The answer is to use a smaller batch size for prediction requests and to deploy the endpoint with GPU accelerators. A smaller batch size reduces the amount of data processed per inference cycle, which directly lowers per-request latency because the model spends less time accumulating and processing a large batch before returning results. GPUs, meanwhile, excel at the parallel matrix operations central to deep learning, enabling the endpoint to handle multiple predictions concurrently far faster than CPU-only machines. On the Google Professional Data Engineer exam, this question tests your understanding of optimizing Vertex AI serving infrastructure for latency-sensitive workloads—a common trap is assuming larger batches always improve throughput, but they actually increase latency for individual requests. Remember the mnemonic “Small Batch, Fast Match” to recall that reducing batch size and matching hardware (GPU) to model type are the two key levers for cutting endpoint latency.
PDE Operationalizing machine learning models Practice Question
This PDE practice question tests your understanding of operationalizing machine learning 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.
Which TWO actions can help reduce the latency of a Vertex AI endpoint serving a large neural network model?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy the model on a machine type with GPU accelerators
Option D is correct because GPU accelerators are specifically designed to handle the parallel computations required by large neural networks, significantly reducing inference latency compared to CPU-only machines. Vertex AI endpoints with GPUs can process multiple predictions concurrently, which is critical for deep learning models where matrix operations dominate the workload.
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.
✗
Use a larger machine type with more CPU cores
Why it's wrong here
CPU scaling may not help as much as GPU.
✗
Enable model compression with quantization
Why it's wrong here
Quantization can reduce model size but may not reduce latency if not optimized.
✗
Increase the number of model versions deployed on the same endpoint
Why it's wrong here
More versions may increase routing complexity.
✓
Deploy the model on a machine type with GPU accelerators
Why this is correct
GPUs speed up neural network inference.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use a smaller batch size for prediction requests
Why this is correct
Smaller batches mean each request is processed faster.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that more CPU cores or model compression always reduce latency, but the trap here is that for large neural networks, the primary bottleneck is parallel compute capability, which only GPUs or TPUs can address effectively.
Detailed technical explanation
How to think about this question
Under the hood, GPU accelerators leverage thousands of CUDA cores to perform tensor operations in parallel, which is essential for large neural networks where a single forward pass involves billions of floating-point operations. In Vertex AI, using a GPU machine type like n1-standard-4 with an attached T4 or V100 GPU can reduce inference latency by orders of magnitude compared to CPU-only instances, especially for batch sizes larger than 1. A real-world scenario is deploying a BERT-based NLP model: on a CPU, a single prediction might take 500ms, while on a GPU it can drop to under 50ms, making real-time applications feasible.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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.
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: Deploy the model on a machine type with GPU accelerators — Option D is correct because GPU accelerators are specifically designed to handle the parallel computations required by large neural networks, significantly reducing inference latency compared to CPU-only machines. Vertex AI endpoints with GPUs can process multiple predictions concurrently, which is critical for deep learning models where matrix operations dominate the workload.
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.
What is the key concept behind this question?
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 →
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. Which TWO actions can help reduce prediction latency for a Vertex AI endpoint?
easy
A.Increase the number of features
✓ B.Optimize the model architecture to reduce size
✓ C.Use a custom prediction container with optimized dependencies
D.Use a larger machine type with more vCPUs
E.Set min replicas to 0 to save cost
Why B: Optimizing the model architecture to reduce size directly decreases the computational load during inference, which lowers prediction latency. Smaller models require fewer floating-point operations (FLOPs) per prediction, enabling faster response times on Vertex AI endpoints.
Last reviewed: Jun 30, 2026
Question Discussion
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This PDE 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 PDE exam.
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