Free Salesforce Data-Architect Practice Exam with Questions & Answers | Set: 4

Importing Account records first followed by order records after sorting orders by AccountID is the recommended way to avoid locking issues during import. This can reduce the number of lock contention errors by minimizing the number of parent records that are concurrently processed by multiple batches. Sorting orders by AccountID can also group the child records by their parent records and avoid updating the same parent record in different batches3. Importing Account records first followed by order records after sorting order by OrderID will not help with avoiding locking issues because it does not group the child records by their parent records. Changing the relationship to Lookup and updating the relationship to master-detail after import will not work because changing a relationship from Lookup to master-detail requires that all child records have a parent record, which may not be the case after import. Importing Order records and Account records separately and populating AccountID in orders using batch Apex will not help with avoiding locking issues because it still requires updating the parent records in batches.

 The Data Architect should consider the limitations on Org data storage and the Bulk API limitations on picklist fields when denormalizing the data model into a single Transaction object with a Branch Office picklist. The Org data storage limit is the total amount of data that can be stored in a Salesforce Org, and it depends on the edition and license type of the Org1. The Bulk API limit on picklist fields is the maximum number of values that can be imported or exported using the Bulk API, and it is 1,000 values per picklist field2. These limitations could affect the performance and scalability of the data model, and the Data Architect should plan accordingly.

According to the Salesforce documentation2, duplicate leads are leads that have the same or similar information as other leads in Salesforce. Duplicate leads can cause poor customer experience, higher costs, and inaccurate reporting. To mitigate the issues caused by duplicate leads, some of the recommended practices are:

Standardize lead information across all channels (option B). This means using consistent formats, values, and fields for capturing lead data from different sources, such as web forms, email campaigns, or third-party vendors. This can help reduce data quality issues and make it easier to identify and prevent duplicate leads.

Implement a third-party solution to clean and enrich lead data (option D). This means using an external service or tool that can validate, correct, update, and enhance lead data before or after importing it into Salesforce. This can help improve data quality and accuracy, and reduce duplicate leads.

Implement a de-duplication strategy to prevent duplicate leads (option E). This means using Salesforce features or custom solutions that can detect and block duplicate leads from being created or imported into Salesforce. For example, using Data.com Duplicate Management3, which allows defining matching rules and duplicate rules for leads and other objects.

Building a process to manually search and merge duplicates (option A) is not a good practice, as it can be time-consuming, error-prone, and inefficient. Building a custom solution to identify and merge duplicate leads (option C) is also not a good practice, as it can be complex, costly, and difficult to maintain. It is better to use existing Salesforce features or third-party solutions that can handle duplicate leads more effectively.

According to the official Salesforce guide1, big objects are designed to store and manage massive data volumes within Salesforce without affecting performance. They can be queried by using Async SOQL or standard SOQL, and they can be accessed by using Apex, Visualforce, Lightning components, or APIs. Big objects are ideal for storing data that is used for analytics or reporting purposes, such as the reward program data. Option A is the correct answer because it allows NTO to create a custom big object in Salesforce that can store the reward program data and make it available for consumption by Einstein Analytics and Discovery. Option B is incorrect because Einstein cannot connect directly to the point of sales system to capture the reward program data. Option C is incorrect because Einstein Analytics does not support creating big objects. Option D is incorrect because custom objects are not suitable for storing large volumes of data.

The issue that an architect will have when implementing the quarterly accounts receivable report in Salesforce is that Salesforce does not allow more than four objects in a single report type. A report type defines the set of records and fields available to a report based on the relationships between a primary object and up to four related objects. A report type has the following limitations:

It cannot include more than four objects in a single report type, which means that the report cannot compile data from five objects (Accounts, Contacts, Opportunities, Orders, and Order Line Items) at once.

It cannot include objects that are more than two relationships away from each other, which means that the report cannot access fields from Order Line Items through Opportunities and Orders.

It cannot include objects that have a many-to-many relationship with each other, which means that the report cannot access fields from Contacts and Opportunities through the junction object Opportunity Contact Role.

The three considerations needed prior to a SF Connect implementation are to develop an object relationship strategy, identify the external tables to sync into external objects, and assess whether the external data source is reachable via an ODATA endpoint. SF Connect is a feature that allows integrating external data sources with Salesforce using external objects. External objects are similar to custom objects, but they store metadata only and not data. They enable on-demand access to external data via standard Salesforce APIs and user interfaces. To implement SF Connect, a data architect needs to consider how the external objects will relate to other objects in Salesforce, which external tables will be exposed as external objects, and whether the external data source supports ODATA protocol for data access.

According to the official Salesforce guide1, data classification metadata is a feature that allows administrators to classify data fields based on their sensitivity level, such as confidential, restricted, or general. Data classification metadata can be used to filter and identify sensitive data fields and apply appropriate security measures, such as encryption, masking, or auditing. Option D is the correct answer because it suggests using data classification metadata to provide ways to filter and identify sensitive data. Option A is incorrect because data grouping metadata is not a feature in Salesforce. Option B is incorrect because field-level security is a feature that controls the visibility and editability of fields based on user profiles or permission sets, but it does not provide ways to filter and identify sensitive data. Option C is incorrect because creating a custom checkbox denoting sensitive data is not a scalable or reliable solution, as it requires manual maintenance and does not enforce any security measures.

Implementing a workflow rule that sends Opportunity data through Outbound Messaging is the optimal real-time approach that achieves the solution of copying Opportunity records to the ERP once they reach a Closed/Won Stage. A workflow rule can trigger an Outbound Message when an Opportunity record meets certain criteria, such as having a Closed/Won Stage. An Outbound Message can send data from Salesforce to an external system via SOAP API, without requiring any code. The external system can then process the data and create a read-only record in the ERP system. The other options are not optimal or real-time, as they would either require additional systems or tools, not provide real-time data synchronization, or not meet the frequency requirement

Setting up a Validation Rule to prevent blank values and marking the field required in setup at the field level are two approaches that the Architect should take to ensure that the field always contains data coming from the source system. A Validation Rule can display an error message when a record is created or edited with a blank value for the field, and prevent it from being saved. Marking the field required in setup at the field level can enforce the requirement for all records, regardless of the page layout or the source system. The other options are not effective or recommended for ensuring data quality, as they would either not prevent blank values, not apply to all records, or not address the root cause of the problem

Creating a trigger on Contact and adding an error to the record with a custom label allows the architect to use the translation workbench to translate the error message based on the user’s language. Creating a validation rule and translating the error message with translation workbench also achieves the same result. The other options would either not provide translation functionality or not display an error message