- A
Increase the number of replicas to reduce error rate.
Why wrong: May temporarily mask the problem if errors are due to overload, but does not fix root cause.
- B
Compare the input data distribution of recent requests to the training data distribution using Explainable AI.
Why wrong: Useful but not the first step; requires setup and may not reveal immediate causes.
- C
Roll back to the previous model version immediately.
Why wrong: Rolling back without investigation may not fix the issue and could disrupt service.
- D
Check the Cloud Monitoring dashboard for latency and error codes, and review the model's prediction logs.
Monitoring and logs provide direct evidence to diagnose errors.
Quick Answer
The answer is to check the Cloud Monitoring dashboard for latency and error codes, and review the model's prediction logs. This is the correct first step because diagnosing prediction errors after model redeployment requires immediate visibility into system-level metrics and request-level data; latency spikes or specific HTTP error codes (like 503 or 500) in Cloud Monitoring can reveal infrastructure issues, while prediction logs expose data drift or feature mismatches between training and serving. On the Google Professional Machine Learning Engineer exam, this tests your understanding of the ML lifecycle’s monitoring phase—specifically that root-cause analysis must begin with observability tools before jumping to retraining or scaling. A common trap is assuming the new data is flawed (Option A) or immediately enabling model monitoring (Option B), which requires prior configuration. Memory tip: “Monitor first, diagnose second—logs and metrics are your detective’s lens.”
PMLE Serving and scaling models Practice Question
This PMLE practice question tests your understanding of serving and scaling 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 machine learning engineer notices that the Vertex AI Prediction endpoint's error rate has increased over the past week. The model was retrained with new data and redeployed. Which step should the engineer take first to diagnose the issue?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
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.
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
Check the Cloud Monitoring dashboard for latency and error codes, and review the model's prediction logs.
Option C is correct because reviewing Cloud Monitoring dashboards and logs provides immediate insights into error patterns and root cause. Option A is premature without investigation. Option B is more advanced and requires setup. Option D might temporarily reduce errors due to overload but does not address the underlying cause.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the number of replicas to reduce error rate.
Why it's wrong here
May temporarily mask the problem if errors are due to overload, but does not fix root cause.
- ✗
Compare the input data distribution of recent requests to the training data distribution using Explainable AI.
Why it's wrong here
Useful but not the first step; requires setup and may not reveal immediate causes.
- ✗
Roll back to the previous model version immediately.
Why it's wrong here
Rolling back without investigation may not fix the issue and could disrupt service.
- ✓
Check the Cloud Monitoring dashboard for latency and error codes, and review the model's prediction logs.
Why this is correct
Monitoring and logs provide direct evidence to diagnose errors.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
- Static NAT maps one inside address to one outside address.
- PAT allows many inside hosts to share one public address using ports.
- Inside local and inside global describe the private and translated addresses.
- NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
- Identify inside and outside interfaces first.
- Check whether the scenario needs static NAT, dynamic NAT or PAT.
- Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
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. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. 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
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related PMLE NAT questions on configuration and troubleshooting.
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Serving and scaling models — study guide chapter
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FAQ
Questions learners often ask
What does this PMLE question test?
Serving and scaling models — This question tests Serving and scaling models — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Check the Cloud Monitoring dashboard for latency and error codes, and review the model's prediction logs. — Option C is correct because reviewing Cloud Monitoring dashboards and logs provides immediate insights into error patterns and root cause. Option A is premature without investigation. Option B is more advanced and requires setup. Option D might temporarily reduce errors due to overload but does not address the underlying cause.
What should I do if I get this PMLE question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related PMLE NAT questions on configuration and troubleshooting.
Are there clue words in this question I should notice?
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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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.
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