The answer is to set up autoscaling with a minimum number of replicas and to use batch prediction for asynchronous workloads. Autoscaling with a minimum replica count prevents excessive scaling costs by ensuring the service doesn't spin up unnecessary instances during low traffic, while batch prediction is far more cost-effective than online prediction because it processes large volumes asynchronously and automatically scales down to zero when idle, eliminating wasted compute spend. On the Google Cloud Generative AI Leader exam, this question tests your ability to distinguish between real-time and batch inference cost models, a common trap being the assumption that more replicas always improve performance. Remember the memory tip: for cost optimization on Vertex AI GenAI, think "batch for bulk, autoscale for burst."
Generative AI Leader Practice Question: Business Strategies for Generative AI Solutions
This Generative AI Leader practice question tests your understanding of business strategies for generative ai solutions. 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 are recommended best practices for cost optimization when deploying generative AI models on Vertex AI?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use batch prediction for non-real-time workloads
Option A is correct because batch prediction processes predictions asynchronously in large batches, which is significantly more cost-effective than online (real-time) prediction for workloads that do not require immediate responses. Vertex AI batch prediction jobs automatically scale down to zero when not in use, eliminating idle compute costs, and you only pay for the resources consumed during the job execution.
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.
GPU instances are more expensive; use CPU for latency-tolerant workloads.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that single-region deployment always reduces costs, when in reality it can increase network egress charges and latency penalties for global users, making multi-region strategies with traffic management more cost-effective.
Detailed technical explanation
How to think about this question
Batch prediction on Vertex AI leverages distributed processing across multiple worker nodes, automatically sharding input data and aggregating results, which reduces per-prediction cost by up to 80% compared to online prediction for high-volume workloads. The service uses preemptible VMs by default for batch jobs, further lowering costs, and supports custom machine types to match workload requirements precisely. In practice, a company processing millions of customer support queries weekly would use batch prediction to run inference overnight, taking advantage of lower spot VM pricing and avoiding the need to maintain always-on endpoints.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
What does this Generative AI Leader question test?
Business Strategies for Generative AI Solutions — This question tests Business Strategies for Generative AI Solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use batch prediction for non-real-time workloads — Option A is correct because batch prediction processes predictions asynchronously in large batches, which is significantly more cost-effective than online (real-time) prediction for workloads that do not require immediate responses. Vertex AI batch prediction jobs automatically scale down to zero when not in use, eliminating idle compute costs, and you only pay for the resources consumed during the job execution.
What should I do if I get this Generative AI Leader 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: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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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