SF-Data-Arch Master Data Management Practice Question
A company is implementing a Customer Data Platform (CDP) to consolidate data from Salesforce, a web store, and an email marketing tool. Which TWO steps are critical for successful identity resolution?
⚠ Common exam trap
Candidates frequently select only one of the two steps, or focus solely on technical matching algorithms while neglecting the business-critical aspect of deciding which system's data is the authoritative source.
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
✓
Define deterministic and probabilistic matching rules.
Identity resolution is the process of linking data points from disparate sources to create a unified profile. By defining clear matching rules and prioritizing data attributes, the CDP can accurately identify unique individuals even when they use different identifiers across channels. This is vital for personalized marketing and accurate customer analytics, ensuring that the organization does not treat the same individual as multiple distinct records.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Define deterministic and probabilistic matching rules.
Why this is correct
Deterministic matching uses exact identifiers like email or ID, while probabilistic matching uses fuzzy logic to link records based on confidence scores. Combining both approaches maximizes the reach of the identity resolution process, ensuring that fragmented customer interactions are accurately linked into a single cohesive profile.
- ✗
Migrate all Salesforce CRM data into the marketing tool's database.
Why it's wrong here
Consolidating data into a marketing tool is not a substitute for identity resolution. Marketing databases often lack the schema flexibility and data processing power to handle the complex, cross-platform matching logic required to unify profiles across disparate systems, leading to incomplete or inaccurate customer data sets.
- ✓
Establish survivorship rules to determine which system's data takes precedence.
Why this is correct
Survivorship rules are essential when multiple systems provide conflicting information for the same attribute. These rules define which source is the most trusted for specific fields, ensuring the unified profile reflects the most accurate and current information while preventing stale data from overwriting high-quality records.
- ✗
Remove all personally identifiable information from the incoming data streams.
Why it's wrong here
Removing PII destroys the attributes necessary for matching records across systems. Identity resolution relies on identifiers like names, emails, and device IDs to link activities. Without these data points, the system cannot correlate interactions, rendering the identity resolution process ineffective and preventing the creation of unified profiles.
- ✗
Automate the deletion of all records that do not contain a phone number.
Why it's wrong here
Deleting records based on missing phone numbers is an arbitrary data quality policy that causes significant data loss. Many customers do not provide phone numbers, yet they are still valuable. This approach artificially shrinks the customer base and disrupts the holistic view of the organization's total customer population.
About these practice questions
Courseiva writes every SF-Data-Arch question from scratch — 222 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Salesforce exam blueprint
This SF-Data-Arch 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 SF-Data-Arch exam.