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Question 292 of 500

Planning and configuring a cloud solution →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is to stream transactions through Pub/Sub to Dataflow and into BigQuery, running analytics directly on BigQuery. This architecture is correct because Pub/Sub ingests the real-time data, Dataflow handles stream processing and windowing without batching delays, and BigQuery’s columnar storage and automatic sharding can return queries within 2 seconds even as the dataset grows by 100 GB per day. BigQuery also supports 7-year retention through time-based partitioning and reduced-cost long-term storage, meeting regulatory compliance without manual data lifecycle management. On the Google Associate Cloud Engineer exam, this scenario tests your understanding of combining managed services for real-time analytics architecture with strict latency and retention requirements—a common trap is choosing a solution that uses Cloud Storage or Bigtable for analytics, which cannot match BigQuery’s sub-second query performance on large historical datasets. Memory tip: think “Pub/Sub pipes it in, Dataflow shapes it, BigQuery serves it fast and keeps it forever.”

Google ACE Planning and configuring a cloud solution Practice Question

This ACE practice question tests your understanding of planning and configuring a cloud solution. 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.

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?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

Question 1hardmultiple choice

Full question →

A
Write transactions to Cloud Spanner; run analytics queries directly against Spanner.
Why wrong: Spanner is an OLTP database optimized for transactional workloads, not complex analytics. Large analytical queries would impact OLTP performance and cost would be very high.
B
Stream transactions through Pub/Sub → Dataflow → BigQuery; run analytics on BigQuery.
This is the canonical GCP streaming analytics pattern: Pub/Sub for ingestion, Dataflow for transformation, BigQuery for analytics with sub-second to 2-second query performance and 7-year retention.
C
Store transactions in Cloud Bigtable and use Dataproc/Spark for analytics queries.
Why wrong: Bigtable doesn't natively support SQL analytics. Spark jobs on Dataproc add latency and complexity; this pattern doesn't reliably achieve 2-second query results for ad-hoc analytics.
D
Use Cloud SQL for storage and Cloud Dataprep for analytics transformations.
Why wrong: Cloud SQL cannot scale to 100 GB/day analytics workloads efficiently. Cloud Dataprep is a data preparation UI tool, not an analytics query engine.

Full breakdown with real-world context →

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.

Option B is correct because 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.

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.

✗
Write transactions to Cloud Spanner; run analytics queries directly against Spanner.
Why it's wrong here
Spanner is an OLTP database optimized for transactional workloads, not complex analytics. Large analytical queries would impact OLTP performance and cost would be very high.
✓
Stream transactions through Pub/Sub → Dataflow → BigQuery; run analytics on BigQuery.
Why this is correct
This is the canonical GCP streaming analytics pattern: Pub/Sub for ingestion, Dataflow for transformation, BigQuery for analytics with sub-second to 2-second query performance and 7-year retention.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Store transactions in Cloud Bigtable and use Dataproc/Spark for analytics queries.
Why it's wrong here
Bigtable doesn't natively support SQL analytics. Spark jobs on Dataproc add latency and complexity; this pattern doesn't reliably achieve 2-second query results for ad-hoc analytics.
✗
Use Cloud SQL for storage and Cloud Dataprep for analytics transformations.
Why it's wrong here
Cloud SQL cannot scale to 100 GB/day analytics workloads efficiently. Cloud Dataprep is a data preparation UI tool, not an analytics query engine.

Common exam traps

Common exam trap: answer the scenario, not the keyword

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.

Detailed technical explanation

How to think about this question

BigQuery uses a distributed, columnar storage format (Capacitor) and a Dremel-based query engine that dynamically allocates slots for parallel processing, enabling sub-2-second queries on terabytes of data when using clustering and partitioning. Dataflow's autoscaling and exactly-once processing semantics ensure that streaming data from Pub/Sub is reliably ingested into BigQuery without duplicates, even under high throughput. For 7-year retention, BigQuery's long-term storage automatically reduces the storage cost by ~50% after 90 days, and time-based partitioning allows efficient pruning of old data for compliance audits.

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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

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.

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FAQ

Questions learners often ask

What does this ACE question test?

Planning and configuring a cloud solution — This question tests Planning and configuring a cloud solution — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Stream transactions through Pub/Sub → Dataflow → BigQuery; run analytics on BigQuery. — Option B is correct because 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.

What should I do if I get this ACE question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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