GCP-ACE - Planning and implementing a cloud solution (30% of the exam) - Section 2.2
Plan and implement storage and data solutions, choosing and deploying data products such as Cloud SQL, BigQuery, Firestore, Spanner, Bigtable, and AlloyDB, choosing storage products and Cloud Storage classes, loading data, and maintaining multi-region redundancy.
Select a data product by its access pattern, consistency, and scale needs, distinguishing Cloud SQL and AlloyDB for regional relational workloads, Spanner for global relational scale, Bigtable for high-throughput wide-column data, Firestore for documents, and BigQuery for analytics. Choose a storage product and Cloud Storage class (Standard, Nearline, Coldline, Archive) by access frequency and retention, load data with the appropriate tool, and design multi-region redundancy for durability.
Cloud SQLSpannerBigtableFirestoreCloud Storage classesStorage Transfer Service
Practice question for this objective
lock_openFree samplePlanning and implementing a cloud solutionhard
A media company keeps rendered video masters that are read back roughly once a month for re-editing, and it wants the lowest-cost Cloud Storage class that still suits that access pattern without incurring an unusually long minimum storage commitment. Which storage class fits this workload?
AStandard storage, because it has no minimum storage duration and imposes no retrieval fees, which suits any object that might be touched during a given month.
BColdline storage, because monthly reads are infrequent enough that its 90-day minimum storage duration and lower price make it the cheapest safe choice here.
CArchive storage, because rendered masters are long-lived and its 365-day minimum storage duration delivers the lowest at-rest price for retained content.
DNearline storage, because it targets data accessed less than once a month while keeping a 30-day minimum storage duration and modest retrieval costs.check_circle Correct
Match a Cloud Storage class to an access frequency by pairing monthly reads with Nearline and its 30-day minimum storage duration. Cloud Storage classes are priced against access frequency and each carries a minimum storage duration; Nearline is tuned for data read about once a month with a 30-day minimum, so it is cheaper than Standard yet avoids the early-deletion penalties of Coldline or Archive on a monthly cycle.
Why A is wrong: Standard is designed for frequently accessed or hot data and carries the highest at-rest price, so paying for it when objects are read only monthly wastes money even though its access terms are permissive.
Why B is wrong: Coldline suits data touched roughly once a quarter, and its 90-day minimum duration would penalise objects re-edited every 30 days with early-deletion charges, so it is a poor fit for a monthly cycle.
Why C is wrong: Archive targets data accessed less than once a year for backup or preservation, and its 365-day minimum duration plus higher retrieval cost make monthly re-editing expensive despite the low storage price.
Why D is correct: Nearline is built for data read about once a month or less, and its 30-day minimum duration aligns with a monthly re-edit cycle while giving a lower at-rest price than Standard.
Exam traps in Planning and implementing a cloud solution
Answers that look right on this material and are not. Each one is a distractor from a different question in the GCP-ACE bank for this domain.
Cloud SQL, because a read replica in each region gives the relational schema global horizontal write scaling with strong consistency.
errorWhy it is wrong: Cloud SQL is genuinely relational, which makes it tempting, but it scales vertically within one primary region and its replicas serve reads only, so it cannot spread writes globally without manual sharding.
Run Cloud SQL for PostgreSQL in a regional high-availability pair with cross-region read replicas for reach.
errorWhy it is wrong: This suits the team's PostgreSQL background, but Cloud SQL writes still funnel through one primary in one region, so it cannot scale writes horizontally or offer external consistency across continents.
Cloud SQL for PostgreSQL, because its managed PostgreSQL adds a built-in columnar engine that accelerates analytical queries on operational data.
errorWhy it is wrong: Cloud SQL for PostgreSQL is regional and PostgreSQL-compatible, which makes it tempting, but it lacks the in-memory columnar acceleration that speeds analytical queries on the same data.