AI0-001 Implementing AI Solutions Practice Question
A team is deploying an AI microservice for real-time object detection in streaming video. Which TWO integration patterns are most appropriate? (Choose two.)
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
✓
Streaming responses for real-time inference
Option A (Streaming responses for real-time inference) is correct because object detection on live video requires continuous, low-latency output as frames arrive, and streaming responses (e.g., gRPC server-streaming or HTTP chunked/SSE) let the service emit detection results incrementally instead of waiting for a full batch to complete. Option E (AI microservice architecture) is correct because packaging the detection model as an independently deployable microservice allows separate scaling, GPU resource allocation, and model versioning without affecting the rest of the streaming pipeline. Option B (Batch processing with nightly jobs) is wrong because nightly jobs introduce hours of latency, which is incompatible with real-time video analytics. Option C (Synchronous request-response with long timeouts) is wrong because long timeouts block callers and cannot sustain the continuous frame-by-frame throughput that streaming video demands. Option D (Monolithic application deployment) is wrong because a monolith couples the detection workload to unrelated components, preventing independent scaling and rapid model updates needed for real-time inference.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Streaming responses for real-time inference
Why this is correct
Streaming responses push tokens or detection results incrementally as they are produced, rather than buffering a complete payload. This satisfies the real-time constraint: the video pipeline receives bounding-box output with minimal latency, keeping inference aligned with the live stream.
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Batch processing with nightly jobs
Why it's wrong here
Nightly batch jobs process recorded footage after the fact, so detections cannot inform live streams within the required latency. Batch is correct for offline analytics such as post-event forensic review or daily aggregation, where freshness of results is not operationally critical.
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Synchronous request-response with long timeouts
Why it's wrong here
Long timeouts hold a connection open per frame, so throughput collapses under continuous video and a stalled detector blocks the caller. Synchronous request-response suits discrete, low-latency queries such as a single image classification call, where the client waits briefly for one bounded result.
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Monolithic application deployment
Why it's wrong here
Monolithic deployment bundles the detector into one process, preventing independent scaling of ingest and inference tiers during variable video load. It suits small single-team applications with one release cadence, not a microservice needing per-component elasticity and separate deployment pipelines.
- ✓
AI microservice architecture
Why this is correct
An AI microservice architecture isolates the detection model behind its own independently deployable, scalable service. This satisfies the real-time constraint by allowing dedicated GPU-backed replicas to scale horizontally and be tuned for low-latency throughput without coupling to other application components.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.