hardMultiple ChoiceObjective-mapped
Cloud Digital Leader Practice Question: A data analytics company uses BigQuery for…
A data analytics company uses BigQuery for large-scale queries. They notice that some queries are very expensive due to scanning large amounts of data. They want to reduce costs without changing query logic. Which feature should they use?
⚠ Common exam trap
Google Cloud often tests the misconception that cost reduction must come from changing pricing models (like flat-rate) rather than from data organization techniques that reduce the actual amount of data processed.
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
✓
Partitioning and clustering tables
Partitioning and clustering tables in BigQuery physically organize data into smaller, manageable segments based on specified columns (e.g., date or timestamp). This allows queries to use partition pruning and clustering-based block pruning to scan only the relevant data, drastically reducing the amount of data processed and thus lowering costs without altering the query logic.
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 caching
Why it's wrong here
Query caching stores results of previously executed queries for a temporary period (typically ~24 hours), allowing identical repeated queries to skip computation and partially reduce costs. However, it does not reduce the cost of the initial scan or the bytes processed for new or different queries, so it fails to address the primary driver of large-scale query expenses. Thus, while useful for repetitive access patterns, it is not a cost-reduction strategy for general analytics workloads.
- ✓
Partitioning and clustering tables
Why this is correct
Partitioning divides a table into segments based on a column (e.g., date), enabling BigQuery to prune entire partitions before scanning, which drastically reduces the physical bytes processed. Clustering sorts data within each partition based on clustered columns, allowing even finer-grained pruning and better compression, further lowering the scan footprint. Together these directly minimize the bytes billed per query, making them the most effective way to cut costs for large-scale, predictable query patterns.
- ✗
Authorized views
Why it's wrong here
Authorized views are a security feature that lets you share query results with users or groups without granting direct access to the underlying tables, enforcing row- and column-level access controls. However, they do not alter how BigQuery executes the query; the underlying table is still fully scanned (unless other optimizations exist), so the bytes processed and associated costs remain unchanged. Therefore, authorized views address data governance, not cost reduction.
- ✗
Flat-rate pricing with reservations
Why it's wrong here
Flat-rate pricing with reservations provides a fixed monthly cost for a committed amount of BigQuery slot capacity (CPU, memory), decoupling billing from per-query byte scanning and offering predictable expenses. Nevertheless, it does not reduce the amount of data scanned or the slot resources consumed; your queries still process the same byte volume, and inefficient scans can still require more concurrent slots or cause performance bottlenecks. Consequently, reservations only stabilize costs, not reduce the underlying workload's efficiency.
Go deeper
Related to this question
Learn chapter
BigQuery and Data Analytics
Key term
Clustering
Clustering is a technique where multiple servers work together as a single system to keep applications running even if one server fails.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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