- A
Reduce the batch size in the prediction request.
Why wrong: Batch size may help but the root cause is likely model size; memory is still exceeded.
- B
Split the input data into smaller files and run multiple batch jobs.
Why wrong: Splitting data does not reduce memory usage per instance if the model remains large.
- C
Add a GPU accelerator to offload computation.
Why wrong: GPU helps compute but does not increase system memory.
- D
Use a machine type with more memory, such as n1-highmem-8 (52 GB).
Increasing memory directly solves out-of-memory errors.
Question 43 of 1,000
hardMultiple ChoiceObjective-mapped
Resolve Out-of-Memory Error in Vertex AI Batch Prediction
This PMLE practice question tests your understanding of pmle exam topics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 data scientist runs a batch prediction job on Vertex AI using a custom container. The job processes a large JSONL file (10 GB) and fails with an out-of-memory error. The machine type is n1-standard-4 (15 GB memory). Which action should be taken to resolve the error while minimizing cost?
Quick Answer
The correct answer is to use a machine type with more memory, such as n1-highmem-8 (52 GB), because the out-of-memory error in batch prediction indicates that the model or data processing footprint exceeds the available RAM on the current n1-standard-4 instance. Vertex AI batch prediction loads the entire model into memory for each prediction worker, and when processing a large JSONL file, the combined model size and input batch can overwhelm 15 GB of memory. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of resource provisioning trade-offs—specifically that high-memory machine types are designed for memory-bound workloads, while splitting input data or adding GPUs does not address insufficient RAM. A common trap is assuming batch size adjustments alone will fix the issue, but the primary bottleneck is the machine’s memory ceiling. Remember the mnemonic: “OOM? Boost the RAM, not the batch or the GPU.”
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 machine type with more memory, such as n1-highmem-8 (52 GB).
Option D is correct. The out-of-memory (OOM) error indicates the machine's memory is insufficient for the model or data processing. Upgrading to n1-highmem-8 (52 GB) directly addresses the memory shortage while minimizing cost, as high-memory machines provide more RAM without unnecessary extras like GPUs. Option A (reducing batch size) might help but is not the primary fix if the model itself is large, and cost efficiency is not improved. Option B (splitting input data into smaller files) does not reduce per-instance memory pressure and could increase latency and cost due to multiple jobs. Option C (adding a GPU) increases compute but not memory, so it does not resolve the OOM error.
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.
- ✗
Reduce the batch size in the prediction request.
Why it's wrong here
Batch size may help but the root cause is likely model size; memory is still exceeded.
- ✗
Split the input data into smaller files and run multiple batch jobs.
Why it's wrong here
Splitting data does not reduce memory usage per instance if the model remains large.
- ✗
Add a GPU accelerator to offload computation.
Why it's wrong here
GPU helps compute but does not increase system memory.
- ✓
Use a machine type with more memory, such as n1-highmem-8 (52 GB).
Why this is correct
Increasing memory directly solves out-of-memory errors.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
Visual reference
What to study next
Got this wrong? Here's your next step.
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Related practice questions
Related PMLE practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Automating and Orchestrating ML Pipelines practice questions
Practise PMLE questions linked to Automating and Orchestrating ML Pipelines.
Collaborating Within and Across Teams to Manage Data and Models practice questions
Practise PMLE questions linked to Collaborating Within and Across Teams to Manage Data and Models.
Serving and Scaling Models practice questions
Practise PMLE questions linked to Serving and Scaling Models.
Monitoring ML Solutions practice questions
Practise PMLE questions linked to Monitoring ML Solutions.
Architecting Low-Code ML Solutions practice questions
Practise PMLE questions linked to Architecting Low-Code ML Solutions.
Scaling Prototypes into ML Models practice questions
Practise PMLE questions linked to Scaling Prototypes into ML Models.
Collaborating to manage data and models practice questions
Practise PMLE questions linked to Collaborating to manage data and models.
Solving business challenges with ML practice questions
Practise PMLE questions linked to Solving business challenges with ML.
PMLE fundamentals practice questions
Practise PMLE questions linked to PMLE fundamentals.
PMLE scenario practice questions
Practise PMLE questions linked to PMLE scenario.
PMLE troubleshooting practice questions
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Start a free PMLE practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this PMLE question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Use a machine type with more memory, such as n1-highmem-8 (52 GB). — Option D is correct. The out-of-memory (OOM) error indicates the machine's memory is insufficient for the model or data processing. Upgrading to n1-highmem-8 (52 GB) directly addresses the memory shortage while minimizing cost, as high-memory machines provide more RAM without unnecessary extras like GPUs. Option A (reducing batch size) might help but is not the primary fix if the model itself is large, and cost efficiency is not improved. Option B (splitting input data into smaller files) does not reduce per-instance memory pressure and could increase latency and cost due to multiple jobs. Option C (adding a GPU) increases compute but not memory, so it does not resolve the OOM error.
What should I do if I get this PMLE question wrong?
Identify which PMLE exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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 →
Keep practising
More PMLE practice questions
- A travel booking company has a real-time recommendation system that suggests hotels and flights to users. The model is s…
- A global retail company uses Vertex AI Recommendations to provide product recommendations on their website. They have a…
- Your team is developing a machine learning model for real-time fraud detection. The training pipeline runs on Vertex AI…
- A healthcare organization is building a machine learning model to predict patient readmission risk. They have sensitive…
- You are an ML engineer at a global e-commerce company. Your team has developed a deep learning model for product recomme…
- A financial services company uses Vertex AI AutoML Tables to build a credit risk model. The dataset contains 500,000 row…
Last reviewed: Jun 24, 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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.