The correct answer is that 'color' is one-hot encoded into multiple binary columns, while 'price' and 'weight' are standardized to have mean 0 and variance 1. This outcome is dictated by the transformation configuration, which applies a one-hot encoder to categorical features to convert them into a binary matrix format suitable for machine learning algorithms, and a standard scaler to numerical features to center them around zero with unit variance, ensuring no single feature dominates due to scale. On the Salesforce AI Associate exam, this question tests your understanding of common preprocessing pipelines for mixed data types, often appearing in scenarios where you must interpret a visual configuration or drag-and-drop transformation setup. A common trap is confusing standardization with normalization—remember, standardization forces a mean of 0 and variance of 1, while normalization typically rescales to a [0,1] range. For a quick memory tip, think "One-hot for categories, standardize for numbers" to keep the two transformations distinct.
AI Associate Data for AI Practice Question
This AI Associate practice question tests your understanding of data for ai. 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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
'color' is one-hot encoded into multiple binary columns; 'price' and 'weight' are standardized to have mean 0 and variance 1.
Option C is correct because the transformation configuration applies a one-hot encoder to the 'color' categorical column, creating multiple binary columns, and applies a standard scaler to the 'price' and 'weight' numerical columns, centering them to mean 0 and scaling to unit variance. This is a common preprocessing pipeline that handles mixed data types appropriately.
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.
✗
The transformation is invalid because one-hot encoding cannot be combined with scaling.
Why it's wrong here
They can be combined.
✗
Only 'color' is transformed; 'price' and 'weight' are unchanged.
Why it's wrong here
Both transformations are applied.
✓
'color' is one-hot encoded into multiple binary columns; 'price' and 'weight' are standardized to have mean 0 and variance 1.
Why this is correct
Correct interpretation of the config.
Related concept
Read the scenario before looking for a memorised answer.
✗
'color' is scaled to [0,1] and 'price', 'weight' are one-hot encoded.
Why it's wrong here
The operations are swapped.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Salesforce often tests the ability to distinguish which transformation applies to which column type, trapping candidates who confuse scaling with encoding or assume that different transformations cannot coexist in a single pipeline.
Detailed technical explanation
How to think about this question
Under the hood, one-hot encoding converts each category in 'color' into a separate binary column (e.g., 'color_red', 'color_blue'), avoiding ordinal assumptions, while standard scaling subtracts the mean and divides by the standard deviation for each numerical feature. This combination is essential in machine learning pipelines to prevent categorical features from being misinterpreted as ordinal and to ensure numerical features contribute equally to distance-based algorithms like k-NN or SVM.
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 practitioner preparing for the AI Associate exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
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: 'color' is one-hot encoded into multiple binary columns; 'price' and 'weight' are standardized to have mean 0 and variance 1. — Option C is correct because the transformation configuration applies a one-hot encoder to the 'color' categorical column, creating multiple binary columns, and applies a standard scaler to the 'price' and 'weight' numerical columns, centering them to mean 0 and scaling to unit variance. This is a common preprocessing pipeline that handles mixed data types appropriately.
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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