01
What is vectorisation and why does it matter?
Show answer
Applying operations to whole arrays at once instead of Python loops, which is far faster because it runs in optimised compiled code.
LatentView Analytics Data Analytics Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About LatentView Analytics hiring for Data Analytics roles
LatentView Analytics is an Indian data analytics company providing data engineering, business analytics and data science services to global clients, many in technology, consumer goods and financial services. It hires in India for analysts, data engineers and data scientists who work on dashboards, data pipelines and predictive models.
For Data Analytics roles, this LatentView Analytics practice set focuses most on Excel (7), Dashboards and KPIs (5), SQL (5), Python for Analysis (4). It has 4 easy, 33 medium, 3 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Analytics
- Headquarters
- Chennai, India
- Main India offices
- Chennai, Bengaluru
LatentView Analytics Data Analytics interview process
- Online assessment (aptitude + SQL)
- Technical interview
- Case or guesstimate round
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
When would you drop missing values, and when would you impute with mean or median?
Show answer
Drop rows when few are missing, roughly under 5 percent, and missingness looks random. Impute when dropping would lose too much data. Use the median for skewed numbers such as income or order value, the mean for roughly symmetric data, and the mode or an Unknown category for categoricals. Group-wise imputation, such as median by city, is better than one global value. Always add a flag column showing what was imputed.
03
What is the difference between relationships, joins and data blending in Tableau?
Show answer
Relationships, the default logical layer, connect tables flexibly and Tableau decides join type and level per view, avoiding duplicated measures. Joins physically merge tables into one at a fixed level, which can duplicate rows if grains differ. Blending combines data from separate data sources at the view level using a primary and secondary source, aggregating the secondary first. Prefer relationships, use joins for specific row-level needs, and blending as a last resort.
04
What is sampling bias? Name common types.
Show answer
Sampling bias occurs when the sample does not represent the population, so conclusions do not generalise. Common types: selection bias, such as surveying only app users about website issues; survivorship bias, studying only customers who stayed; non-response bias, when unhappy customers skip surveys; and convenience sampling, using whoever is easiest to reach. Random sampling, stratification and weighting help reduce it.
05
What are parameters in Tableau and how do you use them?
Show answer
A parameter is a single user-controlled value, such as a number, date or string, that is independent of the data. On its own it does nothing; you reference it in calculations, filters, reference lines or bins. Common uses are a Top N selector, a what-if growth rate, or a measure switcher where a CASE statement picks Sales, Profit or Quantity based on the parameter. Parameter actions let users set it by clicking a mark.
06
How do you define and choose KPIs?
Show answer
Pick metrics tied to a business goal that are measurable, actionable and owned by someone, and pair a lagging metric with a leading one.
07
DAX: calculated column vs measure?
Show answer
A calculated column is computed row by row and stored in the model. A measure is computed at query time using the filter context, e.g. Total Sales = SUM(Sales[Amount]). Prefer measures for aggregations.
08
A report filters WHERE order_date = '2026-03-31' and misses almost all orders from that day. Why?
Show answer
order_date is probably a DATETIME or TIMESTAMP column, so the filter matches only rows at exactly midnight, 2026-03-31 00:00:00. Use a half-open range instead: WHERE order_date >= '2026-03-31' AND order_date < '2026-04-01'. This keeps the index usable, unlike wrapping the column in CAST or DATE(), and it correctly includes orders at 23:59:59.500. Also check the time zone of the stored timestamps.
09
How do you improve Power BI report performance?
Show answer
Use a star schema, remove unused columns, prefer measures, reduce visuals per page, limit high-cardinality fields and use aggregations or incremental refresh.
10
Write SQL to group raw page events into sessions using a 30-minute inactivity rule.
Show answer
WITH ordered AS (
SELECT user_id, event_ts,
LAG(event_ts) OVER (PARTITION BY user_id ORDER BY event_ts) AS prev_ts
FROM page_events
),
flagged AS (
SELECT *,
CASE WHEN prev_ts IS NULL
OR event_ts - prev_ts > INTERVAL '30 minutes'
THEN 1 ELSE 0 END AS new_session
FROM ordered
)
SELECT user_id, event_ts,
SUM(new_session) OVER (PARTITION BY user_id ORDER BY event_ts) AS session_no
FROM flagged;
11
How do SUMIFS and SUMPRODUCT differ?
Show answer
SUMIFS sums values meeting several criteria and is fast. SUMPRODUCT multiplies arrays and adds them, handling more complex conditions and weighted totals.
12
Explain time intelligence functions.
Show answer
DAX functions like TOTALYTD, SAMEPERIODLASTYEAR and DATEADD compare periods; they need a proper continuous date table marked as a date table.
13
How do you find duplicate rows in a table?
Show answer
SELECT col, COUNT(*) FROM t GROUP BY col HAVING COUNT(*) > 1; use ROW_NUMBER() partitioned by the key to delete extras.
14
Your scheduled Python report failed silently and nobody noticed for a week. How do you make it reliable?
Show answer
Add logging to a file or central log, wrap the main steps in try and except that send an alert by email or chat on failure, and exit with a non-zero code so the scheduler marks the job failed. Add data checks, such as row counts above a minimum and no missing dates, before sending output. Use a proper scheduler or orchestrator with retries, and include the data date in every report.
15
Write Python code to extract valid 10-digit Indian mobile numbers from a messy text column.
Show answer
import re
pattern = re.compile(r'(?:\+?91[\s-]?|0)?([6-9]\d{9})')
def get_mobile(text):
if not isinstance(text, str):
return None
digits = re.sub(r'[\s()-]', '', text)
m = pattern.search(digits)
return m.group(1) if m else None
df['mobile'] = df['contact'].apply(get_mobile)
16
Write SQL using LAG to compare each month's revenue with the previous month.
Show answer
WITH monthly AS (
SELECT DATE_TRUNC('month', order_date) AS month,
SUM(amount) AS revenue
FROM orders
GROUP BY 1
)
SELECT month,
revenue,
LAG(revenue) OVER (ORDER BY month) AS prev_revenue,
ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month))
/ NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 1) AS mom_pct
FROM monthly
ORDER BY month;
17
What are Type I and Type II errors?
Show answer
A Type I error is a false positive: concluding there is an effect when there is none, for example launching a feature that does not actually lift sales. Its probability is alpha, often 0.05. A Type II error is a false negative: missing a real effect, with probability beta. Power is 1 minus beta. Larger samples reduce Type II errors, and the business cost of each mistake should guide which one you guard against more.
18
A manager asks for a dashboard that refreshes every minute. How do you decide the right refresh frequency?
Show answer
Match refresh to how quickly decisions are made. A monthly board pack needs daily refresh at most, while a warehouse dispatch board may need 15-minute or live updates. Faster refresh costs more in source load, licensing and complexity, and can show incomplete data. Ask how quickly someone will act on a change, check when upstream systems finish loading, and always display the last refreshed time on the dashboard.
19
How does the FILTER function work in Excel? Write a formula to list all North region orders.
Show answer
-- A2:D500 holds Order, Customer, Region, Amount
=FILTER(A2:D500, C2:C500="North", "No orders")
-- two conditions (AND) multiply the tests
=FILTER(A2:D500, (C2:C500="North")*(D2:D500>10000), "No orders")
-- OR conditions add the tests
=FILTER(A2:D500, (C2:C500="North")+(C2:C500="East"))
20
Write pandas code for a pivot table of sales by region and category with sum and count.
Show answer
import pandas as pd
summary = pd.pivot_table(
df,
index='region',
columns='category',
values='sales',
aggfunc=['sum', 'count'],
fill_value=0,
margins=True # adds an All row and column
)
print(summary)
21
Tell me about a time you disagreed with your manager about how to measure something.
Show answer
Situation: my manager wanted to report average resolution time for support tickets. Task: I felt a few very old tickets distorted the average. Action: I prepared a quick comparison showing mean versus median and the percentage resolved within 24 hours, and explained why the median was more representative. Result: we agreed to show median and the 24-hour rate, keeping the mean in a tooltip, and the team stopped chasing misleading spikes.
22
Tell me about a time you had to say no to a stakeholder's request.
Show answer
Situation: a regional manager asked me to show his team's numbers excluding two weeks of a stock-out. Task: be helpful without misreporting. Action: I explained that removing the period would make his region incomparable with others, and offered an annotation and a separate view showing the stock-out impact. Result: he accepted, the leadership review understood the dip, and the main dashboard stayed consistent for every region.
23
What is a window function?
Show answer
A function that calculates over a set of rows related to the current row without collapsing them, such as ROW_NUMBER, RANK, LAG or SUM() OVER (PARTITION BY ...).
24
A stakeholder asks for a pie chart of 12 product categories. When is a pie chart acceptable, and what would you suggest?
Show answer
A pie chart works only for showing parts of a whole with very few categories, two to four, where one share clearly dominates, such as 70 percent of orders are prepaid. Avoid it when there are many slices, when values are close, or when comparing across periods, because people judge angles poorly. A sorted bar chart is almost always easier to read and compare. Never use 3D pies.
25
How do you create a dynamic dashboard in Excel?
Show answer
Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.
26
What is a data mart?
Show answer
A data mart is a subset of the warehouse built for one department or subject, such as sales, finance or HR. It contains the facts and dimensions that team needs, often pre-aggregated and with business-friendly names. Marts make reporting faster and simpler for that team. Ideally they are built from the central warehouse using conformed dimensions, so the finance mart and the sales mart still agree on revenue.
27
How do you calculate a running total?
Show answer
SUM(amount) OVER (ORDER BY order_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) adds up values cumulatively.
28
Write SQL to find the first-touch and last-touch channel for every user who converted.
Show answer
WITH touches AS (
SELECT t.user_id, t.channel, t.touch_ts,
ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts) AS first_rank,
ROW_NUMBER() OVER (PARTITION BY t.user_id ORDER BY t.touch_ts DESC) AS last_rank
FROM marketing_touches t
JOIN conversions c
ON c.user_id = t.user_id
AND t.touch_ts <= c.converted_ts
)
SELECT user_id,
MAX(CASE WHEN first_rank = 1 THEN channel END) AS first_touch,
MAX(CASE WHEN last_rank = 1 THEN channel END) AS last_touch
FROM touches
GROUP BY user_id;
29
What is broadcasting in NumPy?
Show answer
Broadcasting lets NumPy perform operations on arrays of different shapes without copying data, by virtually stretching the smaller array. For example, subtracting a 1D array of column means from a 2D array centres every column in one line: X - X.mean(axis=0). Shapes are compatible when trailing dimensions are equal or one of them is 1. It is a key reason vectorised code is much faster than Python loops.
30
What are relationships and cardinality in Power BI?
Show answer
Links between tables on key columns; cardinality can be one-to-many, many-to-one or many-to-many, and cross-filter direction controls how filters flow.
31
Write pandas code to calculate customer acquisition cost and payback period in months for each channel.
Show answer
# spend: channel, spend
# customers: channel, customer_id, monthly_margin
new_cust = customers.groupby('channel').agg(
new_customers=('customer_id', 'nunique'),
avg_margin=('monthly_margin', 'mean'))
summary = spend.groupby('channel')['spend'].sum().to_frame().join(new_cust)
summary['cac'] = summary['spend'] / summary['new_customers']
summary['payback_months'] = summary['cac'] / summary['avg_margin']
print(summary.round(1).sort_values('payback_months'))
32
What do the match_mode and search_mode arguments of XLOOKUP control?
Show answer
match_mode decides how a value is matched: 0 exact (default), -1 exact or next smaller, 1 exact or next larger, and 2 wildcard. search_mode decides direction: 1 searches first to last, -1 searches last to first, and 2 or -2 use binary search on sorted data. Searching last to first is a quick way to fetch the latest entry for a customer without sorting, and next-smaller matching handles tax slabs or commission bands.
33
Write SQL to calculate daily active users and the DAU/MAU stickiness ratio.
Show answer
WITH dau AS (
SELECT CAST(event_ts AS DATE) AS day,
COUNT(DISTINCT user_id) AS dau
FROM events
GROUP BY CAST(event_ts AS DATE)
)
SELECT d.day,
d.dau,
(SELECT COUNT(DISTINCT e.user_id)
FROM events e
WHERE CAST(e.event_ts AS DATE) BETWEEN d.day - INTERVAL '29 days' AND d.day) AS mau,
ROUND(100.0 * d.dau / (SELECT COUNT(DISTINCT e.user_id)
FROM events e
WHERE CAST(e.event_ts AS DATE) BETWEEN d.day - INTERVAL '29 days' AND d.day), 1) AS stickiness_pct
FROM dau d
ORDER BY d.day;
34
What is the difference between a dashboard and a report?
Show answer
A report is multi-page and interactive from one dataset; a dashboard is a single-page canvas of pinned tiles from many reports in the Service.
35
What is a Data Table in Excel's What-If Analysis?
Show answer
A Data Table shows how a formula's result changes when one or two inputs vary. For example, place a loan EMI formula in a corner, list interest rates down the column and tenures across the row, then use What-If Analysis, Data Table to fill every combination. It is ideal for sensitivity analysis in pricing or budgeting. Large Data Tables slow recalculation, so set calculation to automatic except tables when needed.
36
How would you design an A/B test for a new checkout button?
Show answer
Define one primary metric, such as checkout conversion, plus guardrails like refund rate. State the hypothesis and minimum detectable effect, then calculate sample size and duration for chosen power and significance. Randomise users, not sessions, into control and variant, check the split is balanced, and run for full weeks to cover weekday patterns. Do not peek and stop early. Finally analyse with a proportion test and check key segments.
37
Write an Excel formula to look up a price using two criteria, product and city.
Show answer
=XLOOKUP(1, (A2:A500=G2) * (B2:B500=H2), C2:C500, "Not found")
-- A = product, B = city, C = price
-- G2 and H2 hold the product and city to find
-- each condition returns TRUE/FALSE; multiplying gives 1 only where both match
38
What is the difference between sets and groups in Tableau?
Show answer
A group combines dimension members into higher-level categories, such as merging small states into Others; it is static and each member belongs to one group. A set splits members into In and Out based on a selection or condition, such as top 10 customers by sales, and can be dynamic and recalculate with data. Sets can be combined, used in calculations and changed by set actions, making them more powerful for interactive analysis.
39
What is Tableau Prep used for?
Show answer
Tableau Prep is a visual tool for cleaning and shaping data before analysis. You build a flow of steps: connect inputs, clean fields, split columns, group similar values, pivot, join, union and aggregate, and see the data change at every step. The output can be an extract or published data source that dashboards use. It suits analysts who need repeatable preparation without writing SQL or Python.
40
What is the difference between a table and a normal range?
Show answer
A table auto-expands, supports structured references, banded rows and slicers, and keeps formulas consistent as rows are added.
Tips to crack the LatentView Analytics Data Analytics interview
- Practise SQL queries and Excel analysis, as many LatentView roles start with reporting and data preparation.
- Prepare guesstimates and simple business cases, explaining your assumptions and calculation steps clearly to the interviewer.
- Be ready to explain one dashboard or analysis project end to end, from data cleaning to insight.
- 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; LatentView Analytics interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the LatentView Analytics Data Analytics interview?
Candidates usually go through 4 stages: Online assessment (aptitude + SQL); Technical interview; Case or guesstimate round; HR 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 LatentView Analytics Data Analytics interview?
This practice set leans on Excel (7), Dashboards and KPIs (5), SQL (5), Python for Analysis (4). Revise those first, then practise explaining one or two of your own projects end to end, because most LatentView Analytics interviewers ask you to walk through real work.
Are these the exact questions asked at LatentView Analytics?
No. These are curated practice questions that match the skills and difficulty usually tested in analytics interviews like LatentView Analytics's. They are not a leaked or verified list of past questions.
Where does LatentView Analytics hire Data Analytics professionals in India?
LatentView Analytics has large teams in Chennai, Bengaluru. Openings and locations change often, so check the LatentView Analytics careers page and filter by city and experience.
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