The answer is that the filter on hire_date is not selective enough to prune most partitions. In BigQuery, partition pruning works by eliminating entire partitions that do not match the filter predicate, but the degree of pruning depends entirely on how restrictive that filter is. A broad range condition, such as filtering on a large span of hire_date values, will match many partitions, forcing the query to scan all the data within them—in this case, 10 GB out of a 100 GB table. On the Google Professional Cloud Database Engineer exam, this concept tests your understanding that partition pruning is not automatic; it requires a selective predicate, ideally an equality or narrow range, to achieve minimal scan size. A common trap is assuming any partition filter guarantees full pruning, when in reality a low-selectivity filter can still leave a large scan footprint. Memory tip: think of “selectivity as a sieve”—a fine mesh (high selectivity) catches only a few partitions, while a wide mesh (low selectivity) lets most through.
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
-- BigQuery query results metadata
Query statement: SELECT department, COUNT(*) as cnt
FROM `project.dataset.employees`
WHERE hire_date >= '2023-01-01'
GROUP BY department
ORDER BY cnt DESC
Query plan:
- Stage 1: Input (scan) - 10 GB processed
- Stage 2: Aggregate - 5 GB processed
- Stage 3: Sort - 0 GB processed
Table details:
- Table size: 100 GB
- Partitioned by: hire_date (daily)
- Clustered by: department
```
The exhibit shows query metadata for a query that scans 10 GB. Given the table is 100 GB and partitioned by hire_date, why did the query scan 10 GB and not less?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The filter on hire_date is not selective enough to prune most partitions
Option A is correct because partition pruning in Databricks (and Spark SQL) depends on the selectivity of the filter predicate. If the filter on `hire_date` matches a large number of partitions (e.g., filtering on a range that covers 10 GB out of 100 GB), the query scans exactly those partitions. The question states the table is 100 GB and partitioned by `hire_date`, so a 10 GB scan implies the filter pruned 90 GB of partitions but was not selective enough to reduce the scan further—e.g., the predicate may be a broad range or lack a precise equality condition.
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.
✓
The filter on hire_date is not selective enough to prune most partitions
Why this is correct
If the date range covers many days, many partitions are scanned.
Related concept
Read the scenario before looking for a memorised answer.
✗
Clustering on department is not being used because the query has ORDER BY
Why it's wrong here
ORDER BY does not disable clustering; clustering is used during aggregate.
✗
The query uses GROUP BY, which forces a full table scan
Why it's wrong here
GROUP BY does not force full scan; partitioning still applies.
✗
The table is not clustered properly
Why it's wrong here
Clustering is present and used; the issue is partition pruning.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that any filter on a partition column automatically prunes to a minimal scan, ignoring that the selectivity of the predicate (e.g., range vs. equality) determines how many partitions are actually skipped.
Detailed technical explanation
How to think about this question
Under the hood, Databricks uses the Hive metastore partition metadata to determine which files to read. When a filter on the partition column is applied, Spark's `FileSourceScanExec` node evaluates the predicate against the partition metadata (e.g., `spark.sql.hive.metastorePartitionPruning=true`). If the filter is a range like `hire_date >= '2023-01-01' AND hire_date < '2023-12-31'`, it may match many partitions, resulting in a 10 GB scan. A real-world scenario is a date-partitioned fact table where analysts often query the last 30 days, but if the filter accidentally includes a full year, the scan remains large.
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: The filter on hire_date is not selective enough to prune most partitions — Option A is correct because partition pruning in Databricks (and Spark SQL) depends on the selectivity of the filter predicate. If the filter on `hire_date` matches a large number of partitions (e.g., filtering on a range that covers 10 GB out of 100 GB), the query scans exactly those partitions. The question states the table is 100 GB and partitioned by `hire_date`, so a 10 GB scan implies the filter pruned 90 GB of partitions but was not selective enough to reduce the scan further—e.g., the predicate may be a broad range or lack a precise equality condition.
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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