Tech Interview Statistics 2026: What Gets Asked Most

We looked at 7,589 interview questions on PracHub, reported by candidates from 50+ companies, to see what tech interviews actually test.

Question distribution by category

Category	Count	Percentage
Coding & Algorithms	2,470	33%
Behavioral & Leadership	1,010	13%
Analytics & Experimentation	905	12%
System Design	853	11%
Data Manipulation (SQL/Python)	649	9%
Machine Learning	583	8%
Statistics & Math	485	6%
Software Engineering Fundamentals	268	4%
ML System Design	225	3%
Product / Decision Making	82	1%
Product Design & Strategy	22	<1%

Coding is the single largest category, but it is only a third of the total. The other two-thirds — behavioral, system design, SQL, ML, analytics, statistics — require completely different preparation.

Which companies ask the most questions

Meta leads in sheer volume. Their interview process is thorough and they hire at enormous scale.

Top companies by reported questions:

Meta: 1,071 questions (14%)
Amazon: 466 questions (6%)
Google: 356 questions (5%)
Microsoft: 80+ questions
Apple: 42+ questions

Meta's dominance is partly because they test across more categories than most companies. A Meta interview loop might include coding, SQL, product sense, behavioral, and system design.

FAANG question mix

Different companies weight different categories:

Meta — Analytics heavy. 273 analytics questions, 268 coding, 166 SQL, 126 behavioral, 94 system design. If you are interviewing at Meta, SQL and analytics prep matters as much as coding.

Amazon — Behavioral heavy. 160 coding, 122 behavioral, 71 ML, 65 SQL, 48 system design. Amazon is the only FAANG company where behavioral questions are 26% of the total.

Google — Coding heavy. 152 coding, 66 behavioral, 38 analytics, 36 ML, 32 system design, 32 statistics. Google's coding bar is the highest among FAANG.

Emerging trends in 2026

AI companies are growing fast. Anthropic, OpenAI, xAI, and other AI labs are now among the most active sources of new questions on PracHub. Their interviews tend to be harder on average, with more emphasis on ML fundamentals and system design for ML infrastructure.

LLM-related questions are new. Questions about transformers, attention mechanisms, fine-tuning, RLHF, and LLM serving infrastructure are showing up in ML interviews at companies that would not have asked them two years ago.

System design is getting more specific. Instead of "design Twitter," companies are asking about real-time ML serving, event-driven architectures, and data pipeline design. The questions assume more baseline knowledge than they used to.

Behavioral interviews are expanding beyond Amazon. More companies are adding structured behavioral rounds, partly influenced by Amazon's success with Leadership Principles. Google's "Googleyness" round has gotten more rigorous.

What this means for preparation

Do not over-index on coding. It is the largest category at 33%, but spending 80% of your prep time on algorithms means you are underprepared for 67% of the interview.
Match your prep to your target company. Amazon behavioral prep is different from Google coding prep. Look at the question distribution for your target company and allocate study time accordingly.
SQL is undertested relative to its importance. Only 9% of total questions, but SQL shows up in almost every DS, DE, and analytics interview. It is a gatekeeper round — fail it and you do not get to the onsite.
ML system design is a growing category. At 225 questions and climbing, this is where experienced ML engineers differentiate themselves.

All 7,589 questions are searchable on PracHub by company, role, category, and difficulty. You can see the exact breakdown for any company or role combination.