The answer is that the model is deployed on a small machine type with insufficient compute. This is the most likely cause of high latency because Vertex AI AutoML Vision endpoints run inference inside containers, and underpowered machine types like n1-standard-2 lack the CPU and memory needed to process high-resolution medical images efficiently, causing response times to balloon past a 2-second SLA. On the Google Professional Machine Learning Engineer exam, this scenario tests your understanding of production deployment trade-offs—specifically that model accuracy on a test set does not guarantee low latency in production if the serving infrastructure is mismatched. A common trap is to blame the model architecture or data pipeline, but the fix here is simply choosing a larger machine type (e.g., n1-standard-16) or a GPU-accelerated instance. Memory tip: “Big images need big machines—don’t starve your endpoint.”
PMLE Architecting low-code ML solutions Practice Question
This PMLE practice question tests your understanding of architecting low-code 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.
A healthcare startup deployed a Vertex AI AutoML Vision model to detect anomalies in medical images. The model performs well on the test set but has high latency in production, exceeding the 2-second SLA. The images are stored in Cloud Storage and are processed via a Cloud Function triggered by new uploads. 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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model is deployed on a small machine type with insufficient compute.
Option B is correct because the most likely cause of high latency exceeding the 2-second SLA is that the Vertex AI AutoML Vision model is deployed on a small machine type (e.g., n1-standard-2 or lower) with insufficient compute resources (CPU/memory). AutoML Vision endpoints use container-based serving, and underpowered machines cannot handle the inference load efficiently, especially for high-resolution medical images, leading to response times beyond the SLA.
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.
✗
The images are being resized and preprocessed in the Cloud Function, adding latency.
Why it's wrong here
Preprocessing adds some latency but is usually minor compared to inference.
✓
The model is deployed on a small machine type with insufficient compute.
Why this is correct
A small machine type (e.g., n1-standard-2) can cause high inference latency under load.
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.
✗
The Cloud Function has a cold start issue.
Why it's wrong here
Cold starts add latency but typically not a consistent >2s delay.
✗
The AutoML Vision endpoint is not using GPU acceleration.
Why it's wrong here
AutoML Vision endpoints automatically use TPU/GPU for inference.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse cold start latency (Cloud Function) with inference latency (model serving), or assume GPU acceleration is optional for AutoML endpoints, when in fact AutoML Vision automatically uses GPUs and the real bottleneck is the compute capacity of the serving machine.
Detailed technical explanation
How to think about this question
Vertex AI AutoML Vision deploys models as containerized services on Compute Engine instances; the machine type (e.g., n1-standard-4 vs. n1-highcpu-16) directly impacts inference throughput and latency. For medical images (often 512x512 or larger), the model's neural network requires significant compute for forward propagation; a small machine type like n1-standard-2 (2 vCPUs, 7.5 GB RAM) can cause queueing and slower per-request processing, easily exceeding 2 seconds under load. Real-world scenarios show that using a machine type with at least 4 vCPUs and 15 GB RAM, or enabling autoscaling, is critical for meeting strict SLAs.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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.
Architecting low-code ML solutions — This question tests Architecting low-code ML solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model is deployed on a small machine type with insufficient compute. — Option B is correct because the most likely cause of high latency exceeding the 2-second SLA is that the Vertex AI AutoML Vision model is deployed on a small machine type (e.g., n1-standard-2 or lower) with insufficient compute resources (CPU/memory). AutoML Vision endpoints use container-based serving, and underpowered machines cannot handle the inference load efficiently, especially for high-resolution medical images, leading to response times beyond the SLA.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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