If you’ve ever sat across from an interviewer, heart thumping, waiting for the SQL question to pop up… you’ll know the feeling. I’ve been there. And, if I’m honest, I’ve watched candidates—brilliant ones—trip up on surprisingly basic things. In this blog post I want to walk you through why most candidates fail SQL concepts in data interviews, what those concepts are, and how you can steer clear of those pitfalls.
Think of it like this: you prepare, you study, you’re confident — yet you still stumble. Why? Often not because you don’t know SQL, but because you don’t understand the nuance. And I’m going to talk through those moments of nuance, the “ah-crap” moments that happen under pressure.
I will share anecdotes, conjecture, stories, and yes, a few mistakes I have made. Because it’s easier to avoid making the same mistakes again after you’ve already made one.
Why these mistakes keep happening
First, let’s step back and ask: why do so many candidates trip on SQL in interviews?
- Because the setting for the interview is different. Even though you may know how to write a query at your desk job, it’s different when you have to explain your reasoning and clarify the question while under time pressure. “Jumping into code without asking clarifying questions” is a serious mistake, according to one author. the data hustle.
- Because SQL is more than just syntax. It makes sense. It’s crucial to understand what SELECT, JOIN, WHERE, and GROUP BY do, but what really irritates people is when they act strangely and why they do it. Misuse of UNION vs. UNION ALL is one example.
- Because a lot of tutorials treat SQL as “write query, get results,” but an interview frequently tests performance, nulls, edge cases, clarity of thought, and unclear instructions. “Ask questions if you don’t understand the requirements,” says one Reddit thread.
- Due to pressure. You’re running behind schedule. Your mind is racing. You neglect to check your outcome. Writing example rows is omitted. You think your reasoning makes sense. At that point, minor errors become major ones.
So yes — knowing SQL is one thing. Being ready under interview conditions is another. And the difference is often these concepts.
The key SQL concepts that trip up candidates
Let’s discuss some of the SQL concepts that many candidates either misunderstand or take for granted in a more conversational manner, and why that’s an issue.
Mis-understanding JOINs, and jumping into them too fast
JOINS: This is where your self-assurance can work against you. I once saw someone write an INNER JOIN right away without considering whether they actually needed an LEFT JOIN or RIGHT JOIN or whether they might inadvertently drop rows. They froze when they asked, “But what if there are records in A with no match in B?”
The interviewer preferred logic over code. According to one article, “Getting your JOINs backwards” is a common mistake.
Here’s how this plays out:
- The question: “List customers who have made purchases but include those who have never made a purchase yet flagged as inactive,”
- Candidate writes:
SELECT c.customer_id, p.purchase_date FROM customers c JOIN purchases p ON c.customer_id = p.customer_id WHERE c.status = 'inactive'; - They lose inactive customers who haven’t made any purchases because JOIN got rid of them. It would require an LEFT JOIN.
- However, the candidate didn’t pause to consider the possibility that p might not have any rows for c.
- The interviewer is also wondering if you are able to reason. Are you able to ask clarifying questions? Are you able to identify edge cases?
Thus, the first habit is to pause. Put sample rows in writing. What would happen if one side was absent? Sketch the joining.
Ignoring NULLs and three-valued logic
Here’s one that catches a surprising number of people. They treat NULL like they treat “0” or “empty string” or “not applicable”— and that’s wrong. The logic in SQL is three-valued (TRUE / FALSE / UNKNOWN) when NULLs are in play.
For example:
SELECT * FROM orders
WHERE discount_amount <> 0;
This won’t return rows where discount_amount is NULL. Because NULL <> 0 evaluates to UNKNOWN, and WHERE only keeps TRUE. If you didn’t consider that, you might miss data.
In interview mode: if you’re asked “Show customers with no login in last 90 days” and you skip thinking about last_login_date = NULL, you’ll mess up.
One interview candidate said:
“Not recognizing that a filter such as Name <> “John” means Name also cannot be null…” Reddit
So always: ask yourself about NULLs. Think: what happens if a column is NULL? Use IS NULL / COALESCE() or handle appropriately.
Mis-using aggregate vs window functions or GROUP BY vs subquery
Another common error: you are aware of GROUP BY, SUM, and COUNT, but you fail to consider which tool is actually appropriate for this problem. When aggregation is necessary, many candidates abuse window functions or dive into nested subqueries rather than the more straightforward GROUP BY + HAVING. According to one article, it’s a major red flag to misuse UNION/UNION ALL, confuse WHERE with HAVING, or use GROUP BY carelessly.
For example:
- Question: “Find the second highest salary per department.”
Candidate may write a complex correlated subquery, when a ROW_NUMBER() window function partitioned by department might be clearer. - Or: they forget that
HAVINGworks afterGROUP BY, so filtering aggregated results there instead ofWHERE, or vice versa.
Habit #3: Consider the shape of the result before writing the query. Am I maintaining detail or am I collapsing rows? Do I require basic aggregation or a window function?
Performance / indexing / SELECT * / understanding underlying data
It’s true that interviewers don’t always ask, “Make the query super fast and index-optimized.” However, they are becoming more concerned with performance, particularly for data roles. According to one article, common errors include using SELECT *, neglecting indexing, selecting subpar column names, etc.
Let me share a tale with you: I was conducting a data role interview. They wrote the right question. Test cases were passed. However, I posed the question, “What would you do if this needed to run every hour and the table had 50 million rows?” They went cold. Regarding partitioning, query planning, and indexes, they had no response. It was scale awareness, not just reasoning.
Key red-flags:
- Instead of specifying columns, use SELECT * to avoid slowing down the process by retrieving unnecessary data.
- Ignoring indexes: “On which column is the filter applied? Is an index necessary?
- Not thinking about sorting: “Do I need a covering index if I use ORDER BY?”
- failing to inquire about or provide clarification on data sizes, anticipated outputs, and latency requirements.
The interviewer might ask, “If scale grows, what changes?” even if your task is a small sample. Additionally, you lose points for being blank.
Rushing, skipping clarifying questions, not telling your thought process
This one is nearly meta-SQL. It’s a process error rather than a syntax error. Many applicants skip the steps of articulating their presumptions, seeking clarifications, and going over sample data in favor of writing code right away, believing they have it. “Jumping into code without asking clarifying questions” is a major issue, according to one blog. the data hustle.
The candidate wrote something and said, “Here’s the query,” during one of my interviews. “What assumptions are you making?” I asked. They paused. They lost because of their hesitation. Interviewers prefer it when you stop, think, and ask:
- “Are there unique keys or duplicate rows?”
- “How about ties for the highest salary?”
- “Which one do I choose if two customers have the same spend?”
- “Should I include customers who aren’t active?”
In fact, it shows maturity to pause for perhaps 20 seconds, think, and then write, “Okay, I’m assuming no duplicate customer_ids unless stated, and I’ll include ties unless told otherwise.”
The best advice is to think aloud. Examine a basic data set. “What happens if table A has three rows and table B has none?” Write brief instances. Next, write code.
Putting it all together — How you can prepare differently
Now you are aware of the typical pitfalls. Let’s talk about what you can do to get ready so you don’t fall into the category of “most candidates” who don’t understand these ideas.
Build mini-scenarios and test edge cases
When practicing queries, don’t just solve “easy” problems. Pick one you solved and ask:
- What happens if some of the values are NULL?
- What happens if there are no rows in table B that match (for join)?
- What happens if there are duplicates?
- What happens if performance counts?
Write some sample rows. Execute your query. Check to see if it selects the right outcome. Then consider a million rows. Which index would be useful?
Practice verbalising assumptions & thought process
Develop the habit of saying, either out loud or silently, as you begin answering a question:
“I assume customers. The customer_id is distinct. I’ll request a date filter. Inactive customers must be included. I still want them even if there are no purchases.
Even if nobody hears you (during practice) it reinforces the habit. Then when you interview you’ll naturally ask clarifications.
Don’t let syntax be the only thing you care about
Yes — correct syntax matters. In an interview, however, the why is more important. Consider “Why this join?” when writing a JOIN. Would I lose rows? What connection exists between the tables? Consider whether you are collapsing rows when you write GROUP BY. What if I require specifics? Consider “What about NULLs?” when writing WHERE. What is excluded by this filter?
Additionally, monitor performance by asking yourself, “Is this scalable? What happens if data increases? Do you use indexes? Am I choosing columns that aren’t needed?
After you write code — validate it
Imagine you finished writing your query. Now stop. Ask:
- Does this yield the expected results for a basic dataset with three rows?
- Have I dealt with ties? Have I dealt with NULLs?
- Did I select the appropriate join type?
- If I used an alias, did I name the new column? Was my order of results correct?
One author emphasises: “The biggest mistake one can make at a SQL interview is not to double-check at the end.” Medium+1
Reflect on past mistakes (your own or others)
I have a little story to share: early in my career, I ran a search to identify “customers who never bought anything.” NOT IN (SELECT…) was the query I used. For my 100-row dataset, it was successful. However, I overlooked this during the interview: the entire NOT IN fails if the subquery returns a NULL because NULL NOT IN() is tricky. I didn’t remember that question. I discovered that when there may be NULLs present, always test for NOT EXISTS rather than NOT IN. I lost my job because of that one mistake. It’s still in my memory.
By reflecting on such past “face-palm” moments you internalise caution.
FAQ
Q1: Do I need to memorise all SQL functions and syntax for interviews?
A: No, not exactly. Functions and syntax are well-known. But knowing when and why to use them is more crucial. Knowing the syntax for LAG() is less impressive to interviewers than being able to explain it: “I’d use LAG() to compare each row with its previous one — and I’d partition by department and order by date.” That logic is flawless.
Q2: What if the company uses a specific SQL dialect (MySQL vs PostgreSQL vs SQL Server)?
A: Good question. It is the same basic relational logic. However, there are differences in some functions, defaults, and performance behaviors. Spend ten to fifteen minutes reviewing date functions, NULL behavior, CTE syntax, and window functions if you are familiar with the dialect you are interviewing for. Don’t worry, though; logic is transferable.
Q3: How deep will the interviewer go on performance / indexing?
A: Depending on the role, yes. They might not place much emphasis on fine-tuning performance for a “data analyst” position. You might apply for a “data engineer” or “database role.” Assume they will ask at least one follow-up question, according to my rule: “Okay, what if the table has 100 million rows? How would you maximize? If you know the answer, excellent. You’ll lose “depth” points if you don’t. Thus, put some basic performance optimization principles into practice: indexes, limiting joins, avoiding SELECT *, comprehending execution order, and avoiding superfluous subqueries.
Q4: Can I still get away with being strong in one area (e.g., joins) even if weak in others?
A: Maybe. However, many applicants have “strong enough” levels of proficiency in a variety of SQL concepts during competitive interviews. You may stand out if you are an expert in one area, but you will still be questioned about the fundamentals. In summary, strengthen your areas of weakness (performance, aggregates, and NULLs) to avoid falling into the “most candidates fail” trap.
Conclusion
Thus, the unsettling reality is that most candidates fail these SQL concepts in data interviews, not because they are stupid or lack study skills, but rather because they frequently underestimate the subtleties, the underlying logic, the “what if?” questions, and the edge cases. Instead of treating SQL like a logic game, they treat it like a syntax check.
When you enter your upcoming interview, keep in mind:
- Pause and seek clarifications.
- Consider edge cases and NULLs.
- Make a deliberate decision to join,
- Choose carefully between aggregation and detail.
- Consider performance, even if only briefly.
- Use sample data to verify your query.
You will immediately distinguish yourself from the others. Additionally, you will stay away from becoming one of those candidates who lose due to minor issues.
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