The answer is feature scaling for gradient-based algorithms and cross-validation to evaluate model performance. Feature scaling, such as normalization or standardization, is critical for gradient-based algorithms like linear regression or neural networks because these models rely on distance calculations and gradient descent; without scaling, features with larger magnitudes can dominate the learning process, leading to slow convergence or poor results. Cross-validation, on the other hand, provides a robust estimate of model generalization by repeatedly splitting data into training and validation sets, reducing the variance of a single train-test split and helping detect overfitting. On the AWS Certified AI Practitioner AIF-C01 exam, this question tests your understanding of foundational preprocessing steps that directly impact model accuracy and reliability, often appearing as a trap where candidates confuse data splitting with preprocessing or overlook scaling for tree-based models. Remember the mnemonic “Scale for Slope, Cross for Confidence” — scale your features when using gradient-based slopes, and cross-validate to build confidence in your model’s real-world performance.
AIF-C01 Fundamentals of AI and ML Practice Question
This AIF-C01 practice question tests your understanding of fundamentals of ai and ml. 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.
Which TWO of the following are best practices for data preprocessing in machine learning? (Select TWO.)
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use cross-validation to evaluate model performance
Cross-validation is a best practice for evaluating model performance because it provides a more robust estimate of how the model will generalize to unseen data by partitioning the data into multiple training and validation sets. This reduces the variance associated with a single train-test split and helps detect overfitting, making it a standard technique in machine learning workflows.
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.
✓
Use cross-validation to evaluate model performance
Why this is correct
Cross-validation provides a more reliable estimate of model generalization.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Always split data 80/20 for training and testing
Why it's wrong here
The split ratio depends on dataset size; 80/20 is not always optimal.
✗
One-hot encoding for ordinal categories
Why it's wrong here
Ordinal categories should use ordinal encoding; one-hot encoding ignores order.
✓
Feature scaling for gradient-based algorithms
Why this is correct
Feature scaling ensures features contribute equally to the optimization.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Drop duplicate records only if they are manual entry errors
Why it's wrong here
Duplicates should generally be removed to avoid bias, not only manual errors.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that one-hot encoding is universally applicable to all categorical data, but the trap here is that candidates forget ordinal categories have a natural order that one-hot encoding discards, leading to loss of information and potentially worse model performance.
Detailed technical explanation
How to think about this question
Feature scaling, such as standardization (z-score normalization) or min-max scaling, is critical for gradient-based algorithms (e.g., logistic regression, neural networks, SVMs) because these algorithms use distance measures or gradient descent, and unscaled features with larger magnitudes can dominate the update steps, leading to slow convergence or poor local minima. For example, in stochastic gradient descent, features with different scales cause the gradient to be disproportionately influenced by one feature, requiring adaptive learning rates or careful tuning to compensate.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Fundamentals of AI and ML — This question tests Fundamentals of AI and ML — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use cross-validation to evaluate model performance — Cross-validation is a best practice for evaluating model performance because it provides a more robust estimate of how the model will generalize to unseen data by partitioning the data into multiple training and validation sets. This reduces the variance associated with a single train-test split and helps detect overfitting, making it a standard technique in machine learning workflows.
What should I do if I get this AIF-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 →
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. Which TWO of the following are best practices for preparing training data for a machine learning model?
hard
✓ A.Handle missing values by imputing or removing them.
✓ B.Split the data into training, validation, and test sets.
C.Remove all outliers to improve model robustness.
D.Use the entire dataset for training to maximize data usage.
E.Avoid shuffling the data to preserve original order.
Why A: Option A is correct because handling missing values is a critical data preprocessing step. Missing data can introduce bias or cause algorithms to fail. Imputation (e.g., using mean, median, or model-based methods) or removal of rows/columns with missing values ensures the dataset is complete and suitable for training, preventing errors during model fitting.
Last reviewed: Jun 30, 2026
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This AIF-C01 practice question is part of Courseiva's free Amazon Web Services 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 AIF-C01 exam.
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