AI Model Deployment Strategies: Canary and A/B Testing
Which TWO deployment strategies allow for testing a new model version before fully rolling it out?
Quick Answer
The answer is canary deployment and A/B testing with traffic splitting. Both strategies allow you to test a new model version on a subset of live traffic before committing to a full rollout, reducing the blast radius of potential failures. Canary deployment works by routing a small percentage of real-world requests to the new model while the stable version handles the majority, enabling validation under production load. A/B testing similarly splits traffic between the old and new models, but it is often used for comparing specific performance metrics or business outcomes. On the CompTIA AI+ AI0-001 exam, this concept tests your understanding of risk mitigation in MLOps, and a common trap is confusing blue-green deployment (which swaps entire environments) with these incremental strategies. A helpful memory tip: think of a canary in a coal mine—it tests the air quality first—and A/B as a direct side-by-side comparison of two versions.
⚠ Common exam trap
It's easy for candidates to confuse shadow deployment with canary deployment, mistakenly thinking shadow also tests user-facing behavior, when in fact shadow only tests infrastructure impact without validating model outputs against live user expectations.
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
✓
Canary deployment
Canary deployment is correct because it routes a small percentage of live traffic to the new model version while the majority continues using the stable version. This allows real-world validation of the new model's performance and error rates under production load before a full rollout, minimizing blast radius if issues arise.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Shadow deployment
Why it's wrong here
Shadow deployment duplicates traffic to the new version but does not serve user requests, so it does not test real behavior.
- ✓
Canary deployment
Why this is correct
Canary releases route a subset of users to the new version for validation.
- ✗
Direct cutover
Why it's wrong here
Direct cutover replaces the old version instantly with no testing period.
- ✓
A/B testing with traffic splitting
Why this is correct
A/B testing directs a percentage of traffic to each version to compare performance.
- ✗
Blue-Green deployment
Why it's wrong here
Blue-Green switches all traffic at once, not gradual testing.
Quick reference
AAA Protocol Comparison
| Protocol | Port(s) | Encryption | Transport | Primary Use |
|---|---|---|---|---|
| RADIUS | 1812 / 1813 | Password only | UDP | Network access control |
| TACACS+ | 49 | Full packet | TCP | Device administration |
| Diameter | 3868 | Full session | TCP / SCTP | Carrier / mobile networks |
| 802.1X | — | EAP-based | Layer 2 | Port-based access control |
TACACS+ encrypts the entire packet; RADIUS only encrypts the password field — a key exam distinction.
About these practice questions
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Same concept, more angles
2 more ways this is tested on AI0-001
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. An MLOps team uses a CI/CD pipeline to automate model retraining. The pipeline triggers on new labeled data, runs feature engineering, retrains the model, evaluates against a holdout set, and deploys if metrics exceed thresholds. Recently, a retrained model passed validation but caused a 5% accuracy drop in production. Which improvement best prevents this?
hard- ✓ A.Implement canary deployment with shadow scoring to compare with current model
- B.Require manual approval before deployment
- C.Use the entire production dataset for validation instead of a holdout set
- D.Increase the amount of training data used in each retraining cycle
Why A: Canary deployment with shadow scoring allows the new model to serve predictions to a small subset of traffic while comparing its outputs against the current production model in real time, without affecting all users. This catches subtle data drift or concept drift that a static holdout set may miss, preventing the 5% accuracy drop from reaching full production.
Variation 2. An MLOps team automates model deployment with a CI/CD pipeline. A performance regression is detected after deploying a new model version. The team needs to automatically roll back to the previous version. Which approach best enables safe automated rollback?
hard- ✓ A.Use a blue/green deployment with automated health checks and traffic switching
- B.Maintain a manual rollback script that the operations team can run
- C.Deploy new models as canary releases and monitor for 24 hours
- D.Automatically keep the previous model version in storage for later use
Why A: Blue/green deployment with automated health checks and traffic switching is the best approach because it allows the team to instantly route all traffic back to the previous (green) environment if the new (blue) version fails health checks. This ensures zero-downtime rollback without manual intervention, directly addressing the need for safe automated rollback in a CI/CD pipeline.
JA
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.