Trending March 2024 # 15+ Technical Seo Interview Questions For Your Next Hires # Suggested April 2024 # Top 9 Popular

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Technical SEO requires technical and analytical skills together with a good understanding of how Google and other search engines work.

A technical SEO must be familiar with the most popular CMS systems and know at least the basics of HTML, CSS, and JavaScript.

In addition to that, a good technical SEO should know the fundamental rules of SEO and be able to identify if a website breaks these rules.

Finally, a technical SEO must be able to offer possible fixes to the problems identified on the website and be able to determine whether the fixes were implemented correctly.

But how can you verify that your next technical SEO hire has these skills and knowledge?

In this article, you’ll find 15 sample job interview questions that will help you decide whether the person you are interviewing is the right candidate for a technical SEO position.

Let’s get started!

1. How Do You Check Whether A URL Is Indexed By Google?

The site: command is the simplest way to quickly check if a given URL is in Google’s index.

Every technical SEO should know the site: command and, ideally, a bunch of other Google search operators that allow for filtering and narrowing search results.

In addition, you may also ask the candidate how they would check how many pages are indexed by Google and what the most accurate way of doing that is.

Here your ideal technical SEO hire should demonstrate familiarity with the Google Search Console Coverage report and indicate how it differs from the site: command.

2. How Do You Block A URL From Being Indexed?

With this question, you want to see whether your potential technical SEO hire actually knows the purpose of a no-index tag and does not confuse it with blocking a page in robots.txt.

They should know that chúng tôi is for controlling and optimizing crawling while no-index tags are for keeping pages out of Google’s index.

In addition, you may also ask about the best ways to protect a page from being accessed by everyone, including curious people (i.e. protecting it with a password in addition to adding a no-index tag).

If the person says that you should block such a page in chúng tôi then it means they still have a lot of SEO homework to do.

3. What Are The Most Important SEO Ranking Factors, In Your Opinion?

Of course, there is no definitive answer to this question. But hearing the person’s perspective on Google ranking factors may tell you a lot about their knowledge & experience.

A good technical SEO specialist candidate will:

Back up their answers with data or – better – data based on their own experience or SEO tests they performed.

Be willing to show you their own websites and talk about the SEO strategies they used to grow the sites.

Avoid absolute statements (e.g. these things are Google SEO rankings factors with this amount of weighing for every website).

Understand the difference between correlation and causation.

Not be afraid of saying “it depends” or “I don’t know” where it makes sense.

4. What SEO Myths Have You Had Enough Of?

Only a person with at least some knowledge and understanding of SEO will be able to answer that question.

If you are looking for an experienced technical SEO expert, ask them to elaborate on their favorite SEO myths and how they deal with them on a daily basis.

5. What Is Your Favorite Website Crawler And Why?

Website crawlers are probably the most important tools for technical SEOs.

For example, everyone can plug the domain name into the crawler and start the crawl but only an experienced technical SEO expert will know:

How to configure the crawl to check exactly what they want to analyze (e.g. check the PSI metrics in bulk for all pages).

How to execute JavaScript to compare the rendered HTML with the source HTML.

How to change the user agent if the crawl does not want to start.

How to actually interpret the data the crawler presents.

How to prioritize the issues the crawler highlights.

You want your next technical SEO specialist to be familiar with all or most of the most popular crawlers, such as Screaming Frog, Sitebulb, Deepcrawl, JetOctopus, etc.

6. How Do You Analyze Page Speed And Core Web Vitals?

Your potential technical SEO hire should use both the Google PageSpeed Insights tool (the Google Lighthouse report) and the Core Web Vitals report in Google Search Console to analyze the speed and performance of the site before drawing any conclusions or giving recommendations.

The point with this question is to check that the person:

Really knows the difference between lab data (the data provided by Google Lighthouse) and field data (the data provided by the CrUX report) and knows which ones to prioritize (i.e. field data).

Knows when it’s best to use the GSC Core Web Vitals report (i.e. to check pages in bulk) and the PSI tool (to get an overview of one specific page, usually the homepage).

Ideally, your candidate also knows other speed and performance tools, such as GTmetrix or WebPageTest, and knows how to use crawlers to analyze the lab performance of pages in bulk.

7. What Are Some Quick Technical SEO Wins?

In this question, you want your potential SEO hire to draw on their experience.

Even though there is no best answer here, you want to see that the person can really differentiate between low-impact, high-impact, low-effort, and high-effort technical SEO optimizations.

For example, it always makes a huge difference if you compress images on the website and convert them to JPEG or WEBP. Meanwhile, it may not really help a lot to rebuild the entire website (and use a ton of resources in the process) to get it from 92/100 score to 98/100 in PSI.

8. A Site That’s Been Online 9 Months Is Getting Zero Traffic. Why?

Ask for the possible reasons that come to mind.

Sometimes the solutions to problems in SEO are simple – for example, the site has no organic traffic because a no-index tag has not been removed or simply GA is not working correctly.

Other times, they require a ton of technical and data analysis that goes well beyond checking the indexability of pages.

With this question, you want the person to demonstrate their ability to look for solutions, think critically, and be creative.

9. How Do You Check If Googlebot May Have Problems Accessing Site Content?

A good technical SEO expert must know something about JavaScript rendering and the potential problems that JavaScript-based websites may face.

Here you want the person to demonstrate:

At least basic knowledge of the topic of SEO & JavaScript (i.e. their familiarity with Martin Splitt from Google).

Their knowledge of tools that allow for comparing rendered HTML with source HTML, such as Screaming Frog, Sitebulb, Rendertron, and – obviously – the URL Inspection tool in GSC.

10. What Example Errors May An XML Sitemap Have And How Would You Handle Them?

I see people focus too much on XML sitemaps with small websites (a couple of hundreds of URLs or less) and too little on that for huge sites (multi-million-page sites).

When it comes to XML sitemaps, you want your next technical SEO hire to show that they know:

What XML errors can be classified as low-impact (e.g. using deprecated parameters) and high-impact (e.g. indicating non-indexable pages).

When it is important to put a lot of focus on the XML sitemap (e.g. with huge sites that may have indexability and crawlability issues as opposed to small websites).

How to use XML sitemaps to improve and optimize the crawl budget of the site.

What pages should be included in the sitemap and how different CMS systems generate XML sitemaps.

11. How Do You Perform A Technical SEO Audit?

With the help of this question, you want to check if the person has their own SEO process for auditing a website.

Do they use a set of different tools to do that? Or do they rely on a fully automated audit where the tool (not the person) decides what issues the site has and what their priorities are?

At this point, you may also:

Ask the person to show you the examples of technical SEO audits they have performed.

Get them to explain how they approached particular issues.

And have them talk about the results their recommendations brought (if they have been implemented).

12. You Discovered That A Website Has Hundreds Of Duplicate Pages. What Do You Do?

With this question, again, you want the person to demonstrate their critical thinking abilities and desire to look for solutions.

There is no right answer here but an experienced technical SEO specialist should mention the following in their answers:

Checking the index status of these pages to make sure that these duplicates actually create a problem.

Checking the user-declared and Google-selected canonicals for these pages (possibly with the new URL Inspection Tool API).

Checking where these pages are in the internal linking structure of the website.

In addition, you may also ask the person when duplicate content is not an issue and how to check if the site actually has this problem.

If the person is saying that the site may get penalized for duplicate content, it means they have some catching up to do.

13. What Do You Use Google Search Console For? What’s Your Favorite Use Of That Tool?

Google Search Console, in most cases, should be the number one SEO tool for technical SEOs.

You want your future technical SEO hire to share with you how they use the tool and how it helps them to achieve their SEO goals.

There is no single correct answer to that question again but you probably want them to mention the following:

The Coverage report and what its specific buckets are for.

The Page Experience report and its limitations.

The Crawl Stats report and how it can be used to analyze how Google crawls the website.

The Security report and how you can use GSC to check if a site has been infected.

Ways to use GSC to analyze internal linking.

14. How Do You Check If The Site Uses Structured Data And Whether It Is Valid?

Structured data can be a specialty itself within SEO but you still want your technical SEO to:

Be familiar with tools, such as Schema Markup Validator and Google Rich Results Test and know the difference between them,

Know how to use crawlers, such as Screaming Frog or Sitebulb to analyze structured data in bulk for many pages,

Be familiar with SEO Chrome extensions like Detailed SEO that allow for quickly looking up what types of structured data are used on a particular page.

Here, you may also ask the person about the difference between structured data, rich results, and featured snippets.

People often confuse these.

15. What Are Your Favorite SEO Resources?

This is a totally open question but the more resources the person cites, the geekier they are.

An absolute must is that they are familiar with Google Search Central, read the Google SEO documentation, and watch the SEO office hours with John Mueller.

If you hire an SEO geek, you can be sure they will never miss any meaningful SEO news and will be happy to test and implement new strategies.

Bonus: Yes Or No Questions

Open questions are great for seeing how a person thinks and how deep their knowledge actually is.

However, yes and no questions may also help you check if a person updates their knowledge frequently and really knows this stuff.

Here are a few yes and no questions about technical SEO to ask your potential hire.

Ask them to justify their answers to get even more insight:

Is structured data a Google ranking factor?

Do errors in the Coverage report in GSC always indicate an error on your website?

Can you use Google Search Console to analyze internal links on the website?

Can Google penalize you for duplicate content?

Is it possible for Google to treat a 302 redirect as 301?

Can you inform Google about the new domain for your website in a different way than through a 301 redirect?

Should you noindex category and tag pages?

Should a non-existent page always return 404?

Does Google always use the canonical URL you declared?

Does Google always respect the nofollow attribute on links?

Final Thoughts On Interviewing Technical SEOs

If your prospective technical SEO hire managed to get through all of these questions and gave you satisfactory answers,  congratulations!

Chances are good that you have a pretty smart and experienced technical SEO wanting to work for you.

On the other hand, even if the candidate wasn’t able to answer all of your questions currently but has a willingness to learn and genuine interest in SEO, they may still make a brilliant technical SEO expert in some time – if you give them a chance.

More resources: 

Featured Image: fizkes/Shutterstock

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14 Technical Seo Takeaways From Techseoboost

TechSEOBoost is a tech SEO’s paradise: incredibly technical and actionable sessions inspiring innovative approaches and empowering solutions.

What was a PPC person doing there!?!?!

Usually, if there’s a PPC track, the PPC folks will go there. If there’s an SEO track, the SEO folks will go there.

Too long have the silos between SEO and PPC blocked empathy and knowledge shared between the two disciplines.

This PPC marketer was curious to understand what pain points and innovations our counterparts were exploring.

One of the best parts of the conference was seeing how many parallels there are in where SEO and PPC are evolving.

If there’s one focus point we can all agree on, it’s audiences and understanding the shifting desires in audiences.

The other takeaway weaving its way through most talks was to share data and make marketing a truly cross-department initiative.

Each speaker had great takeaways – here’s the round-up of the main takeaway from each presentation:

1. NLP for SEO

Pragmatism is a beautiful trait, and Paul Shapiro led a great discussion on how to decide which parsing method would serve you best.

In the spirit of pragmatism, Python was a requirement for this talk.

Lemmatization, while more accurate takes more time.

When deciding how you’ll parse, consider the scope of the content and whether the intent could be lost by taking the faster Stemming route.

2. Automate, Create & Test with Google App Scripts

This session solicited audible excitement for good reason: David Sottimano gave us easy hacks to easily analyze 10 BILLION rows of data without SQL!

The “secret” is Sheets data connector and the implications are exactly as exciting as they sound!

Sottimano outlined the following use cases:

Clean and manipulate data quickly in sheets.

Parse URLs quickly.

Scraping Google via SEPapi.

Creating your own auto-suggest.

For everyday SEO, these practical use-cases were suggested:

Checking for indexing and 301 targets in the same action:

Monitoring pages, comparing content and caching:

Machine learning classification using bigML and SEMrush keyword data:

3. When You Need Custom SEO Tools

The first panel of TechSEOBoost focused on knowing when third-party tools might not be enough and it makes sense to invest in proprietary tools.

The panel consisted of:

Nick Vining: Catalyst (moderator)

Claudia Higgins: Argos

Derek Perkins: Nozzle

JR Oakes: Locomotive

While the panelists each had their unique perspective to share, the overarching theme they focused on was cross-department empathy and data access.

Investing the time and resources to build a custom solution may seem daunting, the panelists all agree that having a single source of digestible truth more than pays for it.

Specific soundbites we call can learn from:

Higgins discussed shedding fear around building a custom solution/thinking it’s only possible if you have a really technical background. Don’t allow lack of tech chops get in the way of you solving a problem you know needs solving!

Oakes empowers us to use usage as a good metric to decide if a tool is outdated, as well as never build unless there’s a clear understanding of the outcome.

Perkins reminds us to hold off on automating a function/data set until it happens at least three times. Any less than three and the sample size and data focus will be compromised.

4. Bias in Search & Recommender Systems

To be human is to have bias – and the impact of those biases are felt in our careers, purchases, and work ethic.

Ricardo Baeza-Yates outlined three biases that have far-reaching implications:

Presentation bias: Whether a product/service/idea is presented and can, therefore, be an eligible choice.

Cultural bias: The factors that go into work-ethic and perspective.

Language bias: The amount of people who speak the language most content is in.

Presentation bias has the biggest impact on SEO (and PPC). If you’re not presented during the period of consideration, you’re not going to be chosen.

It’s not sustainable to own everyone’s presentations bias, so we must understand which personas represent the most profit.

Once we’re in front of our ideal people, we must know how to reach them.

Enter culture and language bias.

Baeza-Yates translates culture bias as living on two scales: minimum effort to avoid the max shame.

Depending on the market, you’ll need to tailor your messaging to honor higher/lower work ethics.

Language bias is an insidious one – the majority of content is in English, but only 23% of the internet accessing world speaks English.

5. GoogleBot & JavaScript

Whenever a Googler shares insights, there’s always at least one nugget to take home.

The big takeaways from Google’s Martin Splitt included:

Google knows where iframes are and odds are it is making it into the DOM.

Avoid layout thrashing – it invites lag time in rendering.

WRS is simply HTML + content/resources: That’s your DOM tree.

Google doesn’t just rely on an average timeout metric – they balance it with network activity.

Mobile indexing has tighter timeouts.

If a page can’t render correctly due to a “Google” problem, they’ll surface an “other” error.

Consider which side of the devil’s bargain you want to be on: if you bundle your code you’ll have fewer requests, but any change will require re-uploading.

Only looking at queue time and render time will lead you down the wrong path – indexing pipeline could be the issue.

I will admit as a PPC, most of this didn’t have the “shock and awe” for me as it did for the rest of the room. That said, one big takeaway I had was on page layout and the impact on CRO (conversion rate optimization).

The choices we make to optimize for conversions (page layout, content thresholds, contact points, etc.) align more than I would have assumed with the Google SEO perspective.

That said, the tests needed in both disciplines confirm the value of dedicated PPC pages and the importance of cross-department communication.

6. What I Learned by Building a Toy to Crawl Like Google

It’s easy to complain and gloat from the sidelines. It takes a brave and clever mind to jump in and take a stab at the thing you may or may not have feelings about.

JR Oakes is equal parts brave, clever, and generous.

You can access his “toy crawler” on Github and explore/adapt it.

His talk discussing the journey focused on three core messages:

If we’re going to build a crawler to understand the mechanics of Google, we need to honor the rules Google sets itself:

Text NLP is really important and if honoring BERT mechanics, stop words are necessary (no stemming).

Understanding when and where to update values and is far harder than anticipated and it created a new level of sympathy/empathy for Google’s pace.

The main takeaway: take the time to learn by doing.

7. Faceted Nav: Almost Everyone Is Doing It Wrong

Faceted navigation is our path to help search engines understand which urls we care they crawl.

Sadly, there’s a misconception that faceted navs are only for ecommerce sites, leaving content rich destination sites exposed to crawl risk.

Yet if every page gets faceted navigation, the crawl will take too long/exceed profit parameters.

Successfully leveraging faceted navigation means identifying which pages are valuable enough to “guarantee” the crawl.

As a PPC, I loved the shout-out for more collaboration between SEO and paid. Specifically:

Sharing data on which pages convert via PPC/SEO so both sides know how to prioritize efforts.

8. Generating Qualitative Content with GTP2 in All Languages

Nothing drives home how much work we need to do to shatter bias, than translation tools. Vincent Terrasi shared the risks of being “complacent” in translation:

Different languages have different idioms/small talk mechanics

Gender mechanics influence some languages while have no baring on others

Rare verbs, uncommon tenses, and language specific mechanics that get lost in translation.

The result: scaling content generation models across non-English speaking populations fails.

Terrasi won’t let us give up!

Instead, he gave us a step by step path to begin creating a true translation model:

Generate the compressed training data set via Byte Pair Encoding (BPE).

Use SenencePiece to generate the BPE file.

Fine tune the model (slide)

Generate the article with the trained model

You can access Terrasi’s tool here.

Where I see PPC implications is in ad creative – we often force our messaging on prospects without honoring the unique mechanics of their markets. If we can begin to generate market specific translations, we can improve our conversion rates and market sentiment.

9. Advanced Data-Driven Tech SEO – Pagination

Conversion rate optimization (CRO) is a crucial part of all digital marketing disciplines.

Yet we often overlook the simple influencers on our path to profit.

One such opportunity is pagination (how we layout the number of pages and products per page).

The more pages clients have to go through to reach their ideal product/content, the greater the risk for mental fatigue.

While there are pros and cons to all forms of pagination, Ghost Block far and away did the best job of honoring user and crawl behaviors.

Here are the outcomes of all pagination formats:

10. The User Is the Query

Dawn Anderson’s perspective on query analysis and audience intent is empowering for SEO and PPC professionals alike.

Way ahead of the curve on BERT, she empowers us to think about the choices we present our prospects and how much we are playing into their filters of interest.

In particular, she challenged us to think about:

The impact of multi-meaning words like “like” and how context of timing, additional words, and people speaking them influences their meaning.

When head terms (“dress” “shoes” “computer”) can have super transactional intent, versus being high up in the funnel.

For example, “Liverpool Manchester” is most often a travel query, but when football is on, it turns into a sports query.

Anderson encourages us to focus on the future – specifically:

Shifting away from text-heavy to visual enablement. We need to come from a place of curation (for example, hashtags) as opposed to verbatim keyword matching.

Shifting away from query targeting and opting more into content suggestions based on persona predictions

Shifting away from answers and weaving ourselves into user journeys (nurturing them to see us a habitual partner rather than a one-off engagement).

This session had the most cross-over implications for PPC – particularly because we have been shifting toward audience-oriented PPC campaigns for the past few years.

11. Ranking Factors Going Casual

I have so much love in my heart for a fellow digital marketer who sees board games as a path to explain and teach SEO/PPC.

This session gave a candid and empowering view on why we need to think critically about SEO studies.

Micha Fisher-Kirshner reminds us to be:

Consistent with our data collection and be honest with ourselves on sample size/statistical significance.

Mindful of positive and negative interactions and what impact they can have on our data sets.

Organized in our categorizations and quality checks.

My favorite takeaway (based on Mysterium) is to be mindful of the onset of any study and be sure all the necessary checks are in place. Much like the game, it’s possible to set one’s self up to have a “no win” condition simply because we didn’t set ourselves up correctly.

I also have to give Fisher-Kirshner a shout out for coming at this from a place of positivity, and not “outing” folks who mess up these checks. Instead, he simply inspired all of us to chase better causation and correlation deduction.

12. Advanced Analytics for SEO

Analytics is the beating heart of our decisions – and getting to learn from this panel was a treat.

Our cast of players included:

Dan Shure – Evolving SEO (host)

Aja Frost – HubSpot

Colleen Harris – CDK Global LLC

Jim Gianoglio – Bounteous

Alexis Sanders – Merkle

While each panelist had their own unique perspective, the overarching suggestion is sharing data between departments and working together to combat anomalies.

Gianoglio reminds us to be mindful of filters that might distort data and never allow a client to force us to a single guiding metric.

Frost shared her skepticism that analytics will be our single source for truth in the emerging GDPR and CCPA world as well as empowering us to explore data blending if we aren’t as confident in SQL to explore data blending.

Harris encouraged us to be pragmatic and realistic about data sources: if the data seems off, we should explore it! Analytics is a means to uncover data distortion.

Sanders encourages us to pull revenue numbers and marry analytics with tools like Screaming Frog and SEMrush to create true attribution for SEO’s impact on profit.

13. Crawl Budget Conqueror

Jori Ford outlined a really pragmatic approach to crawl budgets: honor your money pages and account accordingly!

Her four-step approach is:

Determine the volume of pages and only use the sitemap to correlate if it’s an optimized site map.

Understand which pages are being crawled naturally via custom tracking and log file analyzers (Botify, Deepcrawl, OnCrawl, etc.).

Assess the frequency of pages crawled and how many pages are getting crawled frequently/infrequently.

Segment by percentage type: page type, crawl allocation,  active vs. inactive, and not crawled.

14. Leveraging Machines for Awesome Outreach

Gareth Simpson invites us to explore tasks we can delegate to AI and machine learning. However, before we can, we need to have practical workflows to build machine learning into our day.

Here are the paths to machine learning:

Gather data from sources.

Cleaning data to have homogeneity.

Model building/Selecting the right ML algorithm.

Gaining insights from the model’s results.

Data visualization: transforming results into visual graphs.

Testing machine learning in prospecting might seem crazy (the human element of the relationship is crucial). Simpson helps us uncover delegatable tasks:

More Resources:

Image Credits

All screenshots taken by author from (TechSEOBoost slide decks), December 2023

Top 6 Amazon Athena Interview Questions


Amazon Athena is an interactive query tool supplied by Amazon Web Services (AWS) that allows you to use conventional SQL queries to evaluate data stored in Amazon S3. Athena is a serverless service. Thus there are no servers to operate, and you pay for the queries you perform. Athena is built on Presto, an open-source distributed SQL query engine, and supports various data formats such as CSV, JSON, ORC, and Parquet. Athena allows you to instantly query and analyze massive datasets stored in S3 without having to set up costly ETL procedures or manage infrastructure, making it an efficient and cost-effective data analysis solution.

Athena uses the Amazon Glue Data Catalog, a managed metadata catalog that holds table definitions and schema information, allowing data to be queried without the need to set up or administer a database. Athena may be used for ad-hoc querying, data analysis, and BI reporting, and it can be integrated with other AWS services, such as Amazon QuickSight and AWS Glue. Overall, Amazon Athena provides a simple and powerful approach to analyzing data in S3 without sophisticated data infrastructure setup and management.


Learning Objectives

We will go through the fundamentals of Amazon Athena and how it works.

We will learn several data types offered by Amazon Athena.

We’ll examine how AWS Glue Data Catalog works and relates to Amazon Athena.

Finally, we will cover how to optimize query performance in Amazon Athena and secure data stored in Amazon S3 and queried using Amazon Athena.

This article was published as a part of the Data Science Blogathon.

Table of Contents Q1. What Exactly is Athena, and How does it Function?

Amazon Athena is an Amazon Web Services (AWS) query service that allows you to evaluate data stored in Amazon S3 using regular SQL queries. Athena is a serverless service. Thus there are no servers to operate, and you simply pay for the queries you perform. To use Amazon Athena, create tables in Athena that refer to data in Amazon S3. You can construct tables in Athena that point directly to your S3 data or utilize the Amazon Glue Data Catalog to define external tables that indicate your S3 data. When you’ve defined your tables, you can use the Athena Query Editor or any other standard SQL client to perform SQL queries against them.

When you perform a query in Amazon Athena, the service scales up the resources required to conduct the query and provides the results to you. Athena utilizes Presto, an open-source distributed SQL query engine, to perform your requests. Presto breaks down your query into small jobs spread across a cluster of Amazon EC2 servers. Each instance executes a subset of the query, and the results are merged to get the final output. CSV, JSON, ORC, and Parquet are among the data formats supported by Amazon Athena. You can also use Athena to analyze structured data in relational databases by crawling your database using Amazon Glue and creating a table definition that refers to your data.

Overall, Amazon Athena provides a simple and powerful approach to analyzing data in S3 without sophisticated data infrastructure setup and management. Users may evaluate data stored in various formats using standard SQL queries, and the serverless aspect of the service makes it simple to expand and improve query performance.

Serverless:  It is a serverless service requiring no servers or infrastructure. This removes the need for complex database maintenance duties like scalability, patching, and backups, allowing you to concentrate on data analysis.

Cost-effective:  It charges you only for the queries you perform, with no setup fees or minimum fees. Because you pay for the resources you use, it is a cost-effective alternative for ad-hoc data analysis. Because you pay for the resources you use, it is a cost-effective alternative for ad-hoc data analysis.

Scalability:  It grows automatically to accommodate massive datasets and high query volumes. This means you can examine petabytes of data without requiring or managing new resources.

Flexibility: It supports various data formats, including CSV, JSON, ORC, and Parquet. This enables simple data analysis from multiple sources without pre-processing or transformation.

Easy Integration: It interfaces easily with other AWS services, such as AWS Glue and Amazon QuickSight, making constructing end-to-end data analytics solutions simple.

It provides a versatile, scalable, and cost-effective approach to analyzing data stored in Amazon S3 using standard SQL queries without requiring complicated database administration or infrastructure management.

Q3.What are the Many Data Formats that Athena Supports?

It supports several data formats, including:

CSV (Comma Separated Values): A basic text-based file format for storing tabular data.

JSON (JavaScript Object Notation): A simple, easy-to-read data transfer format.

ORC (Optimized Row Columnar): A high-performance columnar storage format for Hadoop data processing.

Parquet: A columnar storage format developed to increase query speed for huge collections.

Avro: A binary data format that is small and quick and is intended for efficient data serialization and deserialization.

Apache HBase: A NoSQL database designed for fast read/write access to massive datasets.

Amazon CloudFront logs: Amazon CloudFront logs include extensive information on user content requests.

It also supports data saved in Amazon S3 in compressed forms like gzip and Snappy. You may write your custom SerDe (Serializer/Deserializer) to read data in additional formats. Overall, the vast range of supported data formats makes it simple to evaluate data saved in diverse forms in Amazon Athena using typical SQL queries.

Q4. What is the AWS Glue Data Catalog, and How Does it Connect to Athena?

The Amazon Glue Data Catalog is a managed metadata repository that maintains data source and schema information. It is a common repository for storing and maintaining metadata for numerous AWS services, including Athena, such as table definitions, partition information, and schema versions. As you crawl your data sources with Amazon Glue, it automatically extracts information and builds table definitions in the AWS Glue Data Catalog. It may utilize these table definitions to construct external tables that allow you to query data stored in Amazon S3 using regular SQL chúng tôi manages metadata about data sources and schemas using the AWS Glue Data Catalog. When you execute a query in Athena, it leverages the AWS Glue Data Catalog table definitions to understand the structure of the data, allowing it to optimize query execution and increase performance. Data versioning is also supported by the Amazon Glue Data Catalog, allowing you to trace changes to data sources and schemas across time. This ensures that your queries always use the correct schema and data definitions.

Overall, the AWS Glue Data Catalog is an essential component of the AWS analytics stack, serving as a centralized repository for metadata management across different AWS services, including Amazon Athena.

Q5. How can Query Performance in Athena be Improved?

With Amazon Athena, there are numerous approaches to improve query performance:

Partitioning: To decrease the quantity of data scanned by your queries, partition your data depending on one or more columns. You may dramatically increase query speed by splitting your data and limiting the amount of data examined by a query to only.

Compression: You may compress your data on Amazon S3 using a supported compression format like Snappy or GZIP. The reduction can increase query speed by reducing the quantity of data scanned by your queries.

Columnar storage: By lowering the quantity of data scanned and enhancing data compression, you may improve query speed by storing your data in a columnar format like ORC or Parquet.

Query tuning: You may improve the performance of your queries by using suitable query syntaxes, such as choosing just the required columns and eliminating superfluous joins and subqueries. You may also improve query speed by utilizing appropriate data types, such as integer or date data types, and avoiding costly operations, such as regular expressions.

To guarantee that queries run fast and efficiently, optimizing query performance in Amazon Athena needs a mix of data management approaches, query optimization, and workgroup management.

Q6. How can Data Stored in Amazon S3 and Queried Using Athena be Secured?

There are numerous methods for protecting data stored in Amazon S3 and queried using Amazon Athena:

Encryption: You may encrypt your data at rest in Amazon S3 using server-side encryption. To help you safeguard your data, Amazon S3 offers multiple encryption solutions, including AWS KMS-managed keys and customer-managed keys. You may also encrypt your data before uploading it to Amazon S3 using client-side encryption.

Access Control: You may manage who has access to your Amazon S3 data by using access control tools such as bucket policies and object ACLs. AWS Identity and Access Management (IAM) may also govern access to Amazon Athena, enabling you to designate who can perform queries and access query results.

VPC Endpoints: AWS Identity and Access Management (IAM) may also govern access to Amazon Athena, enabling you to designate who can perform queries and access query results.  Amazon VPC endpoints allow you to securely access Amazon S3 and Athena through a private network connection without exposing your data to the public internet. This can assist in increasing data security and prevent illegal access.

Encryption in Transit: Encrypt data as it travels between Amazon S3, Athena, and your application using encryption in transit. This is possible because the SSL/TLS protocols encrypt data as it travels over the network.

Auditing and Logging: AWS CloudTrail can audit and log all API calls made to Amazon S3 and Athena. This allows you to monitor data access and identify unwanted access or activity.

Overall, safeguarding data stored in Amazon S3 and queried using Amazon Athena necessitates a mix of encryption, access control, network security, and auditing to secure your data from illegal access and exploitation.


Key takeaways of this article:

Initially, we examined it, a powerful and versatile tool for accessing data stored in Amazon S3 using conventional SQL.

It supports numerous data formats, including CSV, JSON, and Apache Parquet.

Finally, We talked about optimizing query performance in Amazon Athena by partitioning your data, compressing it, and using columnar formats like Parquet, as well as how to secure data stored in Amazon S3 and queried using Amazon Athena by using encryption, access control, network security, and auditing.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion. 


Top 9 Valuable Statistics Interview Questions And Answer For 2023

Introduction to Statistics Interview Questions And Answers

Statistics is a branch of mathematics mainly concerned with the collection, analysis, interpretation, and presentation of tons of numerical facts. It helps us to understand the data.

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So you have finally found your dream job in Statistics but are wondering how to crack the 2023 Statistics Interview and what could be the probable Statistics Interview Questions. Every interview is different, and the job scope is different too. Keeping this in mind, we have designed the most common Statistics Interview Questions and Answers to help you get success in your interview.

Statistics Interview Questions And Answers

The following Statistics Interview Questions and Answers are mentioned below.

1. Name and explain a few methods/techniques used in Statistics for analyzing the data?


It is an important technique in statistics. The number of the quantity obtained by summing two or more numbers/variables and then dividing the sum by the number of numbers/variables.

If the group is odd, arrange the numbers in the group from smallest to largest. The median will be the one that is exactly sitting in the middle, with an equal number on either side of it. If the group is even, arrange the numbers to pick the two middle numbers and add them, then divide by 2. It will be the median number of that set.

The mode is also one of the types for finding the average. A mode is a number that occurs most frequently in a group of numbers. Some series might not have any mode; some might have two modes which is called bimodal series.

In the statistics study, the three most common ‘averages’ in statistics are Mean, Median, and Mode.

Standard Deviation measures how much your data is spread out in statistics.

Regression is an analysis in statistical modeling. It’s a statistical process for measuring the relationships among the variables; it determines the strength of the relationship between one variable and a series of other changing independent variables.

2. Explain statistics branches?

The two main branches of statistics are descriptive statistics and inferential statistics.

Descriptive Statistics methods include displaying, organizing, and describing the data.

Inferential Statistics: Inferential Statistics conclude from data that are subject to random variation, such as observation errors and sample variation.

3. List all the other models that work with statistics to analyze the data?

Statistics, along with Data Analytics, analyzes the data and helps a business to make good decisions. Predictive ‘Analytics’ and ‘Statistics’ are useful for analyzing current and historical data to make predictions about future events.

4. List the fields where a statistic can be used?






Computer Science


It aids in decision-making.

Provides comparison

Explains the action that has taken place

Predict the future outcome

An estimate of unknown quantities.

5. What is linear regression in statistics?

Linear regression is one of the statistical techniques used in the predictive analysis; this technique will identify the strength of the impact that the independent variables show on deepened variables.

6. List the Sampling Methods?

In a Statistical study, a Sample is nothing but a set of or a portion of collected or processed data from a statistical population by a structured and defined procedure. The elements within the sample are known as sample points.

Below are the 4 sampling methods:

Cluster Sampling: IN the cluster sampling method, the population will be divided into groups or clusters.

Simple Random: This sampling method simply follows pure random division.

Stratified: In stratified sampling, the data will be divided into groups or strata.

Systematical: Systematical sampling method picks every kth member of the population.

7. What is the P-value, and explain it? 8. What is Data Science, and what is the relationship between Data science and Statistics?

Data Science is simply data-driven science; it involves the interdisciplinary field of automated scientific methods, algorithms, systems, and processes to extract insights and knowledge from data in any form, either structured or unstructured. Data Science and Data mining have similarities, both useful abstract information from data.

Data Sciences include Mathematical Statistics along with Computer science and Applications. By combing aspects of statistics, visualization, applied mathematics, and computer science Data Science is turning the vast amount of data into insights and knowledge.

Statistics is one of the main components of Data Science. Statistics is a branch of mathematics commerce with the collection, analysis, interpretation, organization, and data presentation.

9. What is correlation and covariance in statistics?

Covariance and Correlation are two mathematical concepts; these two approaches are widely used in statistics. Correlation and Covariance establish the relationship and measure the dependency between two random variables. Though the work is similar between these two in mathematical terms, they are different from each other.

Correlation: Correlation measures how strongly two variables are related.

Covariance: In covariance, two items vary together, and it’s a measure that indicates the extent to which two random variables change in a cycle. It is a statistical term; it explains the systematic relation between a pair of random variables, wherein changes in one variable are reciprocal by a corresponding change in another variable.

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This has been a guide to the List Of Statistics Interview Questions and Answers. Here we have listed the most useful 9 interview sets of questions so that the jobseeker can crack the interview with ease. You may also look at the following articles to learn more-

Top 11 Git Interview Questions And Answers{ Updated For 2023}

Introduction to GIT Interview Questions and Answers

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Now, if you are looking for a job that is related to GIT then you need to prepare for the 2023 GIT Interview Questions. It is true that every interview is different as per the different job profiles. Here, we have prepared the important GIT Interview Questions and Answers which will help you get success in your interview. These questions will help students build their concepts around GIT and help them ace the interview.

Part 1 – GIT Interview Questions (Basic)

This first part covers basic Interview Questions and Answers.

Q1. Define GIT and repository in GIT?

GIT is a version control system or distributed VCS to use for different projects and programmers to centralize the code of a particular project at one place. The repository in GIT consists of a directory named .git, in which it keeps all the data for the repository. The content remains private to git. GIT is recommended to use as it can be used for any project without any restrictions.

Q2. Difference between GIT and SVN?

GIT is referred to as distributed control version system and SVN is referred as a centralized version system. While working with GIT, the code can be taken once in your local machine and changes can be done and committed, and an end, the whole can be committed in one go to master branch. It means it does not require connection with a network for check in the code all the time. While working with SVN, it needs to be connected with the network when any code needs to be committed.

Q3. Mention GIT commands that are mainly used?

There are some commands that are mostly used:

GIT status: To know the comparison between the working directories and index.

GIT diff: to know the changes between the commits and the working tree.

GIT stash applies: to get the saved changes on the working directory.

GIT log: to know specific commit from the history of commits.

GIT add: It adds file changes in an existing directory to index.

GIT rm: It removes a file from the staging area.

GIT init: creating a new repository.

GIT clone: to copy or check out the working repository.

GIT commit: committing the changes.

GIT PUSH: sending the changes to the master branch.

GIT pull: fetch the code already in the repository.

GIT merge: merge the changes on the remote server to the working directory.

Git reset: to reset or drop all the changes and commits.

Q4. Explain the purpose of branching and its types? Q5. How do you resolve ‘conflict’ in GIT?

When one developer takes the code from GIT in the local system and does the change and tries to commit that code but already another developer has committed the changes. At that point, conflict arises while committing the change. To resolve the conflict in GIT, files need to be edited to fix the conflicting changes and then add the resolved files by running the GIT add command and commit the repaired merge. GIT identifies the position and sets the parents to commit correctly.

Part 2 – GIT Interview Questions (Advanced) Q6. Explain Git stash and Git stash drop?

Git Stash takes the current state of working directory and index. It pushes into the stack for later and returns cleaning the working directory. It helps in instances the work in the project and switches the branches to work. Git stash drop is used when you are done and want to eliminate the stashed item from the list, then running the GIT stash drop command will remove last added stash item by default and can also remove the specific item if any argument is included or mentioned.

Q7. What is GIT bisect and its purpose? Q9. Explain head in git?

This is the frequently asked GIT Interview Questions in an interview. A head in GIT is referred as commit object. Master is referred to as the default head in every repository. The repository can contain any number of head.

Q10. Explain SubGit and its use?

SubGit is a tool for smooth, stress-free SVN to GIT migration. It is a solution for company-wide migration from SVN to GIT. It is better than git-svn, no requirement to change the infrastructure that is already placed allows using all git and svn features, and provides genuine free migration experience.

Q11. How to rebase master in GIT?

Rebasing is defined as the process of moving a branch to a new base commit. The rule of git rebase is to never use it on public branches. To synchronize two branches is to merge them together, which results in extra merge commit and two sets of commits will contain the same changes.

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Top 21 Mobile Testing Interview Questions & Answers {Updated For 2023}

Introduction to Mobile Testing Interview Questions and Answers

The testing done for the application software developed for handheld mobile devices is called mobile application testing. The devices are tested for functionality, consistency, and usability. The testing can be automated or manual. Two types of testing are device testing and application testing. Device testing tests only handheld devices. Application testing tests the applications inside the devices. Testing makes sure that the applications can be used on different platforms and at different levels. Testing is done in various locations and with different network conditions. A global community of testers is available to test different applications of mobile devices.

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Part 1 -Mobile Testing Interview Questions(Basic)

This first part covers basic Interview Questions and Answers.

1. Define Mobile Testing?

The testing done either for devices or applications inside the mobile devices is called mobile testing.

2. Explain Mobile Application testing?

The applications inside the device are tested for its functionality, usability and consistency, usage in different locations, and different network conditions and availability. This is called mobile application testing.

3. How is the Mobile device tested?

The hardware devices are verified and validated along with built-in software applications. Troubleshooting is done for mobile applications, contents, and services. And hence the testing is carried out.

4. What are the different features for which Mobile application is tested?

The application is tested for its functionality, consistency, network conditions, usability, reliability, operational mode, efficiency, adaptability, and speed at the operational level.

5. How is Mobile Testing done?

Mobile testing can be done automatically and manually. Automated testing tests the applications in the device while manual testing tests the user experience of using the device.

6. What are the two kinds of Automation Testing done in the mobile world?

Object-based and image-based automation testing is done. Some of the object-based tools are Jama solution, Ranorex. Routinbot, EggPlant is image-based testing tools.

7. Name some Automated Testing Tools.

Experitest, Appium, Kobiton, Sendroid, MonkeyRunner, Calabash, Testingbot are some tools.

8. What tests are generally performed at the application level?

Function testing, Integration testing, Unit testing, System Testing, and Operation testing is generally performed.

9. What are the types of Mobile Application Testing?

Usability testing, compatibility testing, services testing, interface testing, low-level resource testing, performance testing, and security testing. Installation testing is done to check the installation capability of the device with the application.

10. What are the types of Mobile applications? Part 2 –Mobile Testing Interview Questions 11. While doing Application Testing, how the networks are taken into consideration?

All major networks such as 4G, 3G, 2G, and Wi-Fi are considered during application testing. It is better to consider slow networks while doing application testing so that the application performance can be tracked easily.

12. Is there any criterion while performing a Sanity Test in a mobile application?

Yes, sanity testing is carried out in specific steps. First, the application is installed and uninstalled. The application availability in different networks is tested. Various functionalities of the application are tested. Interrupt testing is done to test the availability of application while receiving calls. Compatibility testing is carried out. The application is tested in different handsets. Negative testing is also done in the end to verify the behavior of the handset while entering the wrong credentials.

13. How can we test the screen size of different Mobile devices?

Mobile emulation tools help to use mobile applications in different screen sizes and resolutions.

14. Give the differences between the emulator and simulator.

Emulator recreates the environment and tests the applications in that environment. Simulator behaves like is the indifferent environment and tests the application similar to that environment.

15. What is cloud-based Mobile Testing?

Developers and testers from around the world are connected and communicated via the internet about various mobile applications. Testing is done in a virtual environment for different applications. Different devices are available for testers virtually which in fact reduces the cost of mobile testing. All the functionalities can be tested on different devices.

16. What are the benefits of cloud-based Mobile Testing?

The user gets the choice of various devices

Parallel testing is done

The cloud environment is secure

Availability and easy access

Tools are accessed from anywhere in the world

17. Why do Mobile numbers have 10 digits?

The numbers are made 10 digits so that each user in our country has a unique mobile number one at a time.

18. What are the common bugs in Mobile testing?

The critical bug occurs when the phone crashes while the application is installed in the device. Block is though the phone is on; it is not possible to do anything unless the phone is restarted. A major bug is identified when the phone is not able to function properly. The minor bug occurs when the user interface doesn’t work properly.

19. How end to end Mobile Testing is carried out?


Application is installed

Application is launched without mobile network

Application is uninstalled

Application performance is measured

Application response is tested

20. Explain the criteria for selecting an Automation Tool for Mobile testing?


Whether the tool supports OS updates.

How long the tool takes to support the new OS

Whether the tool supports multi-platform.

Different scripts can be used or not

21. How to decide between Automated and Manual testing?

Manual testing is done if the application has new functionality and the testing is done only once or twice. Automated testing is done when the testing is repeated and there are complex scenarios.


Some mobile testing tools are easy to learn. Appium is a codeless automation tool and is user-friendly. Jobs in this field are plenty as the usage of mobile phones is increasing day by day. Jobs in this field are plenty as the usage of mobile phones is increasing day by day. Proper focus and preparation help to bag the job.

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