- A
Switch to a larger model (e.g., Gemini 1.5 Pro) to improve response quality and reduce irrelevant details
Why wrong: A larger model would likely increase inference time and cost, worsening the existing problems.
- B
Increase the Vertex AI endpoint's maximum request quota to handle concurrent requests
Why wrong: Increasing quota does not reduce latency per request or address cost; it only allows more concurrent requests.
- C
Apply model quantization (e.g., INT8) to reduce model size and inference time
Why wrong: Quantization can reduce latency but may degrade accuracy, especially for nuanced clinical notes, and is more disruptive to implement.
- D
Implement response caching for common queries and batch process similar requests
Caching reduces redundant computations, and batching improves throughput, together cutting latency and cost.
Generative AI Leader Fundamentals of Generative AI Practice Question
This Generative AI Leader practice question tests your understanding of fundamentals of generative ai. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 healthcare company is using Vertex AI to build a generative AI assistant that helps doctors draft clinical notes. The assistant uses a fine-tuned PaLM 2 model deployed on a private endpoint. Recently, doctors have reported that the assistant takes over 30 seconds to respond, causing workflow delays. Additionally, the monthly Vertex AI costs have increased by 40% without a proportional increase in usage. The model responses are generally accurate but sometimes include irrelevant details. The company wants to improve response time and cost while maintaining acceptable quality. A review of logs shows that most requests are for similar note types (e.g., progress notes, discharge summaries) and that the same prompt is used repeatedly with minor variations. What should the company do first?
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.
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
Implement response caching for common queries and batch process similar requests
Option D is correct because the logs show that most requests are for similar note types with repeated prompts, making response caching ideal for reducing latency and cost. Caching stores responses for identical or near-identical queries, eliminating redundant inference calls, which directly addresses the 30-second response time and 40% cost increase without sacrificing quality.
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.
- ✗
Switch to a larger model (e.g., Gemini 1.5 Pro) to improve response quality and reduce irrelevant details
Why it's wrong here
A larger model would likely increase inference time and cost, worsening the existing problems.
- ✗
Increase the Vertex AI endpoint's maximum request quota to handle concurrent requests
Why it's wrong here
Increasing quota does not reduce latency per request or address cost; it only allows more concurrent requests.
- ✗
Apply model quantization (e.g., INT8) to reduce model size and inference time
Why it's wrong here
Quantization can reduce latency but may degrade accuracy, especially for nuanced clinical notes, and is more disruptive to implement.
- ✓
Implement response caching for common queries and batch process similar requests
Why this is correct
Caching reduces redundant computations, and batching improves throughput, together cutting latency and cost.
Clue confirmation
The clue word "first" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse performance optimization techniques (quantization, quota increases) with the root cause of redundant requests, leading them to pick options that address symptoms rather than the fundamental pattern of repeated prompts.
Detailed technical explanation
How to think about this question
Response caching in Vertex AI leverages a key-value store (e.g., Cloud Memorystore or a local cache) to map prompt hashes to precomputed outputs, bypassing the model for repeated queries. Batch processing groups similar requests (e.g., progress notes with minor variations) into a single inference call, reducing per-request overhead and maximizing GPU utilization. This approach is especially effective in healthcare settings where templates are reused, cutting latency from 30 seconds to milliseconds for cached responses.
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.
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Fundamentals of Generative AI — This question tests Fundamentals of Generative AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Implement response caching for common queries and batch process similar requests — Option D is correct because the logs show that most requests are for similar note types with repeated prompts, making response caching ideal for reducing latency and cost. Caching stores responses for identical or near-identical queries, eliminating redundant inference calls, which directly addresses the 30-second response time and 40% cost increase without sacrificing quality.
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: "first". Order matters here. You are being tested on which action comes before the others — not which action is generally useful.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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