SF-Data-Arch Practice Test 5 — 10 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.

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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.