- A
Enable batching of multiple queries into a single request
Batching increases payload size per request, reducing per-query overhead and improving throughput/latency.
- B
Convert the model to ONNX and use ONNX Runtime
Why wrong: ONNX Runtime can speed up inference, but the latency issue is likely due to request overhead, not model compute.
- C
Switch from gRPC to REST with HTTP/2
Why wrong: Both support HTTP/2; gRPC is binary and typically faster. The issue is likely overhead from stream establishment.
- D
Use a larger instance type with more CPU
Why wrong: Compute power may not be the bottleneck; more CPU adds cost without addressing protocol overhead.
Question 736 of 1,000
AI0-001 AI Infrastructure and Technologies Practice Question
This AI0-001 practice question tests your understanding of ai infrastructure and technologies. 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 team is deploying a BERT-based question-answering model using a REST API endpoint with gRPC for internal microservices. They notice high latency for small payloads. Which optimization is MOST likely to reduce latency?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Enable batching of multiple queries into a single request
Batching multiple queries into a single request reduces the overhead of repeated gRPC connection setup, serialization, and network round trips for small payloads. This amortizes the fixed cost of each inference call across several queries, directly lowering per-query latency in high-throughput scenarios.
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.
- ✓
Enable batching of multiple queries into a single request
Why this is correct
Batching increases payload size per request, reducing per-query overhead and improving throughput/latency.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Convert the model to ONNX and use ONNX Runtime
Why it's wrong here
ONNX Runtime can speed up inference, but the latency issue is likely due to request overhead, not model compute.
- ✗
Switch from gRPC to REST with HTTP/2
Why it's wrong here
Both support HTTP/2; gRPC is binary and typically faster. The issue is likely overhead from stream establishment.
- ✗
Use a larger instance type with more CPU
Why it's wrong here
Compute power may not be the bottleneck; more CPU adds cost without addressing protocol overhead.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the misconception that model optimization (ONNX) or hardware upgrades are the default fix for latency, when the real bottleneck for small payloads is network and serialization overhead, which batching directly mitigates.
Detailed technical explanation
How to think about this question
gRPC uses HTTP/2 multiplexing, which allows multiple streams over a single TCP connection, but each small request still incurs the overhead of Protobuf serialization and framing. Batching combines multiple queries into a single Protobuf message, reducing the number of RPC calls and leveraging gRPC's streaming capabilities more efficiently. In practice, services like TensorFlow Serving and NVIDIA Triton Inference Server use batching to achieve up to 10x latency reduction for small payloads.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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 AI0-001 question test?
AI Infrastructure and Technologies — This question tests AI Infrastructure and Technologies — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable batching of multiple queries into a single request — Batching multiple queries into a single request reduces the overhead of repeated gRPC connection setup, serialization, and network round trips for small payloads. This amortizes the fixed cost of each inference call across several queries, directly lowering per-query latency in high-throughput scenarios.
What should I do if I get this AI0-001 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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.
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