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Question 357 of 499

Operationalizing machine learning models →mediumMultiple ChoiceObjective-mapped

Quick Answer

The answer is to configure horizontal autoscaling with a higher maximum number of nodes and set a CPU utilization target. This is correct because configuring autoscaling on Vertex AI endpoints for consistent latency directly addresses the root cause of degraded performance during traffic spikes: insufficient compute resources. By allowing Vertex AI to automatically add nodes when CPU utilization exceeds the target, the inference load is distributed across more replicas, preventing request queuing and timeouts. On the Google Professional Data Engineer exam, this scenario tests your understanding of Vertex AI Prediction’s scaling mechanisms versus vertical scaling or manual node adjustments. A common trap is to assume that increasing the minimum node count alone will solve latency issues, but that wastes resources during low traffic and still fails to handle sudden peaks. The key is that autoscaling with a CPU utilization target dynamically matches capacity to demand. Memory tip: think “CPU target = traffic trigger” — set it low enough to scale before latency spikes.

PDE Operationalizing machine learning models Practice Question

This PDE practice question tests your understanding of operationalizing machine learning models. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 has deployed a machine learning model on Vertex AI Prediction that serves real-time predictions for a customer-facing application. The model was trained using a custom container and is hosted on a single endpoint with a minimum number of nodes. Recently, the team noticed that during peak traffic, prediction latency increases significantly and some requests time out. The endpoint is configured with a baseline traffic split of 100% on the current model version. Which action should the team take to reduce latency and improve reliability?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

Question 1mediummultiple choice

Full question →

A
Reduce the minimum number of nodes to zero to allow scale-to-zero during low traffic.
Why wrong: Reducing min nodes would increase cold start latency and not help during peak traffic.
B
Place a Google Cloud Load Balancer in front of the Vertex AI endpoint to distribute requests across multiple endpoints.
Why wrong: Vertex AI Prediction endpoints already have built-in load balancing; an external load balancer adds complexity without benefit.
C
Configure horizontal autoscaling with a higher maximum number of nodes and set a CPU utilization target.
Autoscaling allows the endpoint to add nodes during high traffic, reducing latency and preventing timeouts.
D
Implement A/B testing by splitting traffic between two model versions to distribute load.
Why wrong: A/B testing is for evaluating model performance, not for scaling to handle traffic spikes.

Full breakdown with real-world context →

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

✓

Configure horizontal autoscaling with a higher maximum number of nodes and set a CPU utilization target.

Option C is correct because configuring horizontal autoscaling with a higher maximum number of nodes and a CPU utilization target allows Vertex AI Prediction to automatically add more nodes during peak traffic, distributing the inference load and reducing latency. This directly addresses the root cause—insufficient compute resources under high demand—without requiring architectural changes or sacrificing availability.

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.

✗
Reduce the minimum number of nodes to zero to allow scale-to-zero during low traffic.
Why it's wrong here
Reducing min nodes would increase cold start latency and not help during peak traffic.
✗
Place a Google Cloud Load Balancer in front of the Vertex AI endpoint to distribute requests across multiple endpoints.
Why it's wrong here
Vertex AI Prediction endpoints already have built-in load balancing; an external load balancer adds complexity without benefit.
✓
Configure horizontal autoscaling with a higher maximum number of nodes and set a CPU utilization target.
Why this is correct
Autoscaling allows the endpoint to add nodes during high traffic, reducing latency and preventing timeouts.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Implement A/B testing by splitting traffic between two model versions to distribute load.
Why it's wrong here
A/B testing is for evaluating model performance, not for scaling to handle traffic spikes.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse load balancing (Option B) with autoscaling, thinking that distributing requests across multiple endpoints is the same as adding more compute capacity, but Vertex AI endpoints are single resources that cannot be fronted by a load balancer to increase capacity—they require autoscaling to add nodes.

Detailed technical explanation

How to think about this question

Vertex AI Prediction uses the `autoscalingMetricSpecs` configuration to scale based on CPU utilization (e.g., target 60%), and the `maxReplicaCount` parameter caps the maximum nodes. Under the hood, the Vertex AI Prediction service monitors the CPU utilization of the serving container and triggers scaling events via the Kubernetes-based infrastructure, adding nodes up to the configured maximum. In a real-world scenario, if the model is I/O-bound (e.g., heavy feature engineering), scaling on CPU alone may be insufficient; you might also need to tune the `minReplicaCount` to avoid cold starts and consider using a custom container with optimized threading.

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

An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.

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.

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FAQ

Questions learners often ask

What does this PDE question test?

Operationalizing machine learning models — This question tests Operationalizing machine learning models — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Configure horizontal autoscaling with a higher maximum number of nodes and set a CPU utilization target. — Option C is correct because configuring horizontal autoscaling with a higher maximum number of nodes and a CPU utilization target allows Vertex AI Prediction to automatically add more nodes during peak traffic, distributing the inference load and reducing latency. This directly addresses the root cause—insufficient compute resources under high demand—without requiring architectural changes or sacrificing availability.

What should I do if I get this PDE 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: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Same concept, more angles

3 more ways this is tested on PDE

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. A company deploys a machine learning model on Vertex AI for online predictions. The model experiences intermittent spikes in traffic, causing latency increases. Which strategy should the company use to ensure consistent low latency during traffic spikes?

easy

✓ A.Enable autoscaling on the Vertex AI endpoint with appropriate min and max nodes.
B.Manually scale the deployed model to a larger machine type during peak hours.
C.Reduce the number of prediction nodes to minimize overhead.
D.Switch to batch prediction to handle all requests asynchronously.

Why A: Vertex AI endpoints support autoscaling, which dynamically adjusts the number of prediction nodes based on incoming traffic. By setting appropriate min and max nodes, the endpoint can scale up during traffic spikes to maintain low latency and scale down during low traffic to reduce costs. This ensures consistent performance without manual intervention.

Variation 2. A company deploys a machine learning model to Vertex AI for real-time predictions. After deployment, they notice that prediction latency spikes during peak traffic hours. Which approach should they take to reduce latency without sacrificing accuracy?

medium

✓ A.Configure auto-scaling with higher min and max instances
B.Reduce the number of input features
C.Switch from online to batch prediction
D.Use a larger machine type for the model

Why A: Option A is correct because configuring auto-scaling with higher min and max instances ensures that Vertex AI has sufficient pre-warmed replicas to handle traffic spikes without cold-start latency. This approach maintains model accuracy because it does not alter the model architecture or inference logic, only the infrastructure capacity.

Variation 3. A healthcare company deploys a model for diagnosing medical images on Vertex AI using a custom container with a TensorFlow model. The model uses a mixture of GPUs (NVIDIA T4) and CPUs. After deployment, you notice that prediction latency is highly variable: sometimes under 100ms, sometimes over 10 seconds. Investigation shows that the variability correlates with the number of concurrent requests. The endpoint has a min replicas of 1 and max replicas of 3, with target CPU utilization set to 80%. You also observe that GPU utilization remains low (<20%) even during high load. What is the most likely cause of the latency variability? A) The model is not fully utilizing GPUs due to inefficient data loading from CPU. B) The autoscaling metric (CPU utilization) is not appropriate for a GPU-bound workload; the endpoint does not scale based on GPU utilization. C) The GPU machine type is too small for the model. D) The container is not configured to use the GPU correctly.

hard

✓ A.The autoscaling metric (CPU utilization) is not appropriate for a GPU-bound workload; the endpoint does not scale based on GPU utilization.
B.The model is not fully utilizing GPUs due to inefficient data loading from CPU.
C.The container is not configured to use the GPU correctly.
D.The GPU machine type is too small for the model.

Why A: Option B is correct because Vertex AI scales based on CPU utilization by default, but GPU-bound workloads may have low CPU utilization, causing autoscaling not trigger. Thus, during high load, the single replica is overwhelmed, causing high latency. Option A (inefficient data loading) could contribute but is not the primary cause. Option C (GPU too small) would cause consistently high latency. Option D (GPU not configured) would cause continuous errors, not variable latency.

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