5 SQL Query Patterns AI Engineering Interviews Test in 2026

SQL shows up in AI engineering fresher interviews not because companies need you to manage databases, but because feature engineering runs on it.

Every production ML pipeline needs clean, structured feature tables. SQL is how most teams build them: scalably, reliably, without re-running Python notebooks every time the schema shifts. The queries that produce those tables are what interviewers test.

The standard AI engineering interview covers three areas: Python, ML theory, and SQL. Most product companies and analytics-heavy IT service tracks run one round for each. The SQL questions are not “what is a foreign key?” or “explain ACID properties.” They are closer to: “Given this orders table, find the top-spending user in each city” or “Compute the rolling 7-day average revenue per user.”

See FACE Prep’s top 30 AI/ML interview questions for freshers for how SQL fits into the full interview sequence alongside ML theory and system design.

Why SQL Appears in AI Engineering Interviews

The reason is straightforward. Feature engineering (the process of transforming raw data into model-ready inputs) is mostly SQL work in production environments. A recommendation model doesn’t receive a raw events log. It receives a pre-computed feature table with columns like days_since_last_purchase, rolling_30day_avg_spend, and category_preference_score. Someone had to write the queries that produce those columns. That someone is often a junior AI engineer.

Interviewers at companies running data-heavy AI products have converged on a consistent question format: give candidates a 2-3 table schema, describe a business problem, and ask them to write the SQL that would extract the required features. The schema almost always resembles an e-commerce or SaaS user-events dataset. Once you can write confidently against that structure, the questions become pattern-recognition exercises.

The Sample Dataset Used Throughout

All five patterns below use the same three-table schema so the examples build on each other.

• orders(order_id, user_id, product_category, order_date, amount)
• users(user_id, signup_date, city, plan_type)
• events(event_id, user_id, event_type, event_date)

These tables are fictional but structurally representative of what interview questions actually use.

The 5 SQL Patterns AI Interviewers Test

Pattern 1: Window Functions

Window functions let you compute a value for each row while keeping the full row context, unlike GROUP BY, which collapses rows into aggregated groups. The PostgreSQL window functions tutorial defines them clearly: “A window function performs a calculation across a set of table rows that are somehow related to the current row.”

The four functions that appear most often in AI engineering interviews are:

• ROW_NUMBER() — assigns a unique sequential integer to each row within a partition
• RANK() — assigns rank with gaps for ties (1, 1, 3 not 1, 1, 2)
• LAG(column, n) — returns the value from n rows before the current row within the partition
• LEAD(column, n) — returns the value from n rows after the current row

Common interview prompt: “Find the top 3 users by total spend in each city.”

WITH user_spend AS (
  SELECT o.user_id, u.city, SUM(o.amount) AS total_spend
  FROM orders o
  JOIN users u ON o.user_id = u.user_id
  GROUP BY o.user_id, u.city
),
ranked AS (
  SELECT user_id, city, total_spend,
    RANK() OVER (PARTITION BY city ORDER BY total_spend DESC) AS city_rank
  FROM user_spend
)
SELECT * FROM ranked WHERE city_rank <= 3;

The key clause is PARTITION BY city ORDER BY total_spend DESC. PARTITION BY resets the rank counter for each city; ORDER BY determines who ranks first. Most candidates who have only practiced GROUP BY stumble here because they try to filter inside the window function call itself. The filter goes in the outer WHERE clause, after the window is computed.

Pattern 2: Aggregations with GROUP BY and HAVING

Aggregations are the most common SQL operation and the one candidates underestimate. Interviewers don’t test whether you know COUNT() and SUM(). They test whether you know when to use HAVING instead of WHERE.

The distinction: WHERE filters rows before grouping; HAVING filters groups after aggregation. In feature-engineering contexts, this matters because you often want “users who placed at least 3 orders” (a group-level condition), not just “orders where the amount is above a threshold” (a row-level condition).

Common interview prompt: “List users who have placed at least 3 orders, with their total spend and average order value, sorted by total spend.”

SELECT
  user_id,
  COUNT(order_id)  AS order_count,
  SUM(amount)      AS total_spend,
  AVG(amount)      AS avg_order_value
FROM orders
GROUP BY user_id
HAVING COUNT(order_id) >= 3
ORDER BY total_spend DESC;

A secondary pattern: grouping on multiple columns. “Revenue by city and product category” requires GROUP BY city, product_category, not two separate GROUP BY queries. Candidates who write two queries joined with UNION get partial credit at best.

Pattern 3: Joins for User-Behaviour Feature Tables

In ML pipelines, joins are how you merge information from separate source tables into a single feature row per entity. The interview question format usually gives you a users table (demographic and account info) and an orders or events table (behavioural data), then asks you to combine them into one flat feature table.

The critical decision is LEFT JOIN versus INNER JOIN. INNER JOIN drops users with no matching orders, silently removing the very users you most need to represent: churned users, inactive users, newly signed-up users. LEFT JOIN keeps all users and produces NULL for their order-side columns. In ML, that NULL is a signal, not a missing value to discard.

Common interview prompt: “Build a feature table with one row per user, including order count and total spend. Users with no orders should have 0 for both fields.”

SELECT
  u.user_id,
  u.city,
  u.plan_type,
  COALESCE(o.order_count, 0)  AS order_count,
  COALESCE(o.total_spend, 0)  AS total_spend
FROM users u
LEFT JOIN (
  SELECT user_id, COUNT(*) AS order_count, SUM(amount) AS total_spend
  FROM orders
  GROUP BY user_id
) o ON u.user_id = o.user_id;

COALESCE(value, 0) converts NULL to 0 so downstream model training doesn’t break on missing values. Interviewers watch whether candidates handle the NULL case or leave it. Handle it.

Pattern 4: CTEs for Readable Data Transformation

A Common Table Expression (CTE) is a named temporary result set defined with a WITH clause. CTEs don’t change what SQL can compute. Any CTE can be rewritten as a nested subquery. What CTEs change is readability, and in 2026 interviews, readability is evaluated.

Interviewers at data-focused companies have shifted toward assessing code clarity because production SQL that no one can maintain is a liability. A candidate who writes a deeply nested subquery and produces the correct answer scores below a candidate who structures the same logic as two chained CTEs with descriptive names.

Common interview prompt: “Find the average number of days between signup and first order. Also count users who placed their first order within 7 days of signup.”

WITH first_order AS (
  SELECT user_id, MIN(order_date) AS first_order_date
  FROM orders
  GROUP BY user_id
),
user_activation AS (
  SELECT
    u.user_id,
    u.signup_date,
    f.first_order_date,
    DATEDIFF(f.first_order_date, u.signup_date) AS days_to_first_order
  FROM users u
  JOIN first_order f ON u.user_id = f.user_id
)
SELECT
  AVG(days_to_first_order)                                AS avg_activation_days,
  COUNT(CASE WHEN days_to_first_order <= 7 THEN 1 END)   AS activated_within_week
FROM user_activation;

The two CTEs decompose the problem into named steps: first isolate each user’s earliest order date, then calculate activation time. The outer SELECT reads like a summary of what was computed. That decomposition is what interviewers mean when they say “walk me through your approach.”

For more on how to narrate your technical approach during an AI interview, see the guide on how to answer walk-me-through-your-AI-project questions.

Pattern 5: Rolling Windows and Time-Series Features

Rolling features are the clearest bridge between SQL and ML. A model that predicts whether a user will churn doesn’t train on raw order rows. It trains on derived features: how much did this user spend in the last 7 days? Is their recent spend trending up or down? What was the gap since their previous order?

LAG() and rolling aggregations compute all of these without leaving SQL.

Common interview prompt: “For each order, compute the user’s previous order amount and their rolling 7-day average spend.”

SELECT
  user_id,
  order_date,
  amount,
  LAG(amount) OVER (PARTITION BY user_id ORDER BY order_date)  AS prev_order_amount,
  AVG(amount) OVER (
    PARTITION BY user_id
    ORDER BY order_date
    ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
  ) AS rolling_7day_avg
FROM orders;

ROWS BETWEEN 6 PRECEDING AND CURRENT ROW gives a 7-row window (the current row plus 6 preceding rows). LAG(amount) with no second argument defaults to 1 row back. Both functions are partitioned by user_id so the window resets for each user, which is the correct behaviour for per-user feature engineering.

This pattern directly mirrors what pandas .shift() and .rolling() compute in Python. If you’ve practiced those functions (see pandas and NumPy interview questions for AI/ML freshers), the SQL counterpart will feel familiar.

How to Approach a SQL Problem in an AI Interview

Four habits that separate candidates who get offers from those who get “almost”:

• State the approach before writing. Say: “I’ll aggregate orders per user first, then join back to the users table, then apply a window function.” Interviewers want to follow your reasoning, not reverse-engineer it from the final query.
• Use CTEs from the start. Don’t write a working nested-subquery version and then “clean it up.” CTEs from line one signals that you think in steps, not workarounds.
• Name columns explicitly. COUNT(*) AS order_count not just COUNT(*). Unnamed aggregation columns look like incomplete work.
• Test the NULL edge case out loud. “If a user has no orders, the LEFT JOIN will produce NULL for the order_count. I’ll COALESCE that to 0 because a model sees a missing value and a zero-order count differently.” Even if you don’t write it, naming the edge case shows ML awareness.

The Mode SQL tutorial is a solid free resource if you want more structured practice before your interview. The advanced window functions section covers most of what AI interviews test.

For the data infrastructure context that sits above these SQL patterns (how feature tables connect to model training pipelines and serving systems), the ML system design interview guide for freshers covers that layer in depth.

SQL as One Layer of the AI Engineer’s Stack

SQL competency is necessary but not the full picture. The feature tables you build with window functions and rolling aggregations eventually feed models, APIs, and inference pipelines that span Python, vector stores, and LLM calls. The SQL round establishes that you can handle structured data rigorously. The other rounds establish what you do with that data once it’s clean.

If you’re mapping out how SQL fits into the broader AI engineering skill set, where it lives relative to Python, ML theory, and system design, FACE Prep’s 2026 AI roadmap for Indian engineering students lays out the full sequence, with timelines calibrated to the placement window.

The rolling-feature queries in Pattern 5 describe exactly the kind of preprocessing pipeline that feeds a churn-prediction or recommendation model. Writing the SQL is step one. The next step is connecting that output to an LLM call: retrieving the feature summary as context, generating a natural-language explanation of the model’s output, or building an agent that queries the feature store on demand. TinkerLLM is where you build that second step: ₹299 puts real LLM API calls in your hands, and the resulting micro-project (SQL-prepared context feeding an LLM) is what you show interviewers when they ask what you’ve actually shipped.