- A
k=10 always produces a better model than k=5 because it uses more training data
Why wrong: More folds gives better estimation reliability, not necessarily better models — the same total data is used either way.
- B
k=10 provides more reliable performance estimates at 2x the compute cost vs k=5
More folds = less variance in the performance estimate, but more training runs — k=10 is more reliable but computationally costlier than k=5.
- C
k=5 and k=10 produce identical results because the total data is the same
Why wrong: More folds reduce variance in the performance estimate — k=10 gives a more reliable estimate than k=5 despite using the same data.
- D
k=10 requires 10 times more labelled data than k=5
Why wrong: Both work with the same dataset size — k determines how many times you split and train, not how much data you need.
Quick Answer
The answer is that k=10 provides more reliable performance estimates at roughly twice the computational cost of k=5. This difference stems from how k-fold cross-validation works: the dataset is split into k equal folds, with the model trained on k-1 folds and validated on the remaining fold, repeated k times. With k=10, each model trains on 90% of the data and validates on 10%, averaging results over ten runs to produce a lower-variance estimate; k=5 uses 80% training and 20% validation over only five runs, which is faster but more prone to variance in the performance metric. On the AI-900 exam, this concept tests your understanding of bias-variance trade-offs in model evaluation—a common trap is assuming more folds always improve accuracy, when in fact the gain is in reliability, not raw accuracy. A simple memory tip: “Ten folds, ten times the trust; five folds, half the cost.”
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 'k-fold cross-validation' specifically and how is k=10 different from k=5?
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
k=10 provides more reliable performance estimates at 2x the compute cost vs k=5
k-fold cross-validation splits the dataset into k equal folds, training on k-1 folds and validating on the remaining fold, repeating this process k times. With k=10, each model is trained on 90% of the data and validated on 10%, while k=5 uses 80% for training and 20% for validation. The key difference is that k=10 yields a performance estimate with lower variance (more reliable) because it averages over more folds, but it requires approximately twice the computational cost (10 training runs vs. 5).
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.
- ✗
k=10 always produces a better model than k=5 because it uses more training data
Why it's wrong here
More folds gives better estimation reliability, not necessarily better models — the same total data is used either way.
- ✓
k=10 provides more reliable performance estimates at 2x the compute cost vs k=5
Why this is correct
More folds = less variance in the performance estimate, but more training runs — k=10 is more reliable but computationally costlier than k=5.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
k=5 and k=10 produce identical results because the total data is the same
Why it's wrong here
More folds reduce variance in the performance estimate — k=10 gives a more reliable estimate than k=5 despite using the same data.
- ✗
k=10 requires 10 times more labelled data than k=5
Why it's wrong here
Both work with the same dataset size — k determines how many times you split and train, not how much data you need.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing model performance improvement with estimate reliability; candidates often think more folds always yield a better model, but cross-validation is about evaluating performance, not training the final model.
Detailed technical explanation
How to think about this question
Under the hood, k-fold cross-validation reduces overfitting bias in performance estimation by ensuring every data point appears in a validation set exactly once. With k=10, the bias-variance trade-off shifts: the estimate has lower variance (since each fold's validation set is smaller and more representative) but slightly higher bias due to more similar training sets, whereas k=5 has higher variance but lower computational cost. In practice, k=10 is a common default in Azure Machine Learning automated ML because it balances reliability and compute efficiency for moderate-sized datasets.
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 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: k=10 provides more reliable performance estimates at 2x the compute cost vs k=5 — k-fold cross-validation splits the dataset into k equal folds, training on k-1 folds and validating on the remaining fold, repeating this process k times. With k=10, each model is trained on 90% of the data and validated on 10%, while k=5 uses 80% for training and 20% for validation. The key difference is that k=10 yields a performance estimate with lower variance (more reliable) because it averages over more folds, but it requires approximately twice the computational cost (10 training runs vs. 5).
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
2 more ways this is tested on AI-900
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. What is 'cross-validation' and when should it be used in machine learning?
medium- A.Validating that a model works correctly across different Azure regions
- ✓ B.Dividing data into k folds and training k times to get a more reliable performance estimate
- C.Comparing two different models' predictions on the same test set
- D.Checking whether training labels are consistent across different human annotators
Why B: Cross-validation is a resampling technique used to evaluate machine learning models by partitioning the original dataset into k equal-sized folds. The model is trained on k-1 folds and validated on the remaining fold, repeating this process k times so each fold serves as the validation set once. This provides a more robust and less biased estimate of model performance compared to a single train-test split, especially when data is limited.
Variation 2. What is cross-validation in machine learning?
medium- A.Training multiple different models and comparing their performance
- ✓ B.Repeatedly training and evaluating the model on different data splits for reliable performance estimates
- C.Checking if a model works correctly by running it backward
- D.Training a model on two different datasets simultaneously
Why B: Cross-validation is a technique for assessing how a machine learning model will generalize to an independent dataset. It involves partitioning the data into complementary subsets, training the model on one subset (the training fold), and validating it on the remaining subset (the validation fold), then repeating this process multiple times with different partitions. The final performance estimate is the average of the validation scores, which provides a more reliable and less biased measure than a single train-test split.
Last reviewed: Jun 11, 2026
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