Google PCA Manage implementation of cloud architecture Practice Question
Which THREE actions can help reduce costs for a BigQuery workload that runs frequent, ad-hoc analytical queries on a large dataset?
⚠ Common exam trap
Google Cloud often tests the distinction between cost-reduction techniques that reduce bytes scanned (partitioning, clustering, materialized views) versus pricing model choices (flat-rate vs. on-demand), leading candidates to mistakenly select flat-rate pricing as a cost-saving action for ad-hoc queries.
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
✓
Partition the table by a date or timestamp column.
Option B is correct because partitioning the table by a date or timestamp column lets BigQuery prune irrelevant partitions, so ad-hoc queries that filter on that column scan far less data and incur lower on-demand query costs. Option C is correct because materialized views precompute and cache the results of common aggregation queries, so repeated ad-hoc aggregations read the much smaller materialized view instead of rescanning the full large dataset. Option D is correct because clustering on columns frequently used in filter clauses co-locates related data in storage blocks, allowing BigQuery to skip blocks that don't match the filter and further reduce bytes scanned. Option A is not correct because automatic schema detection only simplifies loading data and has no effect on query cost. Option E is not correct because flat-rate pricing with reserved slots provides predictable capacity billing rather than reducing the cost of a sporadic ad-hoc query workload, which is typically cheaper on on-demand pricing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable automatic schema detection to avoid manual schema definition.
Why it's wrong here
Automatic schema detection only infers column names and types when loading data; it does not reduce bytes scanned or storage billed. Cost reduction comes from partitioning, clustering and query pruning, not from avoiding manual schema definition.
- ✓
Partition the table by a date or timestamp column.
Why this is correct
Partitioning by date or timestamp prunes scanned data, so ad-hoc analytical queries read only relevant partitions rather than the full table. This directly reduces bytes processed, and BigQuery bills on-demand queries by data scanned, satisfying the cost-reduction requirement for frequent large-dataset analysis.
- ✓
Create materialized views for common aggregation queries.
Why this is correct
Materialised views precompute and persistently store aggregation results, so repeated ad-hoc queries scan the small view rather than the full large dataset. BigQuery bills on bytes processed, so this directly cuts query cost for the frequent common aggregations described in the stem. Automatic refresh keeps results current without manual intervention.
- ✓
Use clustering on columns frequently used in filter clauses.
Why this is correct
Clustering physically sorts data by the chosen columns, so filters on those columns skip irrelevant blocks. Queries scan fewer bytes, and BigQuery bills on-demand by bytes processed, directly reducing cost for frequently filtered ad-hoc workloads.
- ✗
Use flat-rate pricing with reserved slots.
Why it's wrong here
Reserved slots suit steady, predictable query volumes where committed capacity is cheaper than on-demand; ad-hoc bursts leave slots idle, so you pay for unused capacity. On-demand pricing bills per byte scanned, matching sporadic analytical queries.
Go deeper
Related to this question
Learn chapter
Cloud Storage: Objects and Buckets
Key term
Schema
A schema is a blueprint or logical structure that defines how data is organized, stored, and accessed in a database or information system.
Key term
Table
A table is a structured collection of data organized into rows and columns, used in databases and spreadsheets to store and manage information efficiently.
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This PCA 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 PCA exam.