Quick Answer
The answer is a recent code change that loads the entire dataset into memory before processing. This is correct because batch prediction pipelines are designed to process data in a streaming or chunked fashion to keep memory usage predictable, and any alteration that forces the entire dataset into memory—even if the input volume hasn't changed—will directly exceed the allocated heap or container memory, causing intermittent out-of-memory errors. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of Vertex AI batch prediction resource management and the common trap of assuming unchanged data volume means unchanged memory requirements. A key memory tip: remember that batch prediction should always "chunk before you crunch"—never load all data at once.
This PMLE practice question tests your understanding of monitoring ml solutions. 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.
You are responsible for monitoring a batch prediction pipeline that runs daily. Recently, the pipeline started failing intermittently with out-of-memory errors. The input data volume has not changed. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
A recent code change that loads the entire dataset into memory before processing
This could cause OOM for large datasets.
Increase in model size due to retraining
Why wrong: Model is served elsewhere; batch pipeline typically runs predictions using the same model.
Decrease in the number of worker machines
Why wrong: Fewer workers would cause slower processing, not OOM.
Increase in input data size
Why wrong: Data volume has not changed.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
A recent code change that loads the entire dataset into memory before processing
Option A is correct because a code change that loads the entire dataset into memory before processing would directly cause out-of-memory (OOM) errors, even if the input data volume remains unchanged. In batch prediction pipelines, data is typically streamed or processed in chunks to manage memory efficiently. A change that bypasses this pattern and loads all data at once can exceed the available heap or container memory, leading to intermittent failures depending on data characteristics or concurrent loads.
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.
A recent code change that loads the entire dataset into memory before processing
Why this is correct
This could cause OOM for large datasets.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Increase in model size due to retraining
Why it's wrong here
Model is served elsewhere; batch pipeline typically runs predictions using the same model.
Decrease in the number of worker machines
Why it's wrong here
Fewer workers would cause slower processing, not OOM.
Increase in input data size
Why it's wrong here
Data volume has not changed.
Common exam traps
The trap here is that candidates may assume OOM errors are always caused by increased data volume or resource scaling issues, but the question explicitly states data volume is unchanged, forcing you to consider code-level changes that alter memory access patterns.
Detailed technical explanation
Batch prediction pipelines often use frameworks like Apache Spark or TensorFlow Serving, where memory management relies on partitioning data into chunks (e.g., Spark partitions or TFRecord batches). A code change that calls `collect()` in Spark or loads all data into a single tensor in TensorFlow bypasses these optimizations, causing the driver or worker to allocate memory proportional to the entire dataset. In real-world scenarios, this can manifest as OOM errors only when a particular day's data has slightly more rows or larger feature vectors, making failures intermittent and hard to debug.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
Monitoring ML solutions — This question tests Monitoring ML solutions — Read the scenario before looking for a memorised answer..
The correct answer is: A recent code change that loads the entire dataset into memory before processing — Option A is correct because a code change that loads the entire dataset into memory before processing would directly cause out-of-memory (OOM) errors, even if the input data volume remains unchanged. In batch prediction pipelines, data is typically streamed or processed in chunks to manage memory efficiently. A change that bypasses this pattern and loads all data at once can exceed the available heap or container memory, leading to intermittent failures depending on data characteristics or concurrent loads.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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