Accenture R Programming Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Accenture hiring for R Programming roles
Accenture is a global professional services company offering strategy, consulting, technology, operations and digital services. India hosts a large share of its workforce, delivering software engineering, testing, cloud, data and AI projects for clients worldwide. It hires freshers and experienced professionals in India for associate software engineer, test engineer, data analyst and analytics consultant roles.
For R Programming roles, this Accenture practice set focuses most on R Basics and Data Types (5), Functions and Functional Programming (5), Data Visualization with ggplot2 (5), Reports, Shiny and Production R (5). It has 3 easy, 20 medium, 7 hard questions, so you can start easy and work up to the harder ones.
Company type
Consulting
Headquarters
Dublin, Ireland
Main India offices
Bengaluru, Hyderabad, Pune, Mumbai, Gurugram
Accenture R Programming interview process
Online assessment (cognitive + technical MCQs)
Coding assessment
Communication assessment
Technical and HR interview
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
Solved for AccentureStart practicing
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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
02
What is type coercion in R and how can it cause bugs?
Coercion is the automatic conversion of values to a common type when they are combined in an atomic vector. The order is logical, then integer, then double, then character. So c(1, 'a', TRUE) becomes the character vector '1', 'a', 'TRUE'. Explicit conversion uses as.numeric(), as.character() and similar functions. A common bug is a numeric column with one stray text value, which turns the whole column into character and breaks calculations.
03
After merging two data frames you get more rows than expected. What happened?
Extra rows after a join usually mean the key is duplicated in one or both tables, so each match multiplies rows. I check uniqueness with df %>% count(key) %>% filter(n > 1) on both sides. Then I decide whether to deduplicate, aggregate before joining or join on a composite key such as customer_id plus month. In dplyr 1.1 and later, the relationship argument, for example 'many-to-one', makes the join fail loudly when this assumption breaks.
04
What is the multiple testing problem and how do you correct for it?
When you run many hypothesis tests, the chance of at least one false positive rises sharply. With 20 independent tests at alpha 0.05, it is about 64 percent. Corrections adjust p-values: Bonferroni multiplies by the number of tests and is conservative, Holm is a less strict step-down version, and Benjamini-Hochberg controls the false discovery rate. In R, p.adjust(pvals, method = 'BH') applies them, which is common when testing many metrics or segments.
05
How do you manage working directories and file paths in an R project?
getwd() shows and setwd() changes the working directory, but a hardcoded setwd() breaks on other machines. RStudio projects with the here package build paths relative to the project root.
getwd()
library(here)
sales <- read.csv(here('data', 'sales.csv'))
write.csv(summary_df, here('output', 'summary.csv'),
row.names = FALSE)
file.exists(here('data', 'sales.csv'))
list.files(here('data'), pattern = 'csv$')
06
Your function fails with 'the condition has length > 1' when given a vector. What is wrong?
An if statement can only check a single TRUE or FALSE value, and since R 4.2 a longer condition is an error. The function was written for one value but received a vector. I would replace if with a vectorized alternative such as ifelse(), dplyr::if_else() or case_when(). If the function is truly meant for single values, I validate the input length with stopifnot(length(x) == 1) or apply it over elements with purrr::map().
Facets split a plot into small multiples, one panel per category, sharing the same axes and geoms. facet_wrap(~ region) wraps panels for one variable, and facet_grid(rows = vars(segment), cols = vars(year)) creates a grid for two variables. scales = 'free_y' lets each panel have its own y-axis. Facets are often clearer than one chart with many overlapping coloured lines, for example comparing sales trends across ten states.
%>% comes from the magrittr package and |> is the native pipe built into R since version 4.1. Both pass the left side as the first argument of the right side, so df |> filter(x > 1) equals filter(df, x > 1). The native pipe is faster and needs no package, but it uses _ as a named placeholder only, while %>% supports the dot placeholder anywhere. New code increasingly uses |>, and both work with dplyr.
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.
10
Tell me about a time a production job failed and you had to fix it under pressure.
Situation: the scheduled R job feeding the morning leadership dashboard failed at 6 AM after a source table added a new column type. Task: restore the dashboard before the 9 AM meeting. Action: I checked the logs, identified the parsing error, patched the column specification, reran the job and informed stakeholders of the delay. Afterwards I added schema validation and a failure alert. Result: data was live by 8:30 AM, and similar changes are now caught early.
When two vectors of different lengths are used in an operation, R repeats the shorter one to match the longer one. For example, c(1, 2, 3, 4) + c(10, 20) gives 11, 22, 13, 24. If the longer length is not a multiple of the shorter, R gives a warning but still computes. Recycling is convenient for scalars, but silent recycling of mismatched vectors is a frequent source of wrong results in analysis code.
12
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.
13
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.
14
Tell me about a chart or visualization that changed a business decision.
Situation: the operations team believed late deliveries were spread evenly across the week. Task: I was asked to review delivery data. Action: in R I built a heatmap of late deliveries by weekday and hour with ggplot2, which showed a sharp cluster on Monday mornings linked to weekend order backlog. Result: management added a Sunday evening shift, and late deliveries dropped by about 18 percent the next quarter.
15
Your R pipeline works on your laptop but fails on the Linux server because of missing system libraries. How do you fix it permanently?
Packages like sf, xml2 or RPostgres need system libraries such as GDAL, libxml2 or libpq, which differ between machines. I would containerize the pipeline with Docker, starting from a rocker image like rocker/r-ver pinned to the R version, installing system dependencies in the Dockerfile and restoring packages with renv. The same image then runs on laptops, servers and the cloud, eliminating environment differences between development and production.
16
Describe a time you learned something from a teammate.
Situation: during my final-year project, our group's R code ran very slowly on a large dataset. Task: I was responsible for the cleaning step. Action: a teammate showed me how to replace my nested for loops with vectorized dplyr operations and explained why loops were slow. I practised the approach on other parts of the code. Result: processing time fell from twenty minutes to under one, and I shared the lesson with the rest of the group.
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.
18
What is the difference between Quarto and R Markdown?
Quarto is the next-generation publishing system from Posit. It works with R, Python, Julia and Observable, and is a standalone command-line tool rather than an R package. It uses the same knitr engine for R and similar syntax, with chunk options written as #| comments. Quarto adds consistent support for websites, books, presentations and dashboards. R Markdown is still supported, but new projects increasingly use Quarto, especially in mixed R and Python teams.
19
How would you compare R and Python for a data role?
R is strongest for statistics, exploratory analysis, visualization and reproducible reporting, with mature packages for biostatistics, econometrics and survey analysis. Python is a general-purpose language that is stronger for production software, deep learning and web integration. Many teams use both: R for analysis and reporting, Python for pipelines and deployment. In an interview, avoid language wars and explain that the choice depends on the team's stack, the problem and who maintains the code.
20
A function that looks up exchange rates is called thousands of times with the same inputs and is slow. How do you fix it?
The function repeats expensive work for identical inputs, so I would cache its results with memoisation. The memoise package wraps a function, memo_rate <- memoise::memoise(get_rate), and stores results by argument values, returning cached answers instantly. I can set a cache that expires, for example daily for rates, or store it on disk with cachem so it survives sessions. Alternatively I fetch all needed rates once and join them as a lookup table.
dplyr uses data masking, so you can write column names without quotes. Inside your own function, a column passed as an argument is not found automatically because R looks for a variable with that name. Embracing the argument with {{ }} tells dplyr to use the column the caller supplied. For column names stored as strings, you use .data[[var]]. For new column names, use the glue syntax '{{var}}_mean' := with the walrus operator.
23
What is the difference between if, ifelse() and dplyr's if_else()?
if is a control-flow statement that checks a single TRUE or FALSE value and runs one branch, used for program logic. ifelse() is vectorized and returns a vector of the same length as the condition, used for transforming data. dplyr::if_else() is a stricter vectorized version that requires both outputs to have the same type, preserves classes like dates and has a missing argument for NA conditions, making it safer than ifelse().
The Central Limit Theorem says that the distribution of sample means approaches a normal distribution as sample size grows, roughly beyond 30, regardless of the population's shape, provided observations are independent with finite variance. It is why t-tests, confidence intervals and A/B test calculations work on non-normal data such as order values. You can demonstrate it in R by repeatedly sampling from rexp() and plotting a histogram of the means.
26
Leadership wants a single report page with 15 charts. How do you approach it?
I first ask which decisions the page supports and which three to five metrics matter most, because 15 charts usually hide the message. I put headline KPIs at the top, followed by trend charts and then breakdowns, with consistent colours, scales and a shared theme function. Less important views go to a drill-down tab in a Shiny or Quarto dashboard. I test the draft with one stakeholder and remove charts nobody uses.
27
Your function gives correct results when you test it but wrong results in the main script. What could be the cause?
A common cause is that the function reads a variable from the global environment instead of taking it as an argument. During testing that global variable had one value, and in the main script it has another. Lexical scoping finds it silently. I would check the function body for names not defined as arguments or locally, for example using codetools::findGlobals(fn), and pass everything explicitly so the function is self-contained.
28
You ran an A/B test on a checkout page: 4.1 percent conversion versus 4.6 percent. How do you decide if the difference is real?
Conversion is a proportion, so I use a two-proportion test: prop.test(c(conv_a, conv_b), c(n_a, n_b)). I check the p-value and, more importantly, the confidence interval for the difference. Before trusting it, I confirm the sample size was planned in advance, the test ran for full weeks to cover weekday effects, users were randomized properly and nobody stopped the test early when results looked good. Then I judge business significance.
29
What is the difference between coord_cartesian() limits and scale limits in ggplot2?
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.
30
Reading a 2 GB CSV file with read.csv() is very slow. What would you do?
read.csv() is single-threaded and guesses types slowly. I would switch to data.table::fread() or readr::read_csv(), which are many times faster and multi-threaded. I would read only the needed columns with the select argument and set column types explicitly. If the file is reused, I convert it once to a binary format like Parquet with the arrow package, which loads in seconds and supports reading just a subset of columns.
Tips to crack the Accenture R Programming interview
Practise the communication assessment, which checks spoken English through reading, listening and repeating sentences in a recorded format.
Revise MS Office, cloud basics, networking and pseudocode, as Accenture technical MCQs often cover these general IT topics.
Read about Accenture's focus on cloud, data and AI and be ready to discuss how you would work in client-facing teams.
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; Accenture interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Accenture R Programming interview?
Candidates usually go through 4 stages: Online assessment (cognitive + technical MCQs); Coding assessment; Communication assessment; Technical and HR interview. 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 Accenture R Programming interview?
This practice set leans on R Basics and Data Types (5), Functions and Functional Programming (5), Data Visualization with ggplot2 (5), Reports, Shiny and Production R (5). Revise those first, then practise explaining one or two of your own projects end to end, because most Accenture interviewers ask you to walk through real work.
Are these the exact questions asked at Accenture?
No. These are curated practice questions that match the skills and difficulty usually tested in consulting interviews like Accenture's. They are not a leaked or verified list of past questions.
Where does Accenture hire R Programming professionals in India?
Accenture has large teams in Bengaluru, Hyderabad, Pune, Mumbai, Gurugram. Openings and locations change often, so check the Accenture careers page and filter by city and experience.