Quick Answer
The answer is deduplication, normalization, and identity resolution. Deduplication is correct because Data Cloud must eliminate duplicate records from multiple sources to ensure Einstein AI models train on unique, accurate data rather than inflated or conflicting entries. Normalization transforms data into a consistent format—like standardizing date formats or units—so that Einstein can process it without inconsistencies that skew predictions. Identity resolution links records across sources to create a unified profile, which is essential for AI-driven personalization. On the Salesforce AI Associate exam, this question tests your understanding of the data preparation pipeline before Einstein activation; a common trap is confusing data transformation with data mapping. Remember the mnemonic DNI—Deduplicate, Normalize, Identify—to recall the three critical steps for preparing data in Data Cloud.
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 company is ingesting data from multiple sources into Data Cloud for Einstein. Which THREE data preparation steps should be performed?
Normalization
Ensures consistent data formats across sources.
Field mapping
Aligns source fields to target schema.
Encryption
Why wrong: Encryption is a security measure, not a preparation step for model training.
Data labeling
Why wrong: Labeling is part of supervised learning, not general data ingestion.
Deduplication
Removes duplicate records to avoid bias.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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.
Related concept
Read the scenario before looking for a memorised answer.
Field mapping
Why this is correct
Aligns source fields to target schema.
Related concept
Read the scenario before looking for a memorised answer.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
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.
Detailed technical explanation
Normalization in Data Cloud often involves mapping source data to a canonical data model (CDM), such as Salesforce's standard objects, using Data Cloud's data transformation pipelines. Field mapping ensures that source fields align with target schema fields, and deduplication uses rules-based matching (e.g., fuzzy matching on email or phone) to merge duplicate records, which is essential for maintaining a single source of truth in the Data Cloud data lake.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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FAQ
Data for AI — This question tests Data for AI — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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.
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