- A
Yes, because it uniquely identifies each customer and helps the model differentiate them.
Why wrong: Including a unique identifier is not useful for prediction because the model cannot learn a meaningful relationship between a random ID and the target variable.
- B
No, because the CustomerID is a random unique identifier with no predictive power for churn.
Correct. Unique identifiers are arbitrary and do not correlate with the outcome. They should be removed to avoid overfitting.
- C
Yes, because the model can learn patterns from the numeric values.
Why wrong: While CustomerID might be numeric, it is assigned arbitrarily and does not contain useful patterns for prediction.
- D
No, because the CustomerID column contains too many missing values.
Why wrong: The reason is not about missing values; it's that the ID has no predictive correlation. Missingness is a separate data quality issue.
Quick Answer
The answer is no, because a unique identifier like CustomerID has no predictive power for customer churn. The core technical concept is that such identifiers are randomly assigned and lack any meaningful correlation with the target variable; including them introduces noise and risks overfitting, as the model could simply memorize each ID instead of learning generalizable patterns from truly predictive features. On the Microsoft Azure AI Fundamentals AI-900 exam, this tests your understanding of feature selection principles—specifically that features must have a logical relationship to the outcome. A common trap is assuming all data columns are useful, but the exam emphasizes that arbitrary labels or keys should be excluded. Remember the mnemonic: “If it’s unique and random, it’s not a feature—it’s a label.”
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. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
A data scientist is preparing a dataset to train a model that predicts customer churn. The dataset includes a column 'CustomerID' which is a unique identifier for each customer. Should the data scientist include the 'CustomerID' column as a feature in the training data?
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
No, because the CustomerID is a random unique identifier with no predictive power for churn.
Option B is correct because CustomerID is a unique identifier that does not contain any meaningful pattern or relationship with the target variable (churn). Including such a column would introduce noise and risk overfitting, as the model could memorize each ID rather than learning generalizable patterns. In Azure Machine Learning, features should be predictive attributes, not arbitrary labels.
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.
- ✗
Yes, because it uniquely identifies each customer and helps the model differentiate them.
Why it's wrong here
Including a unique identifier is not useful for prediction because the model cannot learn a meaningful relationship between a random ID and the target variable.
- ✓
No, because the CustomerID is a random unique identifier with no predictive power for churn.
Why this is correct
Correct. Unique identifiers are arbitrary and do not correlate with the outcome. They should be removed to avoid overfitting.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Yes, because the model can learn patterns from the numeric values.
Why it's wrong here
While CustomerID might be numeric, it is assigned arbitrarily and does not contain useful patterns for prediction.
- ✗
No, because the CustomerID column contains too many missing values.
Why it's wrong here
The reason is not about missing values; it's that the ID has no predictive correlation. Missingness is a separate data quality issue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think unique identifiers are useful for differentiation, but the exam tests the principle that features must have predictive power and that arbitrary IDs introduce noise rather than signal.
Detailed technical explanation
How to think about this question
In machine learning, features must have a statistical relationship with the target variable to be useful. Unique identifiers like CustomerID are often high-cardinality categorical variables that, if one-hot encoded, would create thousands of sparse columns, leading to the curse of dimensionality and poor model performance. In Azure Automated ML, such columns are automatically dropped or flagged as 'not useful' during data guardrails to prevent overfitting.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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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: No, because the CustomerID is a random unique identifier with no predictive power for churn. — Option B is correct because CustomerID is a unique identifier that does not contain any meaningful pattern or relationship with the target variable (churn). Including such a column would introduce noise and risk overfitting, as the model could memorize each ID rather than learning generalizable patterns. In Azure Machine Learning, features should be predictive attributes, not arbitrary labels.
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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