The answer is that COUNT(DISTINCT) forces a full scan of all partitions, even with a filter on the partition column. This happens because the SQL engine must verify global uniqueness across the entire dataset—it cannot assume that distinct values are confined to the filtered partitions without scanning every partition, which disables partition pruning. On the Google Professional Cloud Database Engineer exam, this is a classic trap: candidates often expect a filter on the partition key to reduce the scan, but COUNT(DISTINCT) overrides that optimization to guarantee accuracy, especially in engines like BigQuery. The key insight is that distinct aggregation requires a complete view of all data to avoid missing values that might cross partition boundaries. Memory tip: “Distinct demands the whole dataset—partition pruning is paused for precision.”
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
-- Query that scans too many bytes
SELECT event_date, COUNT(DISTINCT user_id) as users
FROM `project.dataset.events`
WHERE event_date >= '2023-01-01'
GROUP BY event_date
-- INFORMATION_SCHEMA result for table `project.dataset.events`:
Size: 500 GB
Partitioned by: event_date (DATE)
Clustered by: user_id
Refer to the exhibit. The query scans 500 GB even though it filters on the partitioning column event_date and only needs data from 30 days. What is the most likely reason?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Refer to the exhibit.
-- Query that scans too many bytes
SELECT event_date, COUNT(DISTINCT user_id) as users
FROM `project.dataset.events`
WHERE event_date >= '2023-01-01'
GROUP BY event_date
-- INFORMATION_SCHEMA result for table `project.dataset.events`:
Size: 500 GB
Partitioned by: event_date (DATE)
Clustered by: user_id
A
COUNT(DISTINCT) often results in full table scan to ensure accuracy, even with partitions.
Distinct aggregations can require scanning all data to ensure correctness.
B
The query lacks a LIMIT clause.
Why wrong: LIMIT does not affect partition pruning.
C
The clustering on user_id is causing a full table scan.
Why wrong: Clustering helps prune blocks, not cause full scan.
D
The table is not actually partitioned by event_date; the filter is on a non-partitioned column.
Why wrong: The metadata shows it is partitioned by event_date.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
COUNT(DISTINCT) often results in full table scan to ensure accuracy, even with partitions.
Option A is correct because COUNT(DISTINCT) in many SQL engines, including those used in data warehousing like Google BigQuery or Snowflake, often requires a full scan of all partitions to ensure global uniqueness. Even with a filter on the partitioning column, the engine cannot guarantee that distinct values are confined to the filtered partitions without scanning all data, especially if the distinct operation spans across partitions or if the engine's optimizer lacks partition pruning for distinct aggregations.
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.
✓
COUNT(DISTINCT) often results in full table scan to ensure accuracy, even with partitions.
Why this is correct
Distinct aggregations can require scanning all data to ensure correctness.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The query lacks a LIMIT clause.
Why it's wrong here
LIMIT does not affect partition pruning.
✗
The clustering on user_id is causing a full table scan.
Why it's wrong here
Clustering helps prune blocks, not cause full scan.
✗
The table is not actually partitioned by event_date; the filter is on a non-partitioned column.
Why it's wrong here
The metadata shows it is partitioned by event_date.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that partition pruning always applies to aggregation functions, but the trap here is that COUNT(DISTINCT) bypasses partition pruning because it requires global deduplication, leading to a full table scan even with a partition filter.
Trap categories for this question
Command / output trap
The metadata shows it is partitioned by event_date.
Detailed technical explanation
How to think about this question
Under the hood, COUNT(DISTINCT) often requires a hyperloglog or similar sketch algorithm to estimate distinct counts, but for exact counts, the engine must shuffle and deduplicate across all partitions. In systems like BigQuery, partition pruning is disabled for COUNT(DISTINCT) over a partitioned column because the distinct values could exist in any partition, leading to a full scan. A real-world scenario is when querying daily user logins over 30 days but needing exact unique users; the engine scans all partitions to ensure no user from outside the date range is missed.
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: COUNT(DISTINCT) often results in full table scan to ensure accuracy, even with partitions. — Option A is correct because COUNT(DISTINCT) in many SQL engines, including those used in data warehousing like Google BigQuery or Snowflake, often requires a full scan of all partitions to ensure global uniqueness. Even with a filter on the partitioning column, the engine cannot guarantee that distinct values are confined to the filtered partitions without scanning all data, especially if the distinct operation spans across partitions or if the engine's optimizer lacks partition pruning for distinct aggregations.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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