- A
Use Cloud Functions to transform each file individually.
Why wrong: Cloud Functions have timeouts and are not suitable for 50 TB of data.
- B
Use Cloud SQL to store intermediate results.
Why wrong: Cloud SQL is not designed for large-scale batch data; GCS is appropriate.
- C
Run Vertex AI batch prediction job with GCS source pointing to the processed TFRecord files.
Batch prediction can read from GCS and use the trained model.
- D
Use Dataflow to read CSV, perform feature engineering, and write to GCS in TFRecord format.
TFRecord is efficient for ML models; batch prediction reads from GCS.
- E
Use Dataflow to read CSV, perform feature engineering, and write to BigQuery.
Why wrong: Writing to BigQuery is unnecessary and may be slower than GCS for batch prediction.
Question 562 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 50 TB in CSV format on GCS. The model requires feature engineering that involves complex transformations (e.g., datetime parsing, one-hot encoding). Which THREE services or steps should you include in your pipeline?
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
✓
Run Vertex AI batch prediction job with GCS source pointing to the processed TFRecord files.
Option C is correct because Vertex AI batch prediction jobs require input data in TFRecord format for optimal performance with TensorFlow-based models. By writing the processed data as TFRecords to GCS, you enable the batch prediction service to read and score the data efficiently, leveraging its native support for this format.
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 Cloud Functions to transform each file individually.
Why it's wrong here
Cloud Functions have timeouts and are not suitable for 50 TB of data.
- ✗
Use Cloud SQL to store intermediate results.
Why it's wrong here
Cloud SQL is not designed for large-scale batch data; GCS is appropriate.
- ✓
Run Vertex AI batch prediction job with GCS source pointing to the processed TFRecord files.
Why this is correct
Batch prediction can read from GCS and use the trained model.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use Dataflow to read CSV, perform feature engineering, and write to GCS in TFRecord format.
Why this is correct
TFRecord is efficient for ML models; batch prediction reads from GCS.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Dataflow to read CSV, perform feature engineering, and write to BigQuery.
Why it's wrong here
Writing to BigQuery is unnecessary and may be slower than GCS for batch prediction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that any cloud service can handle large-scale data processing, but the trap here is that Cloud Functions and Cloud SQL are inappropriate for batch processing of 50 TB, leading candidates to overlook the need for a distributed data processing service like Dataflow.
Detailed technical explanation
How to think about this question
Dataflow (option D) is the correct choice for distributed data processing because it uses Apache Beam under the hood, allowing you to read CSV files from GCS, apply complex feature engineering (e.g., datetime parsing, one-hot encoding) in a scalable, parallel manner, and write the output as TFRecord files. TFRecord is a binary format that TensorFlow models consume natively, reducing I/O overhead during batch prediction. In real-world scenarios, using Dataflow with autoscaling can handle terabytes of data by dynamically adjusting worker count based on throughput, while Cloud Functions would fail due to resource constraints.
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 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: Run Vertex AI batch prediction job with GCS source pointing to the processed TFRecord files. — Option C is correct because Vertex AI batch prediction jobs require input data in TFRecord format for optimal performance with TensorFlow-based models. By writing the processed data as TFRecords to GCS, you enable the batch prediction service to read and score the data efficiently, leveraging its native support for this format.
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.
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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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