- A
Cloud Firestore
Why wrong: Firestore is not supported as input for batch prediction.
- B
Cloud SQL
Why wrong: Cloud SQL is not a direct input source for Vertex AI batch prediction.
- C
BigQuery
BigQuery is a supported input source.
- D
Cloud Spanner
Why wrong: Spanner is not a direct input source for batch prediction.
- E
Cloud Storage
GCS is a supported input source.
Question 739 of 1,000
PMLE Serving and Scaling Models Practice Question
This PMLE practice question tests your understanding of serving and scaling models. 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.
Which TWO of the following can be used as input sources for Vertex AI batch prediction jobs? (Choose 2)
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
✓
BigQuery
BigQuery is a supported input source for Vertex AI batch prediction jobs because Vertex AI can directly read data from BigQuery tables for batch predictions. This integration allows you to store your prediction requests in BigQuery and have Vertex AI write the predictions back to a BigQuery output table, streamlining the workflow for large-scale predictions without needing to export data to Cloud Storage first.
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.
- ✗
Cloud Firestore
Why it's wrong here
Firestore is not supported as input for batch prediction.
- ✗
Cloud SQL
Why it's wrong here
Cloud SQL is not a direct input source for Vertex AI batch prediction.
- ✓
BigQuery
Why this is correct
BigQuery is a supported input source.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Cloud Spanner
Why it's wrong here
Spanner is not a direct input source for batch prediction.
- ✓
Cloud Storage
Why this is correct
GCS is a supported input source.
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 often assume any Google Cloud database (like Firestore, Cloud SQL, or Spanner) can serve as a direct input source for batch predictions, but Vertex AI batch prediction only supports BigQuery and Cloud Storage as input sources, requiring data to be exported or staged in those services first.
Detailed technical explanation
How to think about this question
Under the hood, Vertex AI batch prediction jobs use a distributed processing framework (e.g., Dataflow) to read input data from BigQuery tables or Cloud Storage objects (JSON Lines, CSV, TFRecord). For BigQuery, the job issues a query to read the table, and the predictions are written back to a specified BigQuery table or Cloud Storage location. A subtle behavior is that the input table must be in the same region as the Vertex AI batch prediction job, and the schema must include a column for the instance data (e.g., 'instance' or 'instances') or be in a format compatible with the model's input signature.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Serving and Scaling Models — study guide chapter
Learn the concepts, then practise the questions
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and Scaling Models — This question tests Serving and Scaling Models — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: BigQuery — BigQuery is a supported input source for Vertex AI batch prediction jobs because Vertex AI can directly read data from BigQuery tables for batch predictions. This integration allows you to store your prediction requests in BigQuery and have Vertex AI write the predictions back to a BigQuery output table, streamlining the workflow for large-scale predictions without needing to export data to Cloud Storage first.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.
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