- A
A training approach using multiple human experts to annotate data for different domains
Why wrong: Human expert annotation is a data labelling strategy — MoE is a neural network architecture with dynamic expert routing.
- B
An architecture with many specialised sub-networks that only activates a few per token — enabling efficient large models
MoE activates few experts per forward pass — achieving large model capacity at lower per-inference compute cost.
- C
Combining predictions from multiple separately trained AI models at inference time
Why wrong: Combining separate models is ensemble learning — MoE is a single model architecture with internal expert routing.
- D
A training technique where multiple ML experts review and validate model outputs
Why wrong: Human expert review is quality assurance — MoE is an architectural technique for efficient neural network computation.
What Is Mixture of Experts (MoE) Architecture?
This AI-900 practice question tests your understanding of describe features of generative ai workloads on azure. 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.
What is 'mixture of experts' (MoE) architecture and how does it relate to efficient LLMs?
Quick Answer
The correct answer is that Mixture of Experts (MoE) architecture enables efficient LLMs by using many specialized sub-networks, or experts, but activating only a small subset of them per input token via a gating mechanism. This design allows the model to have a massive total parameter count—often in the trillions—while keeping the computational cost per token low, because only the active experts are computed during inference. On the Azure AI-900 exam, this concept tests your understanding of how Microsoft scales models like GPT-4 efficiently without proportionally increasing hardware demands; a common trap is confusing total parameters with active parameters. Remember that MoE is like having a huge team of specialists where only the relevant few work on each task, saving energy and time. A useful memory tip: “Many experts, few active—efficiency is attractive.”
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
An architecture with many specialised sub-networks that only activates a few per token — enabling efficient large models
Mixture of Experts (MoE) architecture splits the model into multiple specialized sub-networks (experts) and uses a gating mechanism to activate only a small subset of experts per input token. This allows the model to have a very large total parameter count while keeping the computational cost per token low, making it highly efficient for scaling large language models (LLMs) without proportionally increasing inference cost.
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.
- ✗
A training approach using multiple human experts to annotate data for different domains
Why it's wrong here
Human expert annotation is a data labelling strategy — MoE is a neural network architecture with dynamic expert routing.
- ✓
An architecture with many specialised sub-networks that only activates a few per token — enabling efficient large models
Why this is correct
MoE activates few experts per forward pass — achieving large model capacity at lower per-inference compute cost.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Combining predictions from multiple separately trained AI models at inference time
Why it's wrong here
Combining separate models is ensemble learning — MoE is a single model architecture with internal expert routing.
- ✗
A training technique where multiple ML experts review and validate model outputs
Why it's wrong here
Human expert review is quality assurance — MoE is an architectural technique for efficient neural network computation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse MoE with ensemble methods (option C) because both involve multiple 'experts,' but MoE uses a single model with sparse activation per token, not combining outputs from independently trained models.
Detailed technical explanation
How to think about this question
Under the hood, MoE replaces dense feed-forward layers with multiple expert networks and a trainable router that computes a sparse gating probability over experts, typically using top-k selection (e.g., k=2). This sparsity introduces load-balancing challenges, often addressed by auxiliary losses to ensure uniform expert utilization, and can lead to expert collapse if not carefully tuned. In practice, models like Mixtral 8x7B use 8 experts per MoE layer, activating only 2 per token, achieving performance comparable to a dense 70B model with inference cost closer to a 12B model.
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
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: An architecture with many specialised sub-networks that only activates a few per token — enabling efficient large models — Mixture of Experts (MoE) architecture splits the model into multiple specialized sub-networks (experts) and uses a gating mechanism to activate only a small subset of experts per input token. This allows the model to have a very large total parameter count while keeping the computational cost per token low, making it highly efficient for scaling large language models (LLMs) without proportionally increasing inference cost.
What should I do if I get this AI-900 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.
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Last reviewed: Jun 11, 2026
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