Pass the Salesforce Application Architect Data-Architect Questions and answers with ValidTests

The three questions that should be considered when designing an appropriate archiving strategy are: If reporting is necessary, can the information be aggregated into fewer, summary records? Will the data being archived need to be reported on or accessed in any way in the future? Are there any regulatory restrictions that will influence the archiving and purging plans? These questions are important because they help determine the scope, frequency, and method of archiving and purging data from Salesforce. For example, if reporting is necessary, then summary records or analytics snapshots can be used to store aggregated data and reduce the number of records that need to be archived1. If the data being archived needs to be accessed in the future, then offsite storage or external objects can be used to retain the data and make it available on demand2. If there are any regulatory restrictions, such as GDPR or HIPAA, then the archiving and purging plans need to comply with them and ensure data security and privacy

According to the Salesforce documentation, SOQL is a query language that allows querying data from Salesforce objects and fields. SOQL queries have various clauses and operators that can be used to filter and sort the results. However, some clauses and operators can affect the performance of SOQL queries by increasing the cost or complexity of executing them.

To make this query more performant, a data architect should change the where clause to filter by a deterministic defined value (option D). This means using a filter condition that specifies a concrete value or range of values for a field, such as WhatId = ‘001xx000003DGg3’ or WhatId IN (‘001xx000003DGg3’, ‘001xx000003DGg4’). This can improve the performance of the query by reducing the number of records that need to be scanned and returned. A deterministic defined value can also leverage an index on the field, which can speed up the query execution.

Removing description from the requested field set (option A) is not a good solution, as it can affect the functionality or usability of the query. The description field may contain important or relevant information that is needed for the process. Changing the query to SOSL (option B) is also not a good solution, as SOSL is a different query language that allows searching text fields across multiple objects. SOSL queries have different syntax and limitations than SOQL queries, and may not return the same results or performance. Adding limit 100 to the query (option C) is also not a good solution, as it can affect the completeness or accuracy of the query. The limit clause specifies the maximum number of records that can be returned by the query, which may not include all the records that match the filter condition.

A required Lookup relationship ensures that the Shipment record must have a value for each of the three parent records, and also prevents the deletion of those parent records if they are referenced bya Shipment record. A Master-Detail relationship would not allow separate sharing models for each custom object, and a VLOOKUP formula field would not enforce the relationship or prevent deletion

Enabling field history tracking on the Case object is the first step to implement a reporting solution to measure the support reps case closure rates. Field history tracking allows you to track changes to certain fields on the Case object, such as Status, Owner, or Priority. You can then create reports based on the field history data to analyze how long it took to close a case, how many times the case owner changed, or how many cases were escalated.

Data skew occurs when a large number of child records are associated with a single parent record, or when a single user owns a large number of records. This can cause performance issues and lock contention. To avoid data skew, the data architect should limit assigning one user 10,000 records ownership, limit associating 10,000 opportunities to one account, and limit associating 10,000 records looking up to the same record

The Sforce-Enable-PKChunking header enables you to extract large data sets from Salesforce by using a technique called primary key (PK) chunking. PK chunking splits your query into multiple queries based on the record IDs of the queried object. This reduces the query time and avoids the query timeout issues that can occur with large data volumes

The correct answer is C and D. To minimize performance and storage issues, a data architect should recommend leveraging on premise data archival and building integration to view archived data, and leveraging big object to archive case data and lightning components to show archived data. These solutions will allow some user groups to access historical cases for up to 7 years without consuming too much storage space or affecting the performance of queries and reports. Option A is incorrect because exporting data out of salesforce and storing it in flat files on external system will make it difficult to access and query the data. Option B is incorrect because creating a custom object to store case history and run reports on it will still consume a lot of storage space and impact the performance of queries and reports.

The setup audit trail tracks changes made in your org’s setup area for the past 180 days1. You can export the setup audit trail and find the fields in question by filtering by action type, user, or date2.

Leveraging duplicate rules in Salesforce to validate duplicates during the account creation process (option A) is the best option to implement to avoid duplicates, as it allows the sales associates to identify and merge duplicate accounts before they are saved. Developing an Apex class that searches for duplicates and removes them nightly (option B) is not a good option, as it may cause data loss or conflicts, and it does not prevent duplicates from being created in the first place. Implementing an MDM solution to validate the customer information before creating Salesforce (option C) is also not a good option, as it may introduce additional complexity and cost, and it does not address the issue of customers interacting with both the website and the store. Building a custom search functionality that allows sales associates to search for customer in real time upon visiting their retail stores (option D) is also not a good option, as it may not be reliable or user-friendly, and it does not leverage the existing Global Search feature.

Persistence is the data pattern that will allow a data architect to satisfy the requirements, while also keeping limits in mind. Persistence means storing data from external sources in Salesforce objects, either standard or custom. This allows you to access the data within Salesforce and use it for reporting, analytics, workflows, and other features. Persistence also helps you avoid hitting API limits or performance issues when accessing large volumes of data from external systems. You can use various tools such as Data Loader, Bulk API, or Platform Events to persist IoT data from connected devices to a cloud database in Salesforce.