The answer is Query 1 will scan less data than Query 2 because it leverages both partition pruning and clustering together. Partition pruning eliminates entire partitions that do not match the `event_date` filter, while clustering physically co-locates rows with the same `user_id` within that partition, allowing the query to skip over non-matching rows entirely. Query 2, by contrast, only prunes partitions on `event_date` but must scan every row in the remaining partition because it lacks a clustering filter. On the Google Professional Cloud Database Engineer exam, this scenario tests your understanding of how BigQuery’s storage and query engine combine these two features for maximum efficiency—a common trap is assuming partition pruning alone is sufficient for row-level filtering. Remember the memory tip: “Prune the partitions, cluster the rows” to recall that clustering refines the scan within a partition, making the combination far more performant than either alone.
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.
Exhibit
Refer to the exhibit.
```sql
CREATE TABLE `myproject.mydataset.sales` (
sale_id INT64,
product_id INT64,
quantity INT64,
price FLOAT64,
sale_date DATE
)
PARTITION BY sale_date
CLUSTER BY product_id
OPTIONS (
partition_expiration_days = 90
);
-- Query 1:
SELECT product_id, SUM(quantity * price) AS total_revenue
FROM `myproject.mydataset.sales`
WHERE sale_date BETWEEN '2024-01-01' AND '2024-01-31'
AND product_id = 12345
GROUP BY product_id;
-- Query 2:
SELECT sale_date, SUM(quantity) AS total_units
FROM `myproject.mydataset.sales`
WHERE sale_date > '2024-06-01'
GROUP BY sale_date;
```
Refer to the exhibit. Given the table definition and two queries, which statement about query performance is correct?
Refer to the exhibit.
```sql
CREATE TABLE `myproject.mydataset.sales` (
sale_id INT64,
product_id INT64,
quantity INT64,
price FLOAT64,
sale_date DATE
)
PARTITION BY sale_date
CLUSTER BY product_id
OPTIONS (
partition_expiration_days = 90
);
-- Query 1:
SELECT product_id, SUM(quantity * price) AS total_revenue
FROM `myproject.mydataset.sales`
WHERE sale_date BETWEEN '2024-01-01' AND '2024-01-31'
AND product_id = 12345
GROUP BY product_id;
-- Query 2:
SELECT sale_date, SUM(quantity) AS total_units
FROM `myproject.mydataset.sales`
WHERE sale_date > '2024-06-01'
GROUP BY sale_date;
```
A
Query 1 will scan less data than Query 2 because it uses both partition pruning and clustering.
Query 1 filters on partition column and cluster column, enabling both pruning and block elimination.
B
Query 2 will scan less data than Query 1 because it only needs to read one partition.
Why wrong: Query 2 filters on a larger date range (after June 2024) and does not use clustering, so it scans more data.
C
Query 1 will scan the same amount of data as Query 2 because both use partition pruning.
Why wrong: Query 1 uses clustering in addition to partitioning, reducing scan further than Query 2.
D
Both queries will perform a full table scan because the table is partitioned.
Why wrong: Partitioned tables allow partition pruning; a full scan is avoided when filtering on partition column.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Query 1 will scan less data than Query 2 because it uses both partition pruning and clustering.
Query 1 uses both partition pruning (filtering on the partition key `event_date`) and clustering (filtering on the clustering column `user_id`), allowing it to skip irrelevant partitions and scan only the specific rows within the target partition. Query 2 uses only partition pruning on `event_date` but lacks a clustering filter, so it must scan all rows in the partition. Therefore, Query 1 scans less data than Query 2.
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.
✓
Query 1 will scan less data than Query 2 because it uses both partition pruning and clustering.
Why this is correct
Query 1 filters on partition column and cluster column, enabling both pruning and block elimination.
Related concept
Read the scenario before looking for a memorised answer.
✗
Query 2 will scan less data than Query 1 because it only needs to read one partition.
Why it's wrong here
Query 2 filters on a larger date range (after June 2024) and does not use clustering, so it scans more data.
✗
Query 1 will scan the same amount of data as Query 2 because both use partition pruning.
Why it's wrong here
Query 1 uses clustering in addition to partitioning, reducing scan further than Query 2.
✗
Both queries will perform a full table scan because the table is partitioned.
Why it's wrong here
Partitioned tables allow partition pruning; a full scan is avoided when filtering on partition column.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that partition pruning alone is sufficient for optimal performance, ignoring that clustering further reduces data scanned within a partition when filters on clustering columns are present.
Detailed technical explanation
How to think about this question
In BigQuery (the typical PCDE context), partition pruning eliminates entire partitions based on a filter on the partitioning column, while clustering sorts data within partitions, enabling block-level pruning when filters on clustering columns are applied. This means Query 1 can skip blocks that don't contain the target `user_id`, reducing the number of bytes read, whereas Query 2 must read all blocks in the partition. In real-world scenarios, combining partitioning and clustering on high-cardinality columns like `user_id` can dramatically reduce query costs and latency.
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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Query 1 will scan less data than Query 2 because it uses both partition pruning and clustering. — Query 1 uses both partition pruning (filtering on the partition key `event_date`) and clustering (filtering on the clustering column `user_id`), allowing it to skip irrelevant partitions and scan only the specific rows within the target partition. Query 2 uses only partition pruning on `event_date` but lacks a clustering filter, so it must scan all rows in the partition. Therefore, Query 1 scans less data than Query 2.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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