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Question 847 of 1,000

Ingesting and Processing the Data →mediumMultiple ChoiceObjective-mapped

PDE Ingesting and Processing the Data Practice Question

This PDE practice question tests your understanding of ingesting and processing the data. 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.

You are designing a streaming pipeline that ingests events from Pub/Sub, enriches them with a machine learning model, and writes the results to BigQuery. The ML model is deployed on Cloud Run and has a high latency (500ms per request). You need to minimize the impact of slow ML inference on the overall pipeline throughput. Which approach should you take?

Clue words in this question

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

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

A
Use Dataflow to write events to Pub/Sub, then use a separate Dataflow pipeline that batches calls to Cloud Run.
Decoupling via Pub/Sub allows batching and async processing, improving throughput.
B
Increase the number of Dataflow workers to compensate for the latency.
Why wrong: More workers help but each element still incurs 500ms; batching is more effective.
C
Use Cloud Functions to call Cloud Run and write directly to BigQuery.
Why wrong: Cloud Functions have limited concurrency and would still be slow per invocation.
D
Use Dataflow's ParDo with synchronous calls to Cloud Run for each element.
Why wrong: Synchronous calls per element will cause high latency and reduce throughput significantly.

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

✓

Use Dataflow to write events to Pub/Sub, then use a separate Dataflow pipeline that batches calls to Cloud Run.

Option A is correct because it uses Dataflow to batch events before sending them to Cloud Run, which amortizes the 500ms per-request latency over multiple events, significantly increasing throughput. By writing events to Pub/Sub and then processing them in a separate Dataflow pipeline with batched calls, you decouple the ingestion from the inference and avoid blocking on each individual request.

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.

✓
Use Dataflow to write events to Pub/Sub, then use a separate Dataflow pipeline that batches calls to Cloud Run.
Why this is correct
Decoupling via Pub/Sub allows batching and async processing, improving throughput.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of Dataflow workers to compensate for the latency.
Why it's wrong here
More workers help but each element still incurs 500ms; batching is more effective.
✗
Use Cloud Functions to call Cloud Run and write directly to BigQuery.
Why it's wrong here
Cloud Functions have limited concurrency and would still be slow per invocation.
✗
Use Dataflow's ParDo with synchronous calls to Cloud Run for each element.
Why it's wrong here
Synchronous calls per element will cause high latency and reduce throughput significantly.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates assume parallelism (more Dataflow workers) or faster invocation methods (Cloud Functions) can overcome high per-request latency, when the real solution is to batch requests using Dataflow's batch processing capabilities to reduce the number of round trips.

Detailed technical explanation

How to think about this question

Under the hood, Dataflow's GroupIntoBatches transform can collect elements into batches of configurable size or window duration before making a single HTTP request to Cloud Run. This reduces the number of invocations from millions to thousands, and the 500ms latency is incurred per batch, not per event. In real-world scenarios, this batching approach is critical when integrating with external ML services that have high per-request overhead, such as GPU-backed models or complex feature engineering pipelines.

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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

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 PDE question test?

Ingesting and Processing the Data — This question tests Ingesting and Processing the Data — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Use Dataflow to write events to Pub/Sub, then use a separate Dataflow pipeline that batches calls to Cloud Run. — Option A is correct because it uses Dataflow to batch events before sending them to Cloud Run, which amortizes the 500ms per-request latency over multiple events, significantly increasing throughput. By writing events to Pub/Sub and then processing them in a separate Dataflow pipeline with batched calls, you decouple the ingestion from the inference and avoid blocking on each individual request.

What should I do if I get this PDE 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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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