- A
A statistical method for updating model confidence as new training data arrives
Why wrong: Bayesian updating of model beliefs is Bayesian inference — Bayesian optimisation applies probabilistic modelling to find optimal hyperparameters.
- B
A smart hyperparameter search that uses past trial results to select promising configurations
Bayesian optimisation builds a surrogate model of performance vs. hyperparameters — selecting next configs based on where improvement is likely.
- C
An automatic method for adjusting learning rate during training based on gradient information
Why wrong: Adaptive learning rate methods (Adam, Adagrad) are optimisers — Bayesian optimisation finds hyperparameters, not gradient-based parameter updates.
- D
A probabilistic approach to labelling uncertain training examples
Why wrong: Probabilistic labelling is related to active learning — Bayesian optimisation specifically searches the hyperparameter space.
Quick Answer
The answer is a smart hyperparameter search that uses past trial results to select promising configurations. Bayesian optimisation builds a probabilistic model, typically a Gaussian process, of the objective function from previous evaluations, then applies an acquisition function like Expected Improvement to balance exploration and exploitation. This targeted approach makes it far more efficient than grid or random search, especially for expensive-to-train models. On the AI-900 exam, this concept tests your understanding of how Azure Automated Machine Learning intelligently tunes hyperparameters; a common trap is confusing it with random search, which lacks memory of past results. Remember the key difference: Bayesian optimisation learns from history, while random search does not. For a memory tip, think of it as a “smart explorer” that updates its map after every step, rather than wandering blindly.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 'Bayesian optimisation' in hyperparameter tuning?
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
A smart hyperparameter search that uses past trial results to select promising configurations
Bayesian optimisation is a smart hyperparameter search method that builds a probabilistic model (typically a Gaussian process) of the objective function based on past trial results. It uses an acquisition function (e.g., Expected Improvement) to balance exploration and exploitation, selecting the most promising hyperparameter configurations to evaluate next. This makes it far more efficient than grid or random search for expensive-to-evaluate models.
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 statistical method for updating model confidence as new training data arrives
Why it's wrong here
Bayesian updating of model beliefs is Bayesian inference — Bayesian optimisation applies probabilistic modelling to find optimal hyperparameters.
- ✓
A smart hyperparameter search that uses past trial results to select promising configurations
Why this is correct
Bayesian optimisation builds a surrogate model of performance vs. hyperparameters — selecting next configs based on where improvement is likely.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
An automatic method for adjusting learning rate during training based on gradient information
Why it's wrong here
Adaptive learning rate methods (Adam, Adagrad) are optimisers — Bayesian optimisation finds hyperparameters, not gradient-based parameter updates.
- ✗
A probabilistic approach to labelling uncertain training examples
Why it's wrong here
Probabilistic labelling is related to active learning — Bayesian optimisation specifically searches the hyperparameter space.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Bayesian optimisation with Bayesian inference for model parameters (Option A) or with adaptive learning rate algorithms (Option C), because both involve 'Bayesian' or 'optimisation' terminology but serve entirely different purposes.
Detailed technical explanation
How to think about this question
Under the hood, Bayesian optimisation models the objective function as a Gaussian process with a prior mean and kernel (e.g., Matérn 5/2) to capture smoothness. The acquisition function, such as Expected Improvement (EI) or Upper Confidence Bound (UCB), quantifies the potential gain of evaluating a new configuration, allowing the algorithm to focus on regions likely to yield better results. In practice, this is crucial for tuning deep learning models where each trial can take hours, as it reduces the number of required evaluations from hundreds to tens.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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: A smart hyperparameter search that uses past trial results to select promising configurations — Bayesian optimisation is a smart hyperparameter search method that builds a probabilistic model (typically a Gaussian process) of the objective function based on past trial results. It uses an acquisition function (e.g., Expected Improvement) to balance exploration and exploitation, selecting the most promising hyperparameter configurations to evaluate next. This makes it far more efficient than grid or random search for expensive-to-evaluate models.
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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