- A
Implement response caching for common queries
Caching reduces latency and cost by reusing responses for identical requests.
- B
Enable auto-scaling for the serving infrastructure
Auto-scaling adjusts resources based on demand, ensuring performance during traffic spikes.
- C
Reduce the input context length to the absolute minimum
Why wrong: Excessively reducing context length can degrade quality; it should be optimized but not minimized arbitrarily.
- D
Set up monitoring and alerting on latency metrics
Monitoring ensures that latency stays within SLOs and enables proactive troubleshooting.
- E
Use a single, large instance to handle all traffic
Why wrong: A single instance may become a bottleneck and is not cost-efficient for variable traffic.
Question 966 of 997
Generative AI Leader Applying Generative AI in Business Practice Question
This Generative AI Leader practice question tests your understanding of applying generative ai in business. 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.
A company is moving a GenAI proof-of-concept to production. They need to ensure the system can handle variable traffic and maintain low latency. Which THREE practices should they implement? (Choose 3)
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
Option A is correct because response caching stores the outputs of frequently requested queries, allowing the system to serve them instantly without recomputation. This drastically reduces latency for repeated requests and offloads the underlying model, which is critical for maintaining responsiveness under variable traffic patterns in production.
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.
- ✓
Implement response caching for common queries
Why this is correct
Caching reduces latency and cost by reusing responses for identical requests.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Enable auto-scaling for the serving infrastructure
Why this is correct
Auto-scaling adjusts resources based on demand, ensuring performance during traffic spikes.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Reduce the input context length to the absolute minimum
Why it's wrong here
Excessively reducing context length can degrade quality; it should be optimized but not minimized arbitrarily.
- ✓
Set up monitoring and alerting on latency metrics
Why this is correct
Monitoring ensures that latency stays within SLOs and enables proactive troubleshooting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a single, large instance to handle all traffic
Why it's wrong here
A single instance may become a bottleneck and is not cost-efficient for variable traffic.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that minimizing input context length universally improves performance, ignoring the trade-off with output quality, and that a single large instance is simpler and sufficient for production traffic, overlooking scalability and fault tolerance requirements.
Detailed technical explanation
How to think about this question
Response caching typically uses an in-memory store like Redis or Memcached with a time-to-live (TTL) policy to balance freshness and performance. Auto-scaling relies on metrics such as request queue depth or CPU utilization, often implemented via Kubernetes Horizontal Pod Autoscaler or AWS Application Auto Scaling, which dynamically adjusts the number of inference endpoints based on predefined thresholds.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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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Applying Generative AI in Business — study guide chapter
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FAQ
Questions learners often ask
What does this Generative AI Leader question test?
Applying Generative AI in Business — This question tests Applying Generative AI in Business — 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 — Option A is correct because response caching stores the outputs of frequently requested queries, allowing the system to serve them instantly without recomputation. This drastically reduces latency for repeated requests and offloads the underlying model, which is critical for maintaining responsiveness under variable traffic patterns in production.
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.
About these practice questions
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Last reviewed: Jul 4, 2026
This Generative AI Leader 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 Generative AI Leader exam.
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