This PMLE practice question tests your understanding of serving and scaling models. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 deploys a TensorFlow model on Vertex AI Prediction with a single node. During peak hours, inference latency increases. What should they do first to reduce latency?
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.
Enable autoscaling for the deployment
Autoscaling adds nodes during peak traffic, reducing latency.
Increase the machine type of the node
Why wrong: Increasing machine type may help but does not address scaling under load.
Decrease the min replicas to 0
Why wrong: Reducing min replicas may cause cold starts and increase latency.
Enable automatic batching of requests
Why wrong: Batching increases latency as requests wait to be batched.
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 autoscaling for the deployment
Enabling autoscaling for the deployment is the correct first step because it allows Vertex AI Prediction to dynamically adjust the number of replicas based on incoming traffic. During peak hours, autoscaling can add more nodes to distribute the inference load, directly reducing latency without requiring manual intervention or over-provisioning.
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.
Enable autoscaling for the deployment
Why this is correct
Autoscaling adds nodes during peak traffic, reducing latency.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Increase the machine type of the node
Why it's wrong here
Increasing machine type may help but does not address scaling under load.
Decrease the min replicas to 0
Why it's wrong here
Reducing min replicas may cause cold starts and increase latency.
Enable automatic batching of requests
Why it's wrong here
Batching increases latency as requests wait to be batched.
Common exam traps
The trap here is that candidates often confuse improving throughput (batching or bigger machines) with reducing latency under load, but the first action should always be to add more replicas via autoscaling to handle concurrent requests, not to optimize a single node's performance.
Detailed technical explanation
Vertex AI Prediction uses the same underlying infrastructure as Google Kubernetes Engine (GKE) for autoscaling, relying on the Horizontal Pod Autoscaler (HPA) which monitors CPU utilization or custom metrics. The autoscaler has a cooldown period (default 60 seconds) to avoid thrashing, and the min/max replica count must be configured appropriately to balance cost and performance. In real-world scenarios, a sudden spike in traffic (e.g., from a marketing campaign) can overwhelm a single node, and autoscaling is the first line of defense before considering model optimization or hardware upgrades.
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.
Serving and scaling models — study guide chapter
Learn the concepts, then practise the questions
Serving and scaling 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
Serving and scaling models — This question tests Serving and scaling models — Read the scenario before looking for a memorised answer..
The correct answer is: Enable autoscaling for the deployment — Enabling autoscaling for the deployment is the correct first step because it allows Vertex AI Prediction to dynamically adjust the number of replicas based on incoming traffic. During peak hours, autoscaling can add more nodes to distribute the inference load, directly reducing latency without requiring manual intervention or over-provisioning.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
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 →
Keep practising
Last reviewed: Jun 11, 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.