AI Associate Data for AI Practice Question
A company is ingesting data from multiple sources into Data Cloud for Einstein. Which THREE data preparation steps should be performed?
⚠ Common exam trap
Salesforce often tests the distinction between data preparation steps (normalization, field mapping, deduplication) and data security or ML-specific tasks (encryption, data labeling) to see if candidates confuse operational data engineering with security or model training processes.
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
✓
Normalization
Normalization is correct because Data Cloud requires data from multiple sources to be transformed into a consistent format, such as standardizing date formats, units, or naming conventions, to ensure the data can be unified and analyzed effectively. This step is critical for Einstein AI models to process data without inconsistencies that could skew predictions or insights.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Normalization
Why this is correct
Ensures consistent data formats across sources.
- ✓
Field mapping
Why this is correct
Aligns source fields to target schema.
- ✗
Encryption
Why it's wrong here
Encryption is a security measure, not a preparation step for model training.
- ✗
Data labeling
Why it's wrong here
Labeling is part of supervised learning, not general data ingestion.
- ✓
Deduplication
Why this is correct
Removes duplicate records to avoid bias.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI Associate practice question is part of Courseiva's free Salesforce 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 Associate exam.