- A
SELECT customer_id, (SELECT order_date FROM orders ORDER BY order_date LIMIT 1) AS first_purchase, ...
Why wrong: Correlated subquery with LIMIT is inefficient.
- B
SELECT o1.customer_id, o1.order_date AS first_purchase, o2.order_date AS last_purchase FROM orders o1 JOIN orders o2 ON o1.customer_id = o2.customer_id
Why wrong: Self-join is inefficient and may produce incorrect results.
- C
SELECT customer_id, order_date AS first_purchase, ... FROM (SELECT *, ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) AS rn) WHERE rn = 1
Why wrong: Requires a window function and subquery, more complex than necessary.
- D
SELECT customer_id, MIN(order_date) AS first_purchase, MAX(order_date) AS last_purchase FROM orders GROUP BY customer_id
Simple and efficient aggregation.
Quick Answer
The answer is to use SELECT customer_id, MIN(order_date) AS first_purchase, MAX(order_date) AS last_purchase FROM orders GROUP BY customer_id. This approach is correct because it leverages BigQuery’s native aggregate functions to compute the first and last value per group efficiently, avoiding costly self-joins or window functions that would scan the table multiple times. On the Google Professional Cloud Database Engineer exam, this question tests your understanding of fundamental SQL aggregation and query optimization—a core skill for designing performant data pipelines. A common trap is overcomplicating the solution with subqueries or analytic functions like FIRST_VALUE, which are less efficient for simple min/max operations. Remember the memory tip: “MIN and MAX are your first and last friends for grouped dates.”
PCDE Practice Question: Define data structures and implement SQL for Business Intelligence
This PCDE practice question tests your understanding of define data structures and implement sql for business intelligence. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A marketing team needs to analyze customer behavior using BigQuery. They want to create a table that stores the first and last purchase date for each customer from the `orders` table. Which SQL approach should they use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"first"Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
SELECT customer_id, MIN(order_date) AS first_purchase, MAX(order_date) AS last_purchase FROM orders GROUP BY customer_id
Option D is correct because it uses aggregate functions MIN() and MAX() with GROUP BY customer_id to directly compute the first and last purchase dates from the orders table. This is the most efficient and idiomatic SQL approach in BigQuery, leveraging the database engine's built-in aggregation to avoid self-joins or subqueries.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SELECT customer_id, (SELECT order_date FROM orders ORDER BY order_date LIMIT 1) AS first_purchase, ...
Why it's wrong here
Correlated subquery with LIMIT is inefficient.
- ✗
SELECT o1.customer_id, o1.order_date AS first_purchase, o2.order_date AS last_purchase FROM orders o1 JOIN orders o2 ON o1.customer_id = o2.customer_id
Why it's wrong here
Self-join is inefficient and may produce incorrect results.
- ✗
SELECT customer_id, order_date AS first_purchase, ... FROM (SELECT *, ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY order_date) AS rn) WHERE rn = 1
Why it's wrong here
Requires a window function and subquery, more complex than necessary.
- ✓
SELECT customer_id, MIN(order_date) AS first_purchase, MAX(order_date) AS last_purchase FROM orders GROUP BY customer_id
Why this is correct
Simple and efficient aggregation.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that window functions or self-joins are necessary for per-group min/max calculations, when in fact simple aggregation with GROUP BY is the correct and efficient solution.
Detailed technical explanation
How to think about this question
In BigQuery, MIN() and MAX() on a DATE column are O(n) operations that scan the grouped rows once, making them far more efficient than self-joins or window functions for this use case. Under the hood, BigQuery's columnar storage and vectorized execution allow these aggregates to be computed in parallel across shards. A real-world scenario is a customer 360 dashboard where you need to compute recency and first purchase metrics from a large orders table (billions of rows) — using GROUP BY with MIN/MAX avoids expensive shuffles and join explosions.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PCDE question test?
Define data structures and implement SQL for Business Intelligence — This question tests Define data structures and implement SQL for Business Intelligence — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: SELECT customer_id, MIN(order_date) AS first_purchase, MAX(order_date) AS last_purchase FROM orders GROUP BY customer_id — Option D is correct because it uses aggregate functions MIN() and MAX() with GROUP BY customer_id to directly compute the first and last purchase dates from the orders table. This is the most efficient and idiomatic SQL approach in BigQuery, leveraging the database engine's built-in aggregation to avoid self-joins or subqueries.
What should I do if I get this PCDE question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on PCDE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A BI team needs to analyze user behavior with sessionization. Each event has a timestamp and session ID. The table 'sessions' contains columns: session_id, user_id, event_time, event_name. The team wants the first event time per session. Which query is most efficient?
hard- A.SELECT session_id, ARRAY_AGG(event_time ORDER BY event_time LIMIT 1) FROM sessions GROUP BY session_id
- B.SELECT a.session_id, a.event_time FROM sessions a INNER JOIN (SELECT session_id, MIN(event_time) min_ts FROM sessions GROUP BY session_id) b ON a.session_id = b.session_id AND a.event_time = b.min_ts
- C.SELECT session_id, MIN(event_time) FROM sessions GROUP BY session_id
- ✓ D.SELECT session_id, event_time FROM sessions QUALIFY ROW_NUMBER() OVER (PARTITION BY session_id ORDER BY event_time) = 1
Why D: Option D is correct because it uses the QUALIFY clause with ROW_NUMBER() to filter directly within the window function, avoiding a self-join or subquery. This approach is efficient in Snowflake and similar platforms, as it processes the window function once and then filters to the first event per session without materializing intermediate results.
Last reviewed: Jun 30, 2026
This PCDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PCDE exam.
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