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 of the following is a key consideration when selecting a GenAI model for a cost-sensitive application?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Latency and throughput requirements
For cost-sensitive applications, latency and throughput requirements directly impact infrastructure costs, as lower latency often requires more expensive compute resources (e.g., higher GPU memory, faster inference hardware) and higher throughput may necessitate scaling out instances. Model size in parameters is a secondary factor that influences latency and throughput, but the primary cost driver is the operational performance needed to meet service-level agreements (SLAs).
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.
✗
Model size in parameters
Why it's wrong here
While model size influences cost, it is not the only factor; latency and throughput are more direct for cost-sensitive apps.
✓
Latency and throughput requirements
Why this is correct
Latency and throughput directly determine the infrastructure needed and thus the cost per inference.
Related concept
Read the scenario before looking for a memorised answer.
✗
Number of training epochs
Why it's wrong here
Training epochs are irrelevant for inference cost.
✗
The model's training data source
Why it's wrong here
Data source affects model performance, not operational cost.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Google Cloud often tests the misconception that model size (parameters) is the primary cost driver, but the exam emphasizes that operational metrics like latency and throughput are the direct determinants of infrastructure cost in production.
Detailed technical explanation
How to think about this question
Inference cost is dominated by the number of floating-point operations (FLOPs) per token and the hardware utilization rate; for example, a model requiring 10ms latency on a single A100 GPU may cost $0.002 per request, but scaling to 1000 requests per second would require multiple GPUs or optimized batching, dramatically increasing cost. Real-world scenarios like real-time chatbots versus batch summarization show that latency SLAs (e.g., p99 < 200ms) force trade-offs between model size, quantization (e.g., FP16 vs INT8), and deployment architecture (e.g., ONNX Runtime vs TensorRT).
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: Latency and throughput requirements — For cost-sensitive applications, latency and throughput requirements directly impact infrastructure costs, as lower latency often requires more expensive compute resources (e.g., higher GPU memory, faster inference hardware) and higher throughput may necessitate scaling out instances. Model size in parameters is a secondary factor that influences latency and throughput, but the primary cost driver is the operational performance needed to meet service-level agreements (SLAs).
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.
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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