hardMultiple ChoiceObjective-mapped
Google ACE Practice Question: A financial services company needs to run…
A financial services company needs to run analytics queries on transaction data that arrives in real-time. The queries must return results within 2 seconds and the dataset grows by ~100 GB per day. The company also needs to retain all data for 7 years for regulatory compliance. Which architecture best satisfies these requirements?
⚠ Common exam trap
Google Cloud often tests the distinction between OLTP (Spanner, Cloud SQL) and OLAP (BigQuery) services, and candidates mistakenly choose Spanner for analytics because of its global scale and strong consistency, overlooking that it is not optimized for large-scale analytical queries with strict latency SLAs.
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
✓
Stream transactions through Pub/Sub → Dataflow → BigQuery; run analytics on BigQuery.
It uses Pub/Sub for real-time ingestion, Dataflow for stream processing, and BigQuery for analytics, which can handle 100 GB/day growth and return queries within 2 seconds using BigQuery's columnar storage and automatic sharding. BigQuery's 7-year retention is supported by its time-based partitioning and long-term storage at reduced cost, meeting regulatory compliance without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write transactions to Cloud Spanner; run analytics queries directly against Spanner.
Why it's wrong here
Cloud Spanner is a horizontally scalable NewSQL database optimized for strongly consistent transactional (OLTP) workloads, but its storage and query engine are not designed for complex, petabyte-scale analytical scans. Running large analytical queries directly against Spanner would contend with live transaction traffic, degrading OLTP performance and driving up cost, since Spanner bills per node capacity rather than by query volume. Furthermore, Spanner's row-oriented storage is less efficient for columnar aggregation than BigQuery's columnar design, so achieving 2-second responses on ad-hoc analytics would be unreliable.
- ✓
Stream transactions through Pub/Sub → Dataflow → BigQuery; run analytics on BigQuery.
Why this is correct
This is the canonical Google Cloud streaming analytics architecture: Pub/Sub ingests transaction streams asynchronously, Dataflow provides exactly-once, auto-scaling transformations (including windowing and enrichment), and BigQuery stores the results in columnar, partitioned tables. BigQuery's Dremel execution engine and optional BI Engine provide sub-second-to-2-second query performance on recent data, while table partitioning and time-based expiration handle multi-year retention cost-effectively. This serverless pattern avoids managing infrastructure and scales seamlessly from low to very high streaming throughput, making it the only option that meets both the performance and retention requirements.
- ✗
Store transactions in Cloud Bigtable and use Dataproc/Spark for analytics queries.
Why it's wrong here
Bigtable is a wide-column NoSQL database designed for high-throughput, low-latency point lookups and scans, not for SQL-based analytical queries. Running Spark on Dataproc to scan Bigtable would require a full table scan and would incur cluster spin-up and job-scheduling overhead, making reliable 2-second ad-hoc query results highly unlikely. Moreover, this pattern requires you to manage and pay for a separate Dataproc cluster, adding operational complexity without providing a serverless query engine comparable to BigQuery.
- ✗
Use Cloud SQL for storage and Cloud Dataprep for analytics transformations.
Why it's wrong here
Cloud SQL is a managed relational database with fixed CPU/memory/storage limits that cannot sustain a 100 GB/day streaming ingestion rate without manual sharding or frequent maintenance, and its query performance degrades sharply with large analytical scans. Cloud Dataprep is a data preparation UI for cleaning and transforming data, not an analytics/BI query engine, so it cannot execute the low-latency SQL-like queries the requirement demands. This option conflates data wrangling with interactive analytics and fundamentally lacks a scalable analytical storage tier.
Go deeper
Related to this question
Learn chapter
GCP Storage Solutions
Key term
BigQuery
BigQuery is a fully managed, serverless data warehouse on Google Cloud that lets you run fast SQL queries on massive datasets without managing any infrastructure.
Key term
Pub/Sub
Pub/Sub is a messaging pattern where publishers send messages without knowing who receives them, and subscribers receive only the messages they care about.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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