Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About AMD hiring for Data Analytics roles
AMD is an American semiconductor company that designs CPUs, GPUs, adaptive chips and data centre accelerators, including Ryzen, EPYC, Radeon and Instinct products. It acquired Xilinx, adding FPGA and adaptive computing products. In India it has large design centres that hire chip design, verification, physical design, software and firmware engineers.
For Data Analytics roles, this AMD practice set focuses most on Statistics and Case Studies (6), Power BI (6), Python for Analysis (5), Web & Marketing Analytics (4). It has 5 easy, 27 medium, 8 hard questions, so you can start easy and work up to the harder ones.
Company type
Hardware & semiconductors
Headquarters
Santa Clara, California, USA
Main India offices
Bengaluru, Hyderabad
AMD Data Analytics interview process
Online assessment or resume screening
Technical round 1 (digital design or programming)
Technical round 2 (domain depth)
Hiring manager round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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01
What is the difference between a t-test and a z-test?
Both compare means. A z-test assumes the population standard deviation is known or the sample is large, so the normal distribution applies. A t-test estimates standard deviation from the sample and uses the t-distribution, which has heavier tails for small samples. In practice analysts mostly use t-tests: one-sample, independent two-sample (Welch's version if variances differ) or paired, for before-after measurements on the same users.
02
What is bidirectional cross-filtering in Power BI and when is it risky?
Normally filters flow from the one side to the many side, for example from Product to Sales. Bidirectional filtering lets filters also flow back, so a Sales filter can limit Product. It helps in some many-to-many or bridge-table cases. The risks are ambiguous filter paths, unexpected results and slower performance. Prefer single direction and use CROSSFILTER inside a specific measure when you need both directions only there.
A confidence interval gives a range of plausible values for a population parameter based on a sample. A 95 percent interval for average order value of 820 to 880 rupees means that if we repeated the sampling many times, about 95 percent of such intervals would contain the true average. Wider intervals mean more uncertainty; larger samples narrow them. Reporting the interval is more honest than reporting one number.
04
What is the difference between ALL, ALLEXCEPT and REMOVEFILTERS in DAX?
ALL removes filters from a table or columns and can also return all rows as a table. REMOVEFILTERS only removes filters and is clearer inside CALCULATE. ALLEXCEPT removes all filters on a table except the columns you list. A common use is share of total: DIVIDE([Sales], CALCULATE([Sales], ALL(Product))) gives each product's percentage, while ALLEXCEPT(Product, Product[Category]) gives share within its category.
05
What is CUPED and how does it speed up experiments?
CUPED is a variance reduction method that uses each user's pre-experiment behaviour, such as spend in the previous four weeks, as a covariate. You adjust the outcome by removing the part explained by past behaviour, which is unrelated to the treatment because assignment was random. The lower variance gives tighter confidence intervals, so you can detect the same effect with fewer users or a shorter test, often 20-50% faster.
06
A file has dates in mixed formats, some dd/mm/yyyy and some mm/dd/yyyy. How do you handle it?
Find out which system or region produced each format, since a source column often explains it. Values with a day above 12 reveal the format unambiguously. Parse with an explicit format per source, for example pd.to_datetime(col, format='%d/%m/%Y', errors='coerce'), rather than letting the tool guess. Check failed or suspicious parses, such as future dates, and ask the source team to send ISO format yyyy-mm-dd going forward.
Field parameters let report users switch which measure or dimension a visual uses through a slicer. For example, one chart can switch between Sales, Profit and Quantity, or between grouping by Region, Category and Channel. This replaces many duplicate visuals or bookmark tricks, keeping the report lighter and easier to maintain. They are created from Modeling, New parameter, Fields, which generates a small table behind the slicer.
Attribution decides how credit for a conversion is shared across touchpoints. Last click gives all credit to the final touch, first click to the first one. Linear splits credit equally, time decay gives more to recent touches, and position-based gives most to first and last. Data-driven attribution uses an algorithm on your own paths to estimate each channel's contribution. Each model answers a different question, so state which one a report uses.
Build PivotTables from a clean table, add PivotCharts and slicers, link KPIs with formulas, and refresh from Power Query so everything updates together.
13
What is a lambda function in Python and where do analysts use it?
A lambda is a small anonymous function written in one line, for example lambda x: x * 1.18 to add GST. Analysts use lambdas with sorted for custom sort keys, with pandas assign for computed columns, for example assign(margin=lambda d: d.profit / d.sales), and occasionally with apply. For anything longer than one expression or reused in several places, a normal def function is clearer and easier to test.
14
You must present to the sales team that they missed their quarterly target by 18%. How do you handle it?
Lead with the fact clearly, then show where the gap came from, such as which regions, products or deal stages, so it becomes actionable rather than a blame session. Separate controllable causes like lower conversion from uncontrollable ones like a delayed product launch. Highlight what did work, and end with two or three specific actions and the metrics to track them. Share the numbers with sales leaders privately first, to avoid surprises.
15
Write a DAX measure for cumulative sales from the start of the data to the selected date.
Cumulative Sales =
VAR maxDate = MAX('Date'[Date])
RETURN
CALCULATE(
[Total Sales],
FILTER(ALL('Date'[Date]), 'Date'[Date] <= maxDate)
)
-- for a running total that resets each year use TOTALYTD([Total Sales], 'Date'[Date])
16
How do you create a dual-axis chart in Tableau, for example sales bars with a profit margin line?
Drag Order Date to Columns as a continuous or discrete month, put Sales on Rows, then drag Profit Ratio to Rows next to it. Right-click the second pill and choose Dual Axis. On the Marks card set Sales to Bar and Profit Ratio to Line. Decide whether to synchronise axes: do not synchronise when units differ, like rupees and percentage. Label the axes clearly so readers know which is which.
17
Your analysis shows that a flagship initiative championed by leadership is not working. How do you present it?
Prepare carefully and stay factual. Share the finding early with the initiative owner privately so they are not surprised in a big meeting. Lead with the question asked, the method and the evidence, acknowledge limitations and what is working, and avoid blaming anyone. Offer options, such as fixing a specific step, narrowing the scope or stopping, with estimated impact. Bad news delivered with solutions is received far better.
It automatically formats cells based on rules such as colour scales, data bars or highlighting values above a target, making patterns visible at a glance.
20
A stakeholder wants a 40-column crosstab in Tableau that looks exactly like their Excel sheet. How do you handle it?
I would ask what they do with it. If they export and work on it in Excel, a crosstab view with a download option or a scheduled extract is fine, and Tableau should not fight Excel. If they actually scan it for exceptions, I would suggest a summary view with highlights and a drill-down to the detail table. Often both: a short visual summary on top and the detailed table on a second tab.
21
What is the difference between a data warehouse, a data lake and a lakehouse?
A data warehouse stores cleaned, structured data in modelled tables optimised for SQL reporting. A data lake stores raw data of any type, such as logs, JSON and images, cheaply in files, with structure applied when read. A lakehouse combines them: open table formats on lake storage add transactions, schemas and fast SQL on top of files. Analysts typically query the warehouse or the curated layer of a lakehouse.
22
Tableau or Power BI: how would you decide which one a team should use?
Both are capable, so the decision usually depends on context. Power BI fits teams already on Microsoft 365 and Azure, has lower per-user cost, and strong modelling with DAX. Tableau is often preferred for flexible visual exploration, very polished custom visuals and mixed-platform environments. I would also weigh existing skills, data sources, governance needs, licensing and embedding requirements, then run a short pilot on a real use case.
23
Sales data from different countries mixes currencies and some weights are in grams while others are in kilograms. How do you handle it?
Never add mixed units. Identify the unit for each row from a currency or unit column, or infer it from the source system. Convert to one standard, for example INR using a dated exchange rate table joined on transaction date, and kilograms using a conversion factor. Keep the original value and unit columns for audit. Document which rates were used, and flag rows where the unit could not be determined.
24
Write pandas code to find the top 2 products by revenue in each region.
Row context evaluates one row at a time (calculated columns); filter context is the set of filters from slicers, visuals and relationships that a measure sees.
df = df.sort_values(['store', 'month'])
df['mom_pct'] = (
df.groupby('store')['sales'].pct_change() * 100
).round(1)
df['cum_sales'] = df.groupby('store')['sales'].cumsum()
# first month of each store has NaN for mom_pct, which is expected
29
What is the difference between dimensions and measures in Tableau?
Dimensions are qualitative fields such as region, product or date that slice data into groups and create headers. Measures are quantitative fields such as sales or profit that get aggregated, by default with SUM. Dragging a dimension to rows creates one row per member; dragging a measure creates an axis. A numeric field like customer ID should be converted to a dimension so it is not summed.
30
A campaign landing page gets heavy traffic but very few sign-ups. How would you diagnose it?
Check traffic quality first: is the right audience coming, and does the ad message match the page? Then look at page behaviour by device: load time, scroll depth, form start versus form submit, and which form field users abandon. Session recordings or heatmaps help. Test the form on mobile for errors. Common fixes are faster pages, fewer form fields, a clear call to action above the fold and matching the ad's offer.
31
Write SQL to calculate open rate and click-through rate for each email campaign.
SELECT
s.campaign_id,
COUNT(DISTINCT s.user_id) AS delivered,
COUNT(DISTINCT o.user_id) AS opened,
COUNT(DISTINCT c.user_id) AS clicked,
ROUND(100.0 * COUNT(DISTINCT o.user_id) / COUNT(DISTINCT s.user_id), 1) AS open_rate,
ROUND(100.0 * COUNT(DISTINCT c.user_id) / COUNT(DISTINCT s.user_id), 1) AS ctr
FROM email_sends s
LEFT JOIN email_opens o ON o.campaign_id = s.campaign_id AND o.user_id = s.user_id
LEFT JOIN email_clicks c ON c.campaign_id = s.campaign_id AND c.user_id = s.user_id
WHERE s.status = 'delivered'
GROUP BY s.campaign_id;
MCAR, missing completely at random, means missingness is unrelated to anything, like a random sensor glitch; dropping rows is mostly safe. MAR, missing at random, means missingness depends on other observed variables, for example older customers skipping the email field; impute using those variables. MNAR, missing not at random, means it depends on the missing value itself, like high earners hiding income; this biases results and needs careful handling or extra data.
33
Tell me about a time you used data to challenge an assumption.
Situation: our team believed weekend discounts drove most sales. Task: I was preparing the monthly review. Action: I compared discount and non-discount weekends over six months and controlled for paydays and festivals. Discounts moved sales forward from the following week but added little overall. I presented it with one clear chart. Result: management trialled fewer discount weekends, and margin improved without a fall in monthly revenue.
34
What are statistical power and minimum detectable effect (MDE)?
Power is the probability that the test detects a real effect of a given size, commonly set at 80%. The minimum detectable effect is the smallest lift you care about and can reliably detect with your sample size. Smaller MDEs, lower baseline rates and higher power all need more users. If you can only get 10,000 users a week, you may only be able to detect large lifts, which should shape what you test.
35
Organic search traffic fell 40% overnight. How do you investigate?
First check whether it is a tracking problem: did the analytics tag break on some templates, did consent settings change, does server log or backend order data show the same fall? If the drop is real, check the search console for impressions and rankings, recent site releases such as changed URLs, missing redirects, noindex tags or robots.txt changes, and whether a search algorithm update or seasonal pattern matches. Then break the drop down by page group.
36
A retail chain asks you which of its 120 stores it should close. How do you approach it?
Clarify the goal first: profit, cash or market share. Build a store-level dataset with revenue, contribution margin after rent and staff, trend over 24 months, footfall, catchment demographics and cannibalisation with nearby stores. Separate structural losers from stores that are new or temporarily disrupted. Estimate how much revenue would shift to nearby stores or online if a store closes. Present a shortlist with scenarios and risks, not a single answer.
A role-playing dimension is one physical dimension used several times by the same fact table in different roles. The classic case is the date dimension: an orders fact has order date, ship date and delivery date keys, all pointing to the same date table. In SQL you join it three times with different aliases; in Power BI you keep one active relationship and use USERELATIONSHIP or create separate date views for each role.
38
Estimate how many cups of tea are sold in a city of 1 crore people in a day.
State assumptions clearly. Of 1 crore people, assume 70 percent drink tea, so 70 lakh. Say half of their cups are bought outside, at an average of one bought cup per drinker every day for that half, giving about 35 lakh purchased cups, with more at offices and stations. So roughly 30 to 40 lakh cups. Interviewers judge the structure, segmentation and sanity check more than the exact number.
A correlated subquery refers to a column from the outer query, so it is logically evaluated once per outer row. Example: find employees earning above their department average with WHERE salary > (SELECT AVG(salary) FROM emp e2 WHERE e2.dept_id = e1.dept_id). It is readable but can be slow on large tables. The same result is often faster with a window function such as AVG(salary) OVER (PARTITION BY dept_id).
Use np.where for one condition, np.select for several, or df.apply with a function for custom logic.
Tips to crack the AMD Data Analytics interview
Revise digital electronics, computer architecture, Verilog and verification basics for silicon roles, with clear explanations of timing concepts.
For software and driver roles, revise C, C++, operating systems and parallel computing basics, including GPU programming concepts.
Prepare detailed explanations of VLSI or embedded projects, including tools used, problems faced and how you solved them.
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; AMD interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the AMD Data Analytics interview?
Candidates usually go through 4 stages: Online assessment or resume screening; Technical round 1 (digital design or programming); Technical round 2 (domain depth); 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 AMD Data Analytics interview?
This practice set leans on Statistics and Case Studies (6), Power BI (6), Python for Analysis (5), Web & Marketing Analytics (4). Revise those first, then practise explaining one or two of your own projects end to end, because most AMD interviewers ask you to walk through real work.
Are these the exact questions asked at AMD?
No. These are curated practice questions that match the skills and difficulty usually tested in hardware & semiconductors interviews like AMD's. They are not a leaked or verified list of past questions.
Where does AMD hire Data Analytics professionals in India?
AMD has large teams in Bengaluru, Hyderabad. Openings and locations change often, so check the AMD careers page and filter by city and experience.