The answer is standardizing to mean 0 and variance 1, along with normalizing features to a 0-1 range. These two transformations are commonly applied to improve regression model performance because they prevent features with larger numeric magnitudes from dominating the optimization process. Standardization rescales data to have zero mean and unit variance, which is essential when features have different units or distributions, while normalization (min-max scaling) compresses values into a fixed range, ensuring all numeric features contribute equally to distance calculations and gradient descent convergence. On the Salesforce AI Associate exam, this question tests your understanding of feature engineering for regression models, often appearing in scenario-based items where you must choose the correct preprocessing steps. A common trap is assuming only one transformation is needed, but the exam explicitly requires both for balanced feature influence. Memory tip: think “Zero-One for range, Zero-One for scale”—normalization gives a 0-1 range, standardization gives a 0-1 scale (mean 0, variance 1).
AI Associate Data for AI Practice Question
This AI Associate practice question tests your understanding of data for ai. 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.
A data scientist is preparing numeric features for a regression model. Which TWO transformations are commonly applied to improve model performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Normalize to a 0-1 range
Normalizing features to a 0-1 range (min-max scaling) ensures that all numeric features contribute equally to the model, preventing features with larger magnitudes from dominating the gradient descent optimization. This is especially important for distance-based algorithms like k-nearest neighbors or neural networks, where feature scale directly impacts convergence speed and model accuracy.
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.
✓
Normalize to a 0-1 range
Why this is correct
Scales features to a common range, helpful for distance-based models.
Related concept
Read the scenario before looking for a memorised answer.
✗
Remove outliers beyond 3 standard deviations
Why it's wrong here
Outliers may contain signal; removal should be justified, not automatic.
✗
Convert numbers to string labels
Why it's wrong here
Loses order and magnitude, degrading performance.
✗
Apply one-hot encoding
Why it's wrong here
One-hot encoding is for categorical features, not numeric.
✓
Standardize to mean 0 and variance 1
Why this is correct
Centers data and scales variance, useful for linear models.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the distinction between data cleaning (e.g., outlier removal) and feature transformation (e.g., scaling), leading candidates to mistakenly select outlier removal as a transformation that improves model performance.
Detailed technical explanation
How to think about this question
Standardization (z-score normalization) transforms features to have a mean of 0 and a variance of 1, which is critical for algorithms that assume normally distributed data, such as linear regression with regularization (Ridge, Lasso) or principal component analysis (PCA). Under the hood, standardization uses the formula (x - μ) / σ, where μ is the mean and σ is the standard deviation; this ensures that the feature weights in the model are comparable and that the regularization penalty is applied uniformly across all features.
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 network engineer at a university connects two campus buildings via a fibre link. Both routers run OSPF, but no adjacency forms — even though both routers can ping each other. The engineer finds one router is in area 0 and the other in area 1. OSPF adjacency requires matching area numbers, hello/dead timers, and network type. IP reachability alone is not enough.
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.
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Normalize to a 0-1 range — Normalizing features to a 0-1 range (min-max scaling) ensures that all numeric features contribute equally to the model, preventing features with larger magnitudes from dominating the gradient descent optimization. This is especially important for distance-based algorithms like k-nearest neighbors or neural networks, where feature scale directly impacts convergence speed and model accuracy.
What should I do if I get this AI Associate 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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Question Discussion
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