- A
Store batch prediction results in BigQuery.
Why wrong: Not required; you can store in GCS. The question asks for steps, but you could, but it's not among the top three.
- B
Create a Vertex AI batch prediction job with input from GCS (TFRecord files).
Batch prediction supports GCS input.
- C
Use Dataflow to read images and write TFRecord files to GCS.
Dataflow can process 100 TB efficiently.
- D
Store batch prediction results in GCS.
Standard output for batch prediction.
- E
Use Cloud Functions to convert images to TFRecord.
Why wrong: Not scalable for 100 TB.
Question 340 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.
You are designing a batch prediction pipeline using Vertex AI. The input data is 100 TB of images stored in Cloud Storage. The model is a custom TensorFlow model that expects TFRecord format. The pipeline must be cost-effective and run within a time window of 2 hours. Which THREE steps should you include?
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
✓
Create a Vertex AI batch prediction job with input from GCS (TFRecord files).
Option B is correct because Vertex AI batch prediction jobs natively accept TFRecord files stored in Cloud Storage as input, which aligns with the requirement for a custom TensorFlow model. This approach is cost-effective and can complete within 2 hours by leveraging Vertex AI's managed infrastructure, avoiding the need to spin up and manage compute resources manually.
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.
- ✗
Store batch prediction results in BigQuery.
Why it's wrong here
Not required; you can store in GCS. The question asks for steps, but you could, but it's not among the top three.
- ✓
Create a Vertex AI batch prediction job with input from GCS (TFRecord files).
Why this is correct
Batch prediction supports GCS input.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Dataflow to read images and write TFRecord files to GCS.
Why this is correct
Dataflow can process 100 TB efficiently.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Store batch prediction results in GCS.
Why this is correct
Standard output for batch prediction.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Cloud Functions to convert images to TFRecord.
Why it's wrong here
Not scalable for 100 TB.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google often tests the misconception that Cloud Functions can handle large-scale data processing tasks, but the trap here is that Cloud Functions have strict timeout and memory limits, making them unsuitable for converting 100 TB of images to TFRecord format.
Detailed technical explanation
How to think about this question
Vertex AI batch prediction jobs use a distributed architecture that shards input data across multiple workers, enabling parallel processing of large datasets like 100 TB within a 2-hour window. Dataflow (option C) is ideal for the preprocessing step because it can read images from GCS, convert them to TFRecord format, and write back to GCS in a scalable, serverless manner, handling the 100 TB volume efficiently. The combination of Dataflow for preprocessing and Vertex AI for inference ensures cost-effectiveness by using autoscaling and pay-per-use pricing.
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 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: Create a Vertex AI batch prediction job with input from GCS (TFRecord files). — Option B is correct because Vertex AI batch prediction jobs natively accept TFRecord files stored in Cloud Storage as input, which aligns with the requirement for a custom TensorFlow model. This approach is cost-effective and can complete within 2 hours by leveraging Vertex AI's managed infrastructure, avoiding the need to spin up and manage compute resources manually.
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
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