- A
Use a CoGroupByKey transform to join the incoming stream with a stream from BigQuery.
Why wrong: CoGroupByKey requires both sides to be in a stream; the lookup table is static, not a stream.
- B
Use BigQuery IO to query the table for every incoming message.
Why wrong: This would be very slow and expensive due to many queries.
- C
Use a side input that reads the BigQuery table periodically and caches it.
Side inputs are ideal for distributing a static lookup table to all workers. The data can be refreshed on a schedule.
- D
Use a stateful DoFn and store the lookup in state per key.
Why wrong: Stateful DoFn is for per-key state across events, not for a global lookup table.
Question 33 of 1,000
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 building a Dataflow pipeline in Python that reads messages from Pub/Sub, enriches them with data from a BigQuery table, and writes the results to BigQuery. The enrichment lookup table is large and changes infrequently. Which approach minimizes cost and latency?
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 a side input that reads the BigQuery table periodically and caches it.
Option C is correct because using a side input that periodically reads the BigQuery table and caches it avoids querying BigQuery for every incoming message, which would be prohibitively expensive and high-latency. The side input is refreshed at a configurable interval (e.g., every 10 minutes) via a pipeline option, and the cached data is broadcast to all workers, enabling fast, in-memory lookups without per-element I/O. This approach minimizes cost by reducing BigQuery API calls and minimizes latency by avoiding synchronous queries for each message.
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 a CoGroupByKey transform to join the incoming stream with a stream from BigQuery.
Why it's wrong here
CoGroupByKey requires both sides to be in a stream; the lookup table is static, not a stream.
- ✗
Use BigQuery IO to query the table for every incoming message.
Why it's wrong here
This would be very slow and expensive due to many queries.
- ✓
Use a side input that reads the BigQuery table periodically and caches it.
Why this is correct
Side inputs are ideal for distributing a static lookup table to all workers. The data can be refreshed on a schedule.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a stateful DoFn and store the lookup in state per key.
Why it's wrong here
Stateful DoFn is for per-key state across events, not for a global lookup table.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that querying BigQuery per message is acceptable in streaming pipelines, but the trap here is that candidates overlook the cost and latency implications of per-element I/O, especially with BigQuery's pricing model and query latency.
Detailed technical explanation
How to think about this question
Under the hood, a side input in Apache Beam is implemented as a `PCollectionView` that is materialized as a persistent, immutable map or list broadcast to all parallel workers via the Beam runner's side-input distribution mechanism (e.g., gRPC or shared memory). The periodic refresh is achieved by using `GenerateSequence` with a fixed interval to trigger a `ParDo` that reads the BigQuery table and updates the side input; this avoids stale data while keeping costs low. In a real-world scenario, a large lookup table (e.g., 10 GB) would be cached in memory across workers, and the refresh interval (e.g., 1 hour) would be tuned to balance freshness against memory overhead.
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.
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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 a side input that reads the BigQuery table periodically and caches it. — Option C is correct because using a side input that periodically reads the BigQuery table and caches it avoids querying BigQuery for every incoming message, which would be prohibitively expensive and high-latency. The side input is refreshed at a configurable interval (e.g., every 10 minutes) via a pipeline option, and the cached data is broadcast to all workers, enabling fast, in-memory lookups without per-element I/O. This approach minimizes cost by reducing BigQuery API calls and minimizes latency by avoiding synchronous queries for each message.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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