mediumMultiple Choice
PMLE Practice Question: Responsible for monitoring a batch prediction…
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?
⚠ Common exam trap
Many exam-takers 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.
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
✓
A recent code change that loads the entire dataset into memory before processing
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.
Answer analysis
Option-by-option breakdown
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
Loading the full dataset into memory scales memory use with input size, so a code change that materialises everything before processing explains intermittent out-of-memory failures despite unchanged data volume; the constraint is that input volume stayed constant, ruling out data growth.
- ✗
Increase in model size due to retraining
Why it's wrong here
A larger retrained model raises memory per prediction, but the stem says failures are intermittent and input volume unchanged, so the cause is resource contention rather than model size. Model growth would be the answer if memory demand rose consistently with each scoring run.
- ✗
Decrease in the number of worker machines
Why it's wrong here
Fewer workers reduce total cluster capacity, yet the stem says input volume is unchanged and failures are intermittent, so per-worker memory demand is not explained by worker count alone. Reducing workers would be correct if throughput, not memory, were the constraint.
- ✗
Increase in input data size
Why it's wrong here
The stem explicitly states input data volume has not changed, so a larger input cannot explain the new out-of-memory errors. Increased input size would be the cause if the pipeline processed more records than before, which the scenario rules out.
Go deeper
Related to this question
About these practice questions
Courseiva writes every PMLE question from scratch — 775 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.