- A
Pass data in-memory as Python objects between components.
Why wrong: In-memory passing is not supported in KFP; data must be serialized and stored externally.
- B
Use BigQuery table names as component outputs and inputs.
Why wrong: BigQuery table names are acceptable but less portable; GCS URIs are more standard for artifact passing.
- C
Use Cloud SQL to store intermediate results and pass connection strings.
Why wrong: Cloud SQL is not designed for intermediate data passing in ML pipelines; adds latency and complexity.
- D
Store data as artifacts in Cloud Storage and pass the GCS URI between components.
Correct: Passing GCS URIs allows components to be idempotent and data to be versioned.
Question 384 of 1,000
PMLE Automating and Orchestrating ML Pipelines Practice Question
This PMLE practice question tests your understanding of automating and orchestrating ml pipelines. 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 company is using Vertex AI Pipelines to automate model retraining. They have a component that creates a BigQuery table with training data. To ensure idempotency, the component should check if the table already exists and recreate it if necessary. What is the best practice for passing data between pipeline components?
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.
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
✓
Store data as artifacts in Cloud Storage and pass the GCS URI between components.
Option D is correct because Vertex AI Pipelines is designed to pass data between components via Cloud Storage artifacts. By storing the BigQuery table metadata or training data as a file in Cloud Storage and passing the GCS URI as an artifact, the pipeline ensures idempotency and decouples components. This approach aligns with Kubeflow Pipelines' artifact-based I/O model, where each component's outputs are materialized as URIs rather than in-memory objects.
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.
- ✗
Pass data in-memory as Python objects between components.
Why it's wrong here
In-memory passing is not supported in KFP; data must be serialized and stored externally.
- ✗
Use BigQuery table names as component outputs and inputs.
Why it's wrong here
BigQuery table names are acceptable but less portable; GCS URIs are more standard for artifact passing.
- ✗
Use Cloud SQL to store intermediate results and pass connection strings.
Why it's wrong here
Cloud SQL is not designed for intermediate data passing in ML pipelines; adds latency and complexity.
- ✓
Store data as artifacts in Cloud Storage and pass the GCS URI between components.
Why this is correct
Correct: Passing GCS URIs allows components to be idempotent and data to be versioned.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'passing data' with 'passing references to external services' (like BigQuery table names or Cloud SQL connection strings), but Vertex AI Pipelines expects artifact URIs (typically GCS paths) to maintain pipeline lineage, caching, and reproducibility.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI Pipelines uses Kubeflow Pipelines' artifact system, where each component declares typed outputs (e.g., `Dataset`, `Metrics`) that are serialized as metadata and stored in Cloud Storage. The pipeline DAG resolves these URIs at runtime, enabling automatic caching: if a component's inputs and code haven't changed, the cached output artifact is reused, which is critical for idempotent table recreation. In practice, a component can output a GCS path pointing to a Parquet file or a JSON schema, and the downstream component reads that URI to recreate the BigQuery table using a `bq` command or the BigQuery client library.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this PMLE question test?
Automating and Orchestrating ML Pipelines — This question tests Automating and Orchestrating ML Pipelines — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Store data as artifacts in Cloud Storage and pass the GCS URI between components. — Option D is correct because Vertex AI Pipelines is designed to pass data between components via Cloud Storage artifacts. By storing the BigQuery table metadata or training data as a file in Cloud Storage and passing the GCS URI as an artifact, the pipeline ensures idempotency and decouples components. This approach aligns with Kubeflow Pipelines' artifact-based I/O model, where each component's outputs are materialized as URIs rather than in-memory objects.
What should I do if I get this PMLE 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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Last reviewed: Jul 4, 2026
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