- A
Searching the web for neural network architectures published in research papers
Why wrong: Literature search is research methodology — NAS is automated algorithmic exploration of model architecture configurations.
- B
Automating the discovery of optimal neural network architectures using computational search
NAS searches the space of possible architectures computationally — finding better network designs than human experts alone.
- C
Querying a database of pre-built neural networks to find the closest match for a task
Why wrong: Model repository search is a manual selection approach — NAS generates and evaluates novel architectures through automated search.
- D
A legal search process for patenting new AI model architectures
Why wrong: Patent searches are legal processes — NAS is a computational AI research technique for automated model design.
Quick Answer
The correct answer is that neural architecture search (NAS) automates the discovery of optimal neural network architectures using computational search. This is correct because NAS employs methods like reinforcement learning or evolutionary algorithms to systematically explore and evaluate different network topologies, removing the manual trial-and-error of designing layers, nodes, and connections. On the Microsoft Azure AI-900 exam, this concept tests your understanding of how AutoML extends beyond simple hyperparameter tuning to include the automated design of the model’s structure itself—a common trap is confusing NAS with just tuning learning rates or batch sizes. Remember that NAS focuses on the “blueprint” of the network, not just the knobs and dials. A helpful memory tip: think of NAS as an architect automatically drawing the floor plan of a neural network, while AutoML is the general contractor handling the entire building process.
AI-900 Practice Question: Describe fundamental principles of machine learning on Azure
This AI-900 practice question tests your understanding of describe fundamental principles of machine learning 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 'neural architecture search' (NAS) and how does it relate to AutoML?
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
Automating the discovery of optimal neural network architectures using computational search
Neural Architecture Search (NAS) is an automated process that uses computational search methods—such as reinforcement learning, evolutionary algorithms, or gradient-based optimization—to discover optimal neural network architectures for a given task. It is a key component of AutoML because AutoML aims to automate the entire machine learning pipeline, including model selection and hyperparameter tuning, and NAS specifically automates the design of the neural network topology itself.
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.
- ✗
Searching the web for neural network architectures published in research papers
Why it's wrong here
Literature search is research methodology — NAS is automated algorithmic exploration of model architecture configurations.
- ✓
Automating the discovery of optimal neural network architectures using computational search
Why this is correct
NAS searches the space of possible architectures computationally — finding better network designs than human experts alone.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Querying a database of pre-built neural networks to find the closest match for a task
Why it's wrong here
Model repository search is a manual selection approach — NAS generates and evaluates novel architectures through automated search.
- ✗
A legal search process for patenting new AI model architectures
Why it's wrong here
Patent searches are legal processes — NAS is a computational AI research technique for automated model design.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse NAS with simply searching for existing models online or in a database, rather than understanding it as an automated, generative search process that creates new architectures.
Detailed technical explanation
How to think about this question
Under the hood, NAS typically defines a search space of possible operations (e.g., convolution, pooling, skip connections) and uses a controller (e.g., an RNN or a Bayesian optimizer) to propose architectures, which are then trained and evaluated on a validation set. A real-world scenario is Google's use of NAS to discover the EfficientNet family, which achieved state-of-the-art accuracy with significantly fewer parameters than manually designed networks. The search can be computationally expensive, often requiring thousands of GPU-hours, which is why techniques like weight-sharing (ENAS) or one-shot models are used to reduce cost.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe fundamental principles of machine learning on Azure — This question tests Describe fundamental principles of machine learning on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Automating the discovery of optimal neural network architectures using computational search — Neural Architecture Search (NAS) is an automated process that uses computational search methods—such as reinforcement learning, evolutionary algorithms, or gradient-based optimization—to discover optimal neural network architectures for a given task. It is a key component of AutoML because AutoML aims to automate the entire machine learning pipeline, including model selection and hyperparameter tuning, and NAS specifically automates the design of the neural network topology itself.
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
This AI-900 practice question is part of Courseiva's free Microsoft 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 AI-900 exam.
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