To succeed, you must focus your preparation on the specific skills AMD values most for this role. Based on recent candidate experiences, the evaluation is heavily weighted toward practical application rather than theoretical computer science.
Excel and Data Manipulation
This is the most frequently cited technical evaluation area for Data Analyst roles at AMD. Do not underestimate the depth of Excel knowledge required. You will likely face specific questions about functions and features, and you may be asked to verbally walk through how you would solve a data problem using these tools.
Be ready to go over:
- Lookup Functions – Deep understanding of
VLOOKUP, XLOOKUP, and HLOOKUP. Know the limitations of each and when to use INDEX/MATCH instead.
- Data Cleaning & Formatting – Techniques for removing duplicates, handling conditional formatting, and standardizing messy datasets.
- Pivot Tables & Reporting – Creating dynamic summaries to answer business questions quickly.
- Advanced concepts – Macros/VBA (less common but valuable) and Power Query for automating data prep.
Example questions or scenarios:
- "What is the difference between XLOOKUP and VLOOKUP, and why would you use one over the other?"
- "How would you use conditional formatting to highlight trends in a sales dataset?"
- "Explain how you would merge two datasets with different formatting."
Behavioral and Past Experience
AMD wants to know how you work. The "Behavioral" portion often takes up a significant chunk of the interview (sometimes the first 30 minutes). They are looking for evidence of ownership, learning agility, and the ability to work in a team.
Be ready to go over:
- Project Ownership – specific examples of end-to-end projects where you identified a problem and delivered a solution.
- Conflict Resolution – Times when you disagreed with a stakeholder or had to deliver bad news based on data.
- Adaptability – Examples of how you handled a sudden change in project scope or timeline.
Example questions or scenarios:
- "Tell me about a time you had to explain a complex technical finding to a non-technical manager."
- "Describe a situation where you made a mistake in your analysis. How did you handle it?"
- "Walk me through your resume and highlight your most impactful project."
Domain Specifics (Finance & AI)
Depending on the department hiring, you may face questions related to the specific subject matter of the team. For Finance roles, expect questions on forecasting and variance analysis. for AI/Product teams, expect questions on basic AI concepts or product metrics.
Be ready to go over:
- Financial Literacy – Revenue, COGS, margin analysis, and basic accounting principles.
- AI/ML Basics – Understanding high-level concepts of how data feeds into AI models (if applying to an AI-adjacent team).
- Business Logic – How to interpret data in the context of market trends.
Example questions or scenarios:
- "How would you forecast revenue for the next quarter given historical data?"
- "Explain a basic AI concept to a layperson."
- "What financial metrics would you track to measure the success of a new product launch?"