SF-Data-Arch Practice Exam 3 — 20 Questions

Question 1

During a migration, you discover that the source system has a different date format than the required ISO 8601 format for Salesforce. What is the most efficient way to handle this?

Accepted Answer

Transform the data using an ETL tool or script before loading it into Salesforce.. Data transformation should ideally occur in the ETL (Extract, Transform, Load) layer before the data reaches Salesforce. By formatting dates during the extraction or transformation process, you ensure that the data conforms to Salesforce requirements before it hits the API. This is more scalable than attempting to force Salesforce to interpret varying formats, which can lead to data loss or import errors during the ingestion process.

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Use an Apex trigger to format the dates after the records are inserted.

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Update the Salesforce field type to 'Text' to accommodate any date format.

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Manually update the records in the Salesforce UI after the migration completes.

Question 2

What is the main advantage of using an ETL tool rather than the standard Salesforce Data Loader for a large-scale enterprise migration?

Accepted Answer

It provides visual workflow orchestration and error handling.. Enterprise ETL tools provide advanced capabilities such as graphical orchestration, error logging, automated retry logic, and seamless connectivity to heterogeneous data sources. While Data Loader is sufficient for simple, smaller loads, ETL tools allow architects to manage complex migration workflows, parallel jobs, and sophisticated data transformations across multiple systems, which is required for large-scale enterprise integration projects.

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It is always free to use.

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It bypasses Salesforce security controls.

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It allows direct database access to the Salesforce backend.

Question 3

An organization is establishing a Data Governance Council. Which THREE outcomes are primary goals of this council? (Choose Three)

Accepted Answer

Resolving data ownership and stewardship conflicts.. A Data Governance Council is a cross-functional body that aligns data strategy with business objectives. By resolving data ownership conflicts, setting enterprise-wide standards, and fostering communication between business units and IT, the council ensures that data is managed as a strategic asset. This alignment prevents silos, ensures accountability, and maximizes the value derived from data across the entire organization.

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Managing daily Apex deployment cycles.

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Directly configuring Salesforce security profiles.

Question 4

Why should an organization perform 'Data Cleansing' before migrating data into a master record system?

Accepted Answer

To remove inconsistencies and ensure data accuracy in the destination system.. Data cleansing identifies and fixes errors such as duplicates, invalid formats, and incomplete fields before they are propagated into a new system. If dirty data is ingested, it corrupts the new MDM environment, rendering reports unreliable and forcing developers to build complex, expensive workarounds. Cleaning first ensures the initial state of the master system is pristine, providing a reliable foundation for all future business operations and data-driven decisions.

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To increase the total number of records in the Salesforce database.

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To eliminate the need for any future data governance or stewardship.

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To minimize the Salesforce storage cost by deleting all inactive customer accounts.

Question 5

Refer to the exhibit. As a Data Architect, what is the governance concern if this 'VAL-099' rule remains inactive in production?

Accepted Answer

Degradation of data quality over time.. The primary concern is the uncontrolled entry of corrupt data. If a governance rule is defined but not active, the system lacks the technical enforcement to maintain quality standards. This leads to 'data rot' where the system collects invalid entries, which will eventually break downstream integrations, analytics reporting, and business processes, causing significant effort for data cleansing at a later date.

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Increased system storage limits.

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Inability to run system reports.

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Increased Apex trigger execution time.

Question 6

A Data Architect is tasked with ensuring Data Lifecycle Management is governed. Which lifecycle stage should include a requirement for automated data archiving?

Accepted Answer

Data Retention.. The 'Retention' or 'Disposition' phase is where archiving is most critical. As data ages, its value decreases, while storage costs and security risks remain. By governing this stage, the architect ensures that older data is moved to cheaper, secure storage (archiving) rather than being deleted or kept in active production tables, which optimizes system performance and maintains compliance with storage policies.

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Data Creation.

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Data Usage.

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Data Acquisition.

Question 7

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?

Accepted Answer

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

Migrate all Salesforce CRM data into the marketing tool's database.

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Remove all personally identifiable information from the incoming data streams.

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Automate the deletion of all records that do not contain a phone number.

Question 8

A company is planning to migrate legacy customer data into Salesforce. Before the load, the data architect recommends data profiling. Why is this activity mandatory for a successful MDM project?

Accepted Answer

It identifies patterns, anomalies, and quality issues within the legacy data set.. Data profiling uncovers hidden quality issues, such as inconsistent formatting, missing values, or unexpected dependencies. By understanding the true state of the source data, the architect can design effective cleansing and transformation rules. Failing to profile leads to 'garbage in, garbage out,' where inaccurate data undermines the value of the new Salesforce implementation and causes significant post-migration remediation costs and user frustration.

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It ensures that the Salesforce data model is fully compatible with the legacy SQL schema.

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It increases the speed of the data load by compressing the CSV files before import.

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It automatically maps all legacy fields to the appropriate Salesforce fields without human intervention.

Question 9

A global retailer is consolidating customer master data from Salesforce, a legacy loyalty system, and an e-commerce platform. The data architect must design a matching strategy that minimizes false positives while still identifying the same customer across sources where names are spelled differently and addresses vary. Which matching approach should the architect recommend?

Accepted Answer

Probabilistic matching with a configured match threshold and score bands, supplemented by deterministic rules for high-confidence identifiers like loyalty number.. Cross-source customer matching with inconsistent names and addresses requires probabilistic matching over multiple attributes with a tuned threshold, because exact keys alone miss too many true matches. Adding deterministic rules for unique identifiers like loyalty number anchors high-confidence matches. This hybrid strategy controls false positives through scoring while preserving recall, which is exactly what the scenario demands.

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Exact matching on the combination of first name, last name, and postal code.

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Fuzzy matching on the full name field alone using a Levenshtein distance threshold.

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Deterministic matching on exact email address only, treating any non-match as a distinct customer.

Question 10

A multinational manufacturer runs a single Salesforce org for sales and service. Its governance team wants to enforce that all new custom objects created in production carry a business owner, a retention classification, and a data sensitivity label. Administrators frequently create objects ad hoc, and the team wants an automated check rather than a manual review. Which approach should the data architect recommend?

Accepted Answer

Use Metadata API and Apex Metadata API to scan object definitions against a governance checklist and block deployment when required attributes are missing.. Metadata-level governance requires inspecting object definitions, which only metadata-aware tooling can do. A programmatic check using Metadata API and Apex Metadata API lets the team compare each object against required attributes such as owner, retention classification, and sensitivity label, and it can fail the deployment automatically. Manual workflows and record-level rules cannot assert anything about the object definition itself.

Answer

Enable Field Audit Trail and Field History Tracking on all custom objects to capture who created each object.

Answer

Build a custom validation rule on each custom object that checks the owner field is populated on records.

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Require every administrator to submit a change request through a ServiceNow workflow before creating a custom object.

Question 11

A global Salesforce org has implemented a data governance framework. The governance team needs to ensure that data is retained according to legal and regulatory requirements, and that it is securely deleted when no longer needed. They are evaluating Salesforce's native capabilities for data lifecycle management. Which statement accurately describes a limitation or behavior of Salesforce's native data retention and deletion features that the team must consider?

Accepted Answer

Salesforce Field Audit Trail can be used to retain field history for up to 10 years, but it does not automatically delete the data after the retention period; manual intervention is required.. Field Audit Trail retains field history for up to 10 years, but it does not automatically delete data after that period. Manual deletion or archiving is required to enforce retention limits. This is a critical limitation for data lifecycle governance, as organizations must implement their own processes to purge data when it is no longer needed, ensuring compliance with legal and regulatory requirements.

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When a record is deleted in Salesforce, it is immediately and permanently removed from all backups and disaster recovery systems, ensuring compliance with right-to-erasure requests.

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Salesforce automatically archives inactive records to a separate cold storage system after 2 years, and users can retrieve them via a standard API.

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Salesforce provides a built-in data retention policy engine that automatically deletes records based on custom retention rules without any development.

Question 12

A Salesforce data architect is explaining the concept of a 'golden record' to business stakeholders. Which statement best describes a golden record in a master data management context?

Accepted Answer

A single, authoritative version of a master data entity that is trusted across the enterprise.. A golden record is the single, authoritative version of a master data entity that is trusted across the enterprise. It is created by applying survivorship rules to data from multiple source systems, ensuring the best values are selected. It is not simply the latest, static, or a Salesforce-only merged record. This concept is foundational to MDM.

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A record that is stored in the system of record and never changed.

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The most recently updated record from any source system.

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A duplicate-free record created by merging all duplicate records in Salesforce.

Question 13

A data architect is planning to implement a Skinny Table for a custom object with 10 million records to improve query performance. Which two considerations are critical when designing the Skinny Table? (Choose two.)

Accepted Answer

The Skinny Table requires a synchronization mechanism to keep data current with the main object.. A Skinny Table is a denormalized copy of a subset of fields from a large object, used to accelerate specific queries. The two critical considerations are selecting only the fields needed for common queries to minimize row size, and implementing a synchronization mechanism to keep the copy up to date. Without these, the Skinny Table would either be too large to be effective or become inconsistent with the source data.

Answer

The Skinny Table can be used as the primary data source for all reporting to replace the main object.

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The Skinny Table should be indexed on all fields to maximize query flexibility.

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The Skinny Table must include all fields from the original object to maintain data integrity.

Question 14

The governance board at a financial services firm wants to know when critical fields on the Opportunity object were changed, who changed them, and what the previous value was, without writing custom code. Which native capability should the architect enable?

Accepted Answer

Enable Field History Tracking on the selected Opportunity fields. The board wants a native, no-code record of when critical Opportunity fields changed, who changed them, and the prior values. Field History Tracking provides exactly that for selected fields and requires only configuration. Chatter notifications, custom Flow logging, and nightly exports each capture partial or indirect information and cannot deliver a reliable, attributed field-change history.

Answer

Build a Flow that writes a record to a custom log object on every Opportunity update

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Schedule a nightly Data Loader export of Opportunity records for comparison

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Create a Workflow Rule that posts to Chatter when Opportunity fields change

Question 15

Northern Trail Outfitters has a Salesforce org where the Account object contains 40 custom fields, many of which were created by different teams over five years. No one is certain which fields are actively used, which are populated by integrations, and which are safe to remove. The CIO asks the data architect to establish ongoing visibility into field usage and data provenance. Which approach best addresses this governance gap?

Accepted Answer

Create a data dictionary and metadata inventory that documents each field's purpose, source system, and owning team, and establish a review cadence to keep it current.. The core issue is a lack of documented knowledge about existing fields and their origins. A data dictionary paired with a metadata inventory captures purpose, source, and ownership for each field, and a review cadence keeps it trustworthy over time. This combination gives leadership the ongoing visibility needed to make informed deprecation and consolidation decisions rather than guessing from a single usage metric.

Answer

Enable Field Audit Trail on all 40 custom fields and rely on the audit history as the primary source of field usage documentation.

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Restrict field creation permissions to system administrators and freeze the Account object schema to prevent further sprawl.

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Run a one-time report on field population rates and delete all fields with less than 10% fill rate.

Question 16

An enterprise Salesforce org has multiple business units that each maintain their own picklist values for the Industry field on Account. This has caused inconsistent reporting and integration failures. The Data Governance Council mandates a single, standardized Industry picklist across all business units. Which approach best enforces this standardization while allowing controlled local extensions?

Accepted Answer

Use a global picklist value set for the Industry field and allow each business unit to add values only through a formal governance request process that updates the global set.. The governance council needs one standardized Industry picklist while still permitting controlled local additions. A global picklist value set provides the single source of truth, and a formal request process ensures any new values are reviewed and added centrally. This prevents uncontrolled divergence, keeps integrations and reporting consistent, and gives business units a governed path to request extensions.

Answer

Leave each business unit with its own custom picklist and create a formula field that maps local values to a standardized set for reporting.

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Replace the Industry field with a text field and rely on a validation rule that checks values against a custom setting maintained by each business unit.

Answer

Create a single global picklist value set and assign it to the Industry field on Account for all business units, with no ability for local additions.

Question 17

A Salesforce architect is designing a data integration strategy to synchronize 5 million records daily from an external ERP system into Salesforce. The integration must handle upserts, minimize API calls, and avoid governor limits. The external system can provide data in CSV format and supports REST APIs. Which approach should the architect recommend?

Accepted Answer

Use the Bulk API 2.0 with parallel processing and CSV data.. Bulk API 2.0 is specifically built for high-volume data operations, supporting CSV input and parallel processing. It minimizes API calls by handling large batches in a single job and is designed to avoid governor limits through asynchronous processing. SOAP and REST APIs are better for smaller, real-time integrations, and the Streaming API is for outbound messaging. Therefore, Bulk API 2.0 is the correct choice for daily synchronization of millions of records.

Answer

Use the SOAP API with a batch size of 200 records per call.

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Use the Streaming API to push records from the ERP system into Salesforce.

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Use the REST API with composite requests to combine multiple records per call.

Question 18

A Salesforce data architect is migrating 100,000 Opportunity records from a legacy system. The legacy data includes a custom field 'Legacy_Region__c' that must be mapped to a new picklist field 'Region__c' in Salesforce. The picklist values in Salesforce are: 'North America', 'Europe', 'Asia', 'Latin America'. The legacy data uses values like 'NA', 'EU', 'APAC', 'LATAM'. What is the most efficient way to transform the data during migration?

Accepted Answer

Perform the transformation in an ETL tool before loading the data.. The most efficient way to transform legacy values to match Salesforce picklist values is to perform the transformation in an ETL tool before loading. This avoids load errors and ensures data quality. Formula fields, post-load updates, and workflow rules are either not possible or inefficient for this purpose.

Answer

Load the legacy values as-is and then use Data Loader to update the picklist values.

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Use a formula field in Salesforce to convert the legacy values after loading.

Answer

Create a workflow rule to update the picklist field based on the legacy field.

Question 19

Universal Containers wants to ensure that when a user updates a field on an Account record, the change is captured and stored for auditing purposes. The solution must be configurable without writing code and must retain field history for up to 18 months. What should a data architect implement?

Accepted Answer

Enable Field History Tracking on the Account object for the required fields.. Field History Tracking is the standard declarative feature for auditing field changes. It automatically records changes, retains them for up to 18 months, and requires no code. Other options either involve code, track the wrong type of activity, or do not store historical data, making them unsuitable for this requirement.

Answer

Configure a workflow rule to send an email notification on field change.

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Create a trigger on the Account object to write changes to a custom object.

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Use Salesforce Shield Event Monitoring to track field changes.

Question 20

A data architect is reviewing a custom object that has grown to 8 million records. Users report that list views and reports are slow because they sort on a text field, Priority__c, which has only three possible values. What should the architect recommend to improve performance?

Accepted Answer

Remove sorting on Priority__c from list views and reports, and instead sort on an indexed field or filter on a more selective field.. For large data volumes, query performance is heavily influenced by field selectivity. A field with only three distinct values is not selective, so sorting or filtering on it forces the platform to scan many records. The best approach is to avoid sorting on such fields and instead use an indexed, high-cardinality field for sorting or filtering. This reduces the data set the query must process.

Answer

Enable Divisions on the object and assign records to divisions based on Priority__c.

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Request a custom index on Priority__c to speed up sorting.

Answer

Convert Priority__c to a picklist field with a restricted set of values to improve selectivity.