Amazon R Programming Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Amazon hiring for R Programming roles
Amazon is a global technology company spanning e-commerce, cloud computing through AWS, devices, advertising and streaming. In India it runs large engineering, operations and analytics teams supporting both Amazon.in and global businesses. It hires for software development engineers, SDETs, quality assurance engineers, business intelligence engineers, data engineers and data scientists.
For R Programming roles, this Amazon practice set focuses most on Reports, Shiny and Production R (7), Functions and Functional Programming (5), Data Wrangling with dplyr and tidyr (4), Data Visualization with ggplot2 (4). It has 21 medium, 9 hard questions, so you can start easy and work up to the harder ones.
Company type
Product
Headquarters
Seattle, USA
Main India offices
Bengaluru, Hyderabad, Chennai, Pune, Gurugram
Amazon R Programming interview process
Online assessment (coding + work style survey)
Technical round 1 (DSA)
Technical round 2 (DSA or system design)
Bar raiser round
Hiring manager round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for AmazonStart practicing
3 QUESTIONS FREESign up to unlock the full question bank and track your progress.Unlock every question and answer, then continue practising company-wise and package-wise.
First I investigate whether they are data errors, such as an extra zero, or genuine high earners. I flag outliers with the IQR rule, values beyond Q1 - 1.5 IQR or Q3 + 1.5 IQR, or with percentiles. Errors I fix or remove with documentation. Genuine values I keep, but I report the median alongside the mean, or winsorize at the 1st and 99th percentiles for modelling. The choice depends on the business question.
data.table is a high-performance package for large tabular data. Its syntax DT[i, j, by] means filter rows with i, compute j, grouped by by. It updates columns by reference with :=, avoiding copies, and offers fast keyed joins and fread()/fwrite() for file IO. For example, DT[city == 'Delhi', .(avg_sal = mean(salary)), by = dept] filters and aggregates in one step. It is popular for datasets with tens of millions of rows.
04
How do you build a logistic regression model for churn in R?
Rcpp is a package that lets you write C++ functions and call them from R with almost no boilerplate, using cppFunction() for small snippets or sourceCpp() for files. It is used when R code has loops that cannot be vectorized, such as recursive calculations, simulations with dependence between steps or custom algorithms. Many popular packages, including dplyr and data.table internals, rely on compiled code. Use it only after profiling shows a real bottleneck.
A closure is a function that remembers the environment in which it was created. A function factory is a function that returns such functions. For example, power <- function(exp) function(x) x^exp; square <- power(2) creates a function that remembers exp = 2. They are useful for creating families of related functions, counters and memoised functions. Because of lazy evaluation, call force(exp) inside the factory so the value is captured immediately.
07
A simulation loop runs for 40 minutes. How would you speed it up?
First I profile with profvis to find the slow lines. Then I try vectorizing, for example generating all random numbers at once with rnorm(n * reps) and using matrix operations instead of nested loops. I preallocate outputs instead of growing them. If iterations are independent, I run them in parallel with future and furrr. If a tight loop cannot be vectorized, I rewrite it in C++ with Rcpp, which often gives a 10 to 100 times speed-up.
08
Describe a disagreement with a teammate about a technical approach.
Situation: a teammate wanted to build our reporting pipeline in Excel macros, while I preferred R scripts. Task: agree on an approach quickly. Action: instead of arguing, we listed requirements together: refresh frequency, data size and who would maintain it. I built a small R prototype in a day to compare. Result: we chose R for the pipeline and kept an Excel output for users, and my teammate later helped improve the formatting.
A minimal package has a DESCRIPTION file with metadata and dependencies, a NAMESPACE file listing exported functions, an R/ folder with function code, a man/ folder for documentation and usually tests/ for testthat tests. The usethis and devtools packages create and manage this: usethis::create_package(), use_r(), use_test(), and devtools::document(), test() and check(). Documentation is written as roxygen2 comments above each function. Internal packages are ideal for sharing team utilities.
10
Your team wants to share a Shiny app with 200 internal users. What deployment options exist?
Options include shinyapps.io, a hosted service that is quickest to start but may not suit sensitive internal data, Posit Connect, a commercial on-premise or cloud server with authentication, scheduling and scaling, the free open-source Shiny Server on a Linux machine, and a Docker container behind a reverse proxy like Nginx, often with ShinyProxy for authentication. For internal data, I would choose Connect or containers inside the company network with single sign-on.
11
Write a tidymodels workflow to train and evaluate a random forest.
plotly's ggplotly() converts an existing ggplot into an interactive chart with hover tooltips, zoom and legend toggling. ggiraph adds interactive tooltips and selection to ggplot geoms with fine control. highcharter and echarts4r wrap JavaScript charting libraries, and leaflet builds interactive maps. These outputs work in R Markdown or Quarto HTML reports and Shiny apps. For static PDF reports, standard ggplot2 is still the better choice.
13
You need month-over-month sales growth for each product. How do you calculate it in R?
I first aggregate to one row per product and month with group_by() and summarise(). Then I group by product, arrange by month and use mutate(growth = (sales - lag(sales)) / lag(sales) * 100). The first month for each product becomes NA, which is correct. I make sure months with zero sales exist using tidyr::complete(), otherwise lag() compares non-consecutive months. Finally I round and format growth for reporting.
14
How do you test whether two categorical variables are related in R?
Use a chi-square test of independence on a contingency table; use Fisher's exact test when expected counts are small.
tab <- table(survey$city_tier, survey$preferred_plan)
tab
res <- chisq.test(tab)
res$p.value
res$expected # check expected counts >= 5
fisher.test(tab) # for small samples
# effect size
library(rcompanion)
cramerV(tab)
t.test() handles one-sample, two-sample and paired tests. Welch's test, which does not assume equal variances, is the default.
# two independent groups
t.test(score ~ batch, data = students)
# paired: before vs after training
t.test(after, before, paired = TRUE)
# one-sample against a target of 30 minutes
t.test(delivery_time, mu = 30, alternative = 'greater')
res <- t.test(score ~ batch, data = students)
res$p.value; res$conf.int
A list-column is a data frame column whose cells are lists, such as nested data frames or fitted models. With tidyr::nest() you group data, for example by region, into a column of sub-data frames, then use purrr::map() to fit a model per region and broom::tidy() to extract coefficients. This keeps data, models and results aligned row by row, which is cleaner than managing separate lists of models and names.
17
How do you generate the same report for each of 20 regions automatically?
Use a parameterized Quarto or R Markdown report and render it in a loop.
# report.qmd header:
# params:
# region: 'North'
# then use params$region in the code
regions <- unique(sales$region)
purrr::walk(regions, function(r) {
quarto::quarto_render(
'report.qmd',
execute_params = list(region = r),
output_file = paste0('report_', r, '.html')
)
})
18
How do you reshape data between wide and long formats in R?
Measure before optimizing. system.time() gives total time, bench::mark() compares alternatives, and profvis shows which lines consume time and memory.
system.time(result <- slow_function(df))
bench::mark(
loop = sum_loop(x),
vec = sum(x)
)
library(profvis)
profvis({
out <- clean_data(raw)
model <- fit_model(out)
})
21
Your dplyr pipeline is too slow on a 50 million row dataset. What are your options?
I can keep dplyr syntax but change the backend. dtplyr translates dplyr code to data.table, which is much faster in memory. duckdb with dbplyr runs the pipeline in an embedded columnar database, often handling data larger than RAM. arrow lets me query Parquet files with dplyr verbs lazily. I would also filter and select early, avoid row-wise operations and profile the pipeline to find the slow step before rewriting anything.
22
How would you create a reusable branded theme for all team charts?
The forecast package provides auto.arima(), ets() for exponential smoothing and easy plotting of forecasts with intervals. The newer tidyverts family, with tsibble for time-series data frames, fable for models and feasts for features and decomposition, works with tidy data and fits many series at once. prophet handles strong seasonality and holidays. Typical steps are visualizing, decomposing trend and seasonality, fitting candidates and comparing accuracy on a holdout period.
24
You are analysing a national survey where some groups were deliberately oversampled. How do you compute correct averages?
Simple averages would be biased toward oversampled groups, so I use the survey weights provided with the data. In R, the survey package defines the design with svydesign(ids = ~psu, strata = ~stratum, weights = ~wt, data = df), then svymean(), svytotal() and svyglm() give weighted estimates with correct standard errors that account for clustering and stratification. The srvyr package offers the same with dplyr-style syntax.
25
Write a minimal Shiny app that filters sales by region and plots them.
A ui with an input and an output, and a server that reacts to the input:
library(shiny); library(ggplot2); library(dplyr)
ui <- fluidPage(
selectInput('region', 'Region', choices = unique(sales$region)),
plotOutput('trend')
)
server <- function(input, output, session) {
filtered <- reactive(filter(sales, region == input$region))
output$trend <- renderPlot({
ggplot(filtered(), aes(month, revenue)) + geom_line()
})
}
shinyApp(ui, server)
geom_smooth() adds a fitted trend line with a confidence band, using loess for small data and a GAM for large data by default, or method = 'lm' for a straight line. stat_summary() computes summaries of y for each x, for example stat_summary(fun = mean, geom = 'point') to plot group means, or fun.data = mean_cl_normal for error bars. Both let you show patterns without precomputing summaries.
27
The same 40 lines of cleaning code are copied into ten scripts. What would you do?
I would extract the logic into well-named functions with arguments for the parts that differ, then move them into one shared file loaded with source() or, better, a small internal R package. A package gives documentation with roxygen2, tests with testthat and versioning, so every script uses the same tested logic. Fixing a bug then happens in one place. I would migrate the scripts gradually and compare outputs to confirm nothing changed.
28
What is the difference between reactive(), observe() and observeEvent() in Shiny?
reactive() creates a cached value that other code can read, such as filtered data, and it is lazy, recomputing only when needed. observe() runs code for its side effects whenever any reactive dependency changes, returning nothing. observeEvent() runs side effects only when a specific event fires, such as a button click, ignoring other dependencies. Use reactive() for computing values and observeEvent() for actions like saving data or showing notifications.
Setting limits with scale_y_continuous(limits = c(0, 100)) or ylim() removes data outside the range before statistics are calculated, which changes boxplots, smooth lines and bar totals and gives warnings about removed rows. coord_cartesian(ylim = c(0, 100)) only zooms the view, keeping all data in calculations. So for zooming into part of a chart without altering the summary, coord_cartesian() is the correct choice.
Tips to crack the Amazon R Programming interview
Prepare STAR-format stories for each Amazon Leadership Principle, such as Customer Obsession, Ownership and Dive Deep.
Practise medium-level DSA problems on arrays, trees, graphs and dynamic programming, explaining time and space complexity aloud.
For BIE or data roles, practise SQL window functions and metric-driven case questions on e-commerce funnels and delivery operations.
Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
Practise the hard questions on this page aloud; Amazon interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Amazon R Programming interview?
Candidates usually go through 5 stages: Online assessment (coding + work style survey); Technical round 1 (DSA); Technical round 2 (DSA or system design); Bar raiser round; Hiring manager round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Amazon R Programming interview?
This practice set leans on Reports, Shiny and Production R (7), Functions and Functional Programming (5), Data Wrangling with dplyr and tidyr (4), Data Visualization with ggplot2 (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Amazon interviewers ask you to walk through real work.
Are these the exact questions asked at Amazon?
No. These are curated practice questions that match the skills and difficulty usually tested in product interviews like Amazon's. They are not a leaked or verified list of past questions.
Where does Amazon hire R Programming professionals in India?
Amazon has large teams in Bengaluru, Hyderabad, Chennai, Pune, Gurugram. Openings and locations change often, so check the Amazon careers page and filter by city and experience.