Quick Answer
The answer is to enable data sharding and reduce input pipeline parallelism. This is correct because out-of-memory errors during Vertex AI training often stem from the data loading pipeline consuming excessive RAM, especially when the entire dataset is loaded into memory per worker. Sharding splits the dataset across multiple workers so each processes only a fraction, while reducing parallelism limits the number of prefetching threads, directly lowering peak memory usage. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of distributed training bottlenecks and the trade-off between throughput and memory; a common trap is to increase batch size, which worsens the error, or to immediately scale up hardware, which increases cost unnecessarily. Remember the mnemonic “Shard and Slow” — shard the data and slow the pipeline to stop the OOM blow.
This PMLE practice question tests your understanding of scaling prototypes into ml models. 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 company uses Vertex AI for training. They have a large dataset stored in Cloud Storage and need to train a custom model using TensorFlow. The training job is failing with an out-of-memory error. What is the best first step?
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.
Clue: "first"
Why it matters: Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
Reduce model size.
Why wrong: May affect accuracy; not first step.
Enable data sharding and reduce input pipeline parallelism.
Reduces memory footprint of data loading.
Use a larger machine type.
Why wrong: Could help but is more expensive and doesn't address root cause of data pipeline memory.
Increase the batch size.
Why wrong: Would increase memory usage, worsening the OOM.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Enable data sharding and reduce input pipeline parallelism.
Option D is correct because enabling data sharding and reducing input pipeline parallelism can lower memory usage from data loading. Option A is wrong because increasing batch size would increase memory usage. Option B is wrong but might be a later step; it's not the best first step as it increases cost. Option C is wrong because reducing model size may degrade accuracy and is not a first step.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Reduce model size.
Why it's wrong here
May affect accuracy; not first step.
Enable data sharding and reduce input pipeline parallelism.
Why this is correct
Reduces memory footprint of data loading.
Clue confirmation
The clue words "best", "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Use a larger machine type.
Why it's wrong here
Could help but is more expensive and doesn't address root cause of data pipeline memory.
Increase the batch size.
Why it's wrong here
Would increase memory usage, worsening the OOM.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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.
Scaling prototypes into ML models — study guide chapter
Learn the concepts, then practise the questions
Scaling prototypes into ML models practice questions
Targeted practice on this topic area only
All PMLE questions
506 questions across all exam domains
Google Professional Machine Learning Engineer study guide
Full concept coverage aligned to exam objectives
PMLE practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Practise PMLE questions linked to Scaling prototypes into ML models.
Practise PMLE questions linked to Automating and orchestrating ML pipelines.
Practise PMLE questions linked to Collaborating within and across teams to manage data and models.
Practise PMLE questions linked to Architecting low-code ML solutions.
Practise PMLE questions linked to Collaborating to manage data and models.
Practise PMLE questions linked to Serving and scaling models.
Practise PMLE questions linked to Monitoring ML solutions.
Practise PMLE questions linked to Solving business challenges with ML.
Practise PMLE questions linked to PMLE fundamentals.
Practise PMLE questions linked to PMLE scenario.
Practise PMLE questions linked to PMLE troubleshooting.
Practice this exam
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Scaling prototypes into ML models — This question tests Scaling prototypes into ML models — Read the scenario before looking for a memorised answer..
The correct answer is: Enable data sharding and reduce input pipeline parallelism. — Option D is correct because enabling data sharding and reducing input pipeline parallelism can lower memory usage from data loading. Option A is wrong because increasing batch size would increase memory usage. Option B is wrong but might be a later step; it's not the best first step as it increases cost. Option C is wrong because reducing model size may degrade accuracy and is not a first 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.
Yes — watch for: "best", "first". 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.
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 →
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.