhance or optimise the organisation across multiple levels. Unlike the more operational continuous improvements highlighted above, ‘big bets’ often require more planning and investment, but if successful have the potential to future-proof a business.

For this post I’ve chosen to look at dynamic content and personlisation as an example of a ‘big bet’. This is closely linked to all three of the trends highlighted at the start of the post (data, mobile and ‘content shock’) and as I’ve tried to do throughout the post, this idea can apply to large and small businesses alike.

Optimise content to drive action

The essence of personalisation is about using content that is most relevant to the audience in order to generate higher engagement and conversion. One method of doing this is to use dynamic content, essentially showing the same web page to two people but serving different content within that page based on what we know about them:

Test and learn opportunity:

First and third-party data can be used to create more relevant and compelling experiences, and iterative platform testing can be used with or alongside A/B testing tools to learn about what is/ isn’t working. Take the time to find out what type and quality of data you have and run small tests initially to optimise content for different audience groups.

Create personal video and TV experiences

Test and learn opportunity:

Source: Google, 2023

A core goal for marketers should be about producing content and communications that matter to the audience. Programmatic marketing is something we’ve covered previously, but in a nutshell it’s about enabling brands to be responsive to their audience in real-time, with highly relevant messaging and creativity. The objective is to tailor messages to the right person, at the right moment, in the right context.

Test and learn opportunity:

Depending on device, location and weather, content can be delivered programmatically to different audiences and this is something that can be tested across different campaigns.

Google offers an excellent guide to getting started with programmatic marketing, with a useful checklist of key steps:

Organise audience insights

Design compelling creative

Execute with integrated technology

Reach audiences across screens

Measure the impact


Whilst it would be both unwise and costly to carry out tests for every trend that arises, an effective test and learn programme can enable us to carry out tactical and strategic experiments to build learnings and help us understand what works for our business.

How To Test If A Pc Can Run A Game

Are you excited to try out a new game on your PC? Try not to get ahead of yourself because it’s possible that the game might not work on your system or will give you very poor FPS (frames per second).

In this post, you’ll learn how to test if your PC can run a game before spending any money.

Table of Contents

The Manual Way

Before anything else, it’s best to go over the process manually. Doing so will help you understand what components you have in your computer.

This way, you will know how to test if your PC can run a game even if the automatic way won’t work. You’ll also get a better idea of what components on your system need to be upgraded to run the game properly.

Check Your Computer’s Specifications

First, let’s lookup the hardware information. Of the many details, focus on the CPU (processor) speed, the RAM (installed physical memory), and the GPU (graphics card) information.

You can do this without downloading anything. Just hit the Windows key, search for System Information, and launch it.

There, get the information about your PC’s CPU speed. If you have no idea how powerful or weak your CPU is based on the information provided by Windows, you can go to a site like CPU Benchmark and type in your processor there to see how it ranks overall.

Next, check the amount of RAM. These days anything less than 8 GB would be considered low, especially for a desktop PC. Also, don’t worry about the speed of the RAM, instead focus on the total RAM.

Then choose Display.

There, you’ll get more information about your Adapters and Resolution. Below, you can see that the only graphics card in this computer is Intel UHD graphics, which is built into the CPU. For any kind of gaming, you’ll obviously need a dedicated GPU.

Another easy way to do this is to use Speccy. It’s a system information tool that can provide you with all the details about the hardware in your computer.

Speccy has a free version, as well as a pro version. Since you will only be using it to retrieve basic information for now, downloading the free version is fine.

Download it into your computer and run the application. Once it’s done, you can get your hands on a lot of useful information about your computer.

Focus on CPU, RAM, and Graphics.

Check Your Game’s System Requirements

Next, go to the website where you will buy your game and find out the system requirements. If you have a hard time finding it on the website, you can search for it using Google.

Type in your game’s full name + system requirements and press enter.

Once you have located these requirements, it’s time to compare them with the system information we gathered in the previous step. Like before, your focus should be on the CPU, RAM, and Graphics.

If you want a better gaming experience, turn to the Recommended Requirements for your game. The Minimum and Recommended Requirements usually come next to each other. But if not, just go over it online. Again, use Google to search for these.

There, you can find information that’s similar to the game’s Minimum System Requirements, but slightly higher. And just like with the Minimum Requirements information, your focus should be on the CPU, RAM, and Graphics.

The Automatic Way

The manual way on how to test if your PC can run a game is straight-forward, but requires a decent amount of technical knowledge. If you prefer just being told whether or not your system is good or not, then you’ll like the method below.

Once you press enter, the requirements will be provided to you. This includes both the Minimum and Recommended System Requirements.

However, that’s not all! You’ll also see three button to the right side. The only one we are really interested in is the Can You Run It button. The other two are basically affiliate links to gaming PCs and graphics cards.

Similarly, this app will scan the hardware on your computer and then automatically compare it to the minimum and recommended requirements for the game.

Do You Need to Upgrade?

Finally, you need to decide whether your computer has the specs to run the game or if you need to invest some money in upgrading a core component of your computer.

To help with that decision, we recommend checking out a GPU comparison website like GPUCheck. Here, you can pick your current GPU and pick another GPU that you may want to purchase as an upgrade.

Finally, choose the desired quality settings you would want to use in your game. By default, it’s set to Ultra Quality, which is probably what most people want.

GPUCheck will give you detailed information about each GPU, including the FPS that you would get for different resolutions. So depending on the type of monitor of you have and whether it supports a high refresh rate and is 1080p/1440p/4K, you can quickly get an idea if you game will be playable or not.

How To Run A Memory Test On Windows 10

How to Run a Memory Test on Windows 10 [Quick steps] Check out the different ways to run the Memory Diagnostic Tool on your PC




The Memory Diagnostic Tool can help you fix some memory-related issues and errors.

You can access the tool on your PC in different ways.

You can open the Diagnostic Tool via the Command Prompt or the Run dialogue.



Easily get rid of Windows errors

Fortect is a system repair tool that can scan your complete system for damaged or missing OS files and replace them with working versions from its repository automatically. Boost your PC performance in three easy steps:

Download and Install Fortect on your PC.

Launch the tool and Start scanning


readers have already downloaded Fortect so far this month

While this tool is a saver, users can run into trouble opening this tool. So, in this guide, we will share many quick methods to run a memory test on Windows 10 using the Memory Diagnostic tool. So, let us get right into it.

How can I run a memory test on Windows 10 using the Diagnostic Tool? 1. Use the Start menu

Press the Win key to open the Start menu.

Type Windows Memory Diagnostic and open it.

You can select from the two below options:

Restart now and check for problems (recommended)

Check for problems the next time I start my computer

The tool will find any issues and fix the problem.

This is the easiest way to access the Windows Memory Diagnostic tool on your Windows 10 PC. But, of course, you can also follow the same steps for Windows 11.

2. Use Windows Search

Press the Win + S keys to open the Windows Search.

Type Windows Memory Diagnostic and open it.

Select from the two below options:

Restart now and check for problems (recommended)

Check for problems the next time I start my computer

The tool will find any issues and fix the problem.

To run the memory test on Windows 10, you can also run the Windows Memory Diagnostic tool from the Windows Search.

3. Use the Command Prompt

Press the Win key to open the Start menu.

Open Command Prompt as an admin.

Type the below command to run the Windows Memory Diagnostic tool and hit Enter. MdSched

Choose any of the two below options:

Restart now and check for problems (recommended)

Check for problems the next time I start my computer

The tool will find any issues and fix the issue.

Command Prompt is another way to help you run a memory test on Windows 10. This method could come in handy when your PC isn’t booting.

In that case, you can access the recovery mode, open the command prompt, and run the tool to fix memory issues.

4. Use the system settings

Expert tip:

5. Use the Control Panel

Control Panel is another option for running the memory test on Windows 10 using the Diagnostic Tool.

6. Use File Explorer

Open the File Explorer.

In the address bar, type MdSched and hit Enter.

Choose any of the two below options:

Restart now and check for problems (recommended)

Check for problems the next time I start my computer

The tool will find any problems and fix the issue.

7. Use the Task Manager

It is a bit complicated to run the memory test on Windows 10; however, it can come in handy when all you have access to is the Task Manager.

8. Use the Run dialogue

Press the Win + R key to open the Run dialogue.

Type MdSched and press Enter.

You can choose from any of the two below options:

Restart now and check for problems (recommended)

Check for problems the next time I start my computer

The tool will find any problems and fix the issue.

That is it from us in this guide. Our guide explains what you can do if the Memory Diagnostic Tool gets stuck on your PC.

If you are getting a hardware problem with the Memory Diagnostic Tool, then you can refer to the solutions in our guide to fixing the issue.

For users facing the Memory Refresh Timer error on their Windows PCs, we have a guide that explains a bunch of solutions to resolve the problem.

Still experiencing issues?

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Learn The Different Test Techniques In Detail

Introduction to Test techniques

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Web development, programming languages, Software testing & others

List of Test techniques

There are various techniques available; each has its own strengths and weakness. Each technique is good at finding particular types of defects and relatively poor at finding other types of defects. In this section, we are going to discuss the various techniques.

1. Static testing techniques 2. Specification-based test techniques

all Specification-based techniques have the common characteristics that they are based on the model of some aspect of the specification, enabling the cases to be derived systematically. There are 4 sub-specification-based techniques which are as follows

Equivalence partitioning: It is a specification-based technique in which test cases are designed to execute representatives from equivalence partition. In principle, cases are designed to cover each partition at least once.

Boundary value analysis: It is a technique in which cases are designed based on the boundary value. Boundary value is an input value or output value which is on the edge of an equivalence partition or at the smallest incremental distance on either side of an edge. For example, minimum and maximum value.

Decision table testing: It is a technique in which cases are designed to execute the combination of inputs and causes shown in a decision table.

State transition testing: It is a technique in which cases are designed to execute valid and invalid state transitions.

3. Structure-based testing

Test coverage: It is a degree that is expressed as a percentage to which a specified coverage item has been exercised by a test suite.

Statement coverage: It is a percentage of executable statements that the test suite has exercised.

Decision Coverage: It is a percentage of decision outcomes that a test suite has exercised. 100% decision coverage implies both 100% branch coverage and 100% statement coverage.

Branch coverage: It is a percentage of the branches that the test suite has exercised. 100% branch coverage implies both 100% decision coverage and 100% statement coverage.

4. Experience-based testing

The experience-based technique is a procedure to derive and select the cases based on the experience and knowledge of the tester. All experience-based have the common characteristics that they are based on human experience and knowledge, both of the system itself and likely defects. Cases are derived less systematically but may be more effective. The experience of both technical people and business people is a key factor in an experience-based technique.


The most important thing to understand here is that the best technique is no single testing, as each technique is good at finding one specific class of the defect. also, using just a single technique will help ensure that any defects of that particular class are found. It may also help to ensure that any defects of other classes are missed. So using a variety of techniques will help you ensure that a variety of defects are found and will result in more effective testing. Therefore it is most often used to statistically test the source code.

Recommended Articles

This is a guide to Test Techniques. Here we discuss the List of Various Test techniques along with their Strength and Weakness. You may also have a look at the following articles to learn more –

30 Questions To Test A Data Scientist On Natural Language Processing


Humans are social animals and language is our primary tool to communicate with the society. But, what if machines could understand our language and then act accordingly? Natural Language Processing (NLP) is the science of teaching machines how to understand the language we humans speak and write.

We recently launched an NLP skill test on which a total of 817 people registered. This skill test was designed to test your knowledge of Natural Language Processing. If you are one of those who missed out on this skill test, here are the questions and solutions. We encourage you t go through them irrespective of whether you have gone through any NLP Program or not.

Here are the leaderboard ranking for all the participants.

Overall Distribution

Below are the distribution scores, they will help you evaluate your performance.

You can access the scores here. More than 250 people participated in the skill test and the highest score obtained was 24.

Helpful Resources

Here are some resources to get in-depth knowledge of the subject.

And if you are just getting started with Natural Language Processing, check out most comprehensive programs on NLP-

Skill Test Questions and Answers





F) 1, 2, 3 and 4

Solution: (C)

Lemmatization and stemming are the techniques of keyword normalization, while Levenshtein and Soundex are techniques of string matching.

2) N-grams are defined as the combination of N keywords together. How many bi-grams can be generated from a given sentence:

“Analytics Vidhya is a great source to learn data science”

E) 11

Solution: (C)

Bigrams: Analytics Vidhya, Vidhya is, is a, a great, great source, source to, To learn, learn data, data science

3) How many trigrams phrases can be generated from the following sentence, after performing following text cleaning steps:

Stopword Removal

Replacing punctuations by a single space

“#Analytics-vidhya is a great source to learn @data_science.”

E) 7

Solution: (C)

After performing stopword removal and punctuation replacement the text becomes: “Analytics vidhya great source learn data science”

Trigrams – Analytics vidhya great, vidhya great source, great source learn, source learn data, learn data science

4) Which of the following regular expression can be used to identify date(s) present in the text object:

D) None of the above

Solution: (D)

None if these expressions would be able to identify the dates in this text object.

Question Context 5-6:

You have collected a data of about 10,000 rows of tweet text and no other information. You want to create a tweet classification model that categorizes each of the tweets in three buckets – positive, negative and neutral.

5) Which of the following models can perform tweet classification with regards to context mentioned above?

C) None of the above

Solution: (C)

Since, you are given only the data of tweets and no other information, which means there is no target variable present. One cannot train a supervised learning model, both svm and naive bayes are supervised learning techniques.

A) Naive BayesB) SVMC) None of the above

6) You have created a document term matrix of the data, treating every tweet as one document. Which of the following is correct, in regards to document term matrix?

Removal of stopwords from the data will affect the dimensionality of data

Normalization of words in the data will reduce the dimensionality of data

Converting all the words in lowercase will not affect the dimensionality of the data

F) 1, 2 and 3

Solution: (D)

Choices A and B are correct because stopword removal will decrease the number of features in the matrix, normalization of words will also reduce redundant features, and, converting all words to lowercase will also decrease the dimensionality.

7) Which of the following features can be used for accuracy improvement of a classification model?

E) All of theseSolution: (E)

A) Frequency count of termsB) Vector Notation of sentenceC) Part of Speech TagD) Dependency GrammarE) All of these

All of the techniques can be used for the purpose of engineering features in a model.

8) What percentage of the total statements are correct with regards to Topic Modeling?

It is a supervised learning technique

LDA (Linear Discriminant Analysis) can be used to perform topic modeling

Selection of number of topics in a model does not depend on the size of data

Number of topic terms are directly proportional to size of the data

E) 100

Solution: (A)

LDA is unsupervised learning model, LDA is latent Dirichlet allocation, not Linear discriminant analysis. Selection of the number of topics is directly proportional to the size of the data, while number of topic terms is not directly proportional to the size of the data. Hence none of the statements are correct.

9) In Latent Dirichlet Allocation model for text classification purposes, what does alpha and beta hyperparameter represent-

D) Alpha: density of topics generated within documents, beta: density of terms generated within topics TrueSolution: (D)

A) Alpha: number of topics within documents, beta: number of terms within topics FalseB) Alpha: density of terms generated within topics, beta: density of topics generated within terms FalseC) Alpha: number of topics within documents, beta: number of terms within topics FalseD) Alpha: density of topics generated within documents, beta: density of terms generated within topics True

Option D is correct

10) Solve the equation according to the sentence “I am planning to visit New Delhi to attend Analytics Vidhya Delhi Hackathon”.

C = (# of words with frequency count greater than one)What are the correct values of A, B, and C? E) 6, 4, 3

Solution: (D)

Nouns: I, New, Delhi, Analytics, Vidhya, Delhi, Hackathon (7)

Verbs: am, planning, visit, attend (4)

Hence option D is correct.

11) In a corpus of N documents, one document is randomly picked. The document contains a total of T terms and the term “data” appears K times.

What is the correct value for the product of TF (term frequency) and IDF (inverse-document-frequency), if the term “data” appears in approximately one-third of the total documents?

D) Log(3) / KTSolution: (B)

A) KT * Log(3)B) K * Log(3) / TC) T * Log(3) / KD) Log(3) / KT

formula for TF is K/T

formula for IDF is log(total docs / no of docs containing “data”)

= log(1 / (⅓))

= log (3)

Hence correct choice is Klog(3)/T

Question Context 12 to 14:

12) Which of the following documents contains the same number of terms and the number of terms in the one of the document is not equal to least number of terms in any document in the entire corpus.

D) d5 and d6Solution: (C)

A) d1 and d4B) d6 and d7C) d2 and d4D) d5 and d6

Both of the documents d2 and d4 contains 4 terms and does not contain the least number of terms which is 3.

13) Which are the most common and the rarest term of the corpus?

D) t5, t6Solution: (A)

A) t4, t6B) t3, t5C) t5, t1D) t5, t6

T5 is most common terms across 5 out of 7 documents, T6 is rare term only appears in d3 and d4

14) What is the term frequency of a term which is used a maximum number of times in that document?

D) t1 – 2/6Solution: (B)

A) t6 – 2/5B) t3 – 3/6C) t4 – 2/6D) t1 – 2/6

t3 is used max times in entire corpus = 3, tf for t3 is 3/6

15) Which of the following technique is not a part of flexible text matching?

D) Keyword HashingSolution: (D)

A) SoundexB) MetaphoneC) Edit DistanceD) Keyword Hashing

Except Keyword Hashing all other are the techniques used in flexible string matching

16) True or False: Word2Vec model is a machine learning model used to create vector notations of text objects. Word2vec contains multiple deep neural networks

B) FALSESolution: (B)


Word2vec also contains preprocessing model which is not a deep neural network

17) Which of the following statement is(are) true for Word2Vec model?

D) All of the aboveSolution: (C)

A) The architecture of word2vec consists of only two layers – continuous bag of words and skip-gram modelB) Continuous bag of word (CBOW) is a Recurrent Neural Network modelC) Both CBOW and Skip-gram are shallow neural network modelsD) All of the above

Word2vec contains the Continuous bag of words and skip-gram models, which are deep neural nets.

D) 6Solution: (D)

A) 3B) 4C) 5D) 6

Subtrees in the dependency graph can be viewed as nodes having an outward link, for example:

Media, networking, play, role, billions, and lives are the roots of subtrees

Text cleaning

Text annotation

Gradient descent

Model tuning

Text to predictors

D) 13452

Solution: (C)

A right text classification model contains – cleaning of text to remove noise, annotation to create more features, converting text-based features into predictors, learning a model using gradient descent and finally tuning a model.

20) Polysemy is defined as the coexistence of multiple meanings for a word or phrase in a text object. Which of the following models is likely the best choice to correct this problem?

D) All of theseSolution: (B)

A) Random Forest ClassifierB) Convolutional Neural NetworksC) Gradient BoostingD) All of these

CNNs are popular choice for text classification problems because they take into consideration left and right contexts of the words as features which can solve the problem of polysemy

21) Which of the following models can be used for the purpose of document similarity?

D) All of the aboveSolution: (D)

A) Training a word 2 vector model on the corpus that learns context present in the documentB) Training a bag of words model that learns occurrence of words in the documentC) Creating a document-term matrix and using cosine similarity for each documentD) All of the above

word2vec model can be used for measuring document similarity based on context. Bag Of Words and document term matrix can be used for measuring similarity based on terms.

22) What are the possible features of a text corpus

Count of word in a document

Boolean feature – presence of word in a document

Vector notation of word

Part of Speech Tag

Basic Dependency Grammar

Entire document as a feature

F) 123456

Solution: (E)

Except for entire document as the feature, rest all can be used as features of text classification learning model.

23) While creating a machine learning model on text data, you created a document term matrix of the input data of 100K documents. Which of the following remedies can be used to reduce the dimensions of data –

Latent Dirichlet Allocation

Latent Semantic Indexing

Keyword Normalization

D) 1, 2, 3

Solution: (D)

All of the techniques can be used to reduce the dimensions of the data.

24) Google Search’s feature – “Did you mean”, is a mixture of different techniques. Which of the following techniques are likely to be ingredients?

Collaborative Filtering model to detect similar user behaviors (queries)

Model that checks for Levenshtein distance among the dictionary terms

Translation of sentences into multiple languages

D) 1, 2, 3

Solution: (C)

Collaborative filtering can be used to check what are the patterns used by people, Levenshtein is used to measure the distance among dictionary terms.

25) While working with text data obtained from news sentences, which are structured in nature, which of the grammar-based text parsing techniques can be used for noun phrase detection, verb phrase detection, subject detection and object detection.

D) Continuous Bag of WordsSolution: (B)

A) Part of speech taggingB) Dependency Parsing and Constituency ParsingC) Skip Gram and N-Gram extractionD) Continuous Bag of Words

Dependency and constituent parsing extract these relations from the text

26) Social Media platforms are the most intuitive form of text data. You are given a corpus of complete social media data of tweets. How can you create a model that suggests the hashtags?

D) All of theseSolution: (D)

A) Perform Topic Models to obtain most significant words of the corpusB) Train a Bag of Ngrams model to capture top n-grams – words and their combinationsC) Train a word2vector model to learn repeating contexts in the sentencesD) All of these

All of the techniques can be used to extract most significant terms of a corpus.

27) While working with context extraction from a text data, you encountered two different sentences: The tank is full of soldiers. The tank is full of nitrogen. Which of the following measures can be used to remove the problem of word sense disambiguation in the sentences?

C) Use dependency parsing of sentence to understand the meaningsSolution: (A)

A) Compare the dictionary definition of an ambiguous word with the terms contained in its neighborhoodB) Co-reference resolution in which one resolute the meaning of ambiguous word with the proper noun present in the previous sentenceC) Use dependency parsing of sentence to understand the meanings

Option 1 is called Lesk algorithm, used for word sense disambiguation, rest others cannot be used.

28) Collaborative Filtering and Content Based Models are the two popular recommendation engines, what role does NLP play in building such algorithms.

D) All of theseSolution: (D)

A) Feature Extraction from textB) Measuring Feature SimilarityC) Engineering Features for vector space learning modelD) All of these

NLP can be used anywhere where text data is involved – feature extraction, measuring feature similarity, create vector features of the text.

29) Retrieval based models and Generative models are the two popular techniques used for building chatbots. Which of the following is an example of retrieval model and generative model respectively.

D) Recurrent neural network and convolutional neural networkSolution: (B)

A) Dictionary based learning and Word 2 vector modelB) Rule-based learning and Sequence to Sequence modelC) Word 2 vector and Sentence to Vector modelD) Recurrent neural network and convolutional neural network

choice 2 best explains examples of retrieval based models and generative models

30) What is the major difference between CRF (Conditional Random Field) and HMM (Hidden Markov Model)?

D) Both CRF and HMM are Discriminative modelSolution: (B)

A) CRF is Generative whereas HMM is Discriminative modelB) CRF is Discriminative whereas HMM is Generative modelC) Both CRF and HMM are Generative modelD) Both CRF and HMM are Discriminative model

Option B is correct

End Notes

If you want to learn more about Natural Language Processing and how it is implemented in Python, then check out our video course on NLP using Python.

Happy Learning!


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