Master the Salesforce Certified Data Architect (WI24) Data-Architect Exam with Confidence!

Determining if any integration points create records in Salesforce, running key reports to determine what fields should be required, and reviewing the sharing model to determine impact on duplicate records are three areas of Salesforce that the architect should review before proposing any design recommendation. These areas will help to identify the sources and quality of data, the business rules and validations for data entry, and the access and visibility of data across users and roles. Reviewing the metadata xml files for redundant fields to consolidate, and exporting the setup audit trail to review what fields are being used are not necessary steps for optimizing a data stewardship engagement, as they are more related to metadata management and audit tracking.

According to the Get Started with Big Objects unit on Trailhead, one of the use cases for custom big objects is to store and manage loyalty program data for customers. The unit states that “From loyalty programs to transactions, order, and billing information, use a custom big object to keep track of every detail.” Therefore, a custom big object can be used to capture the reward program data and display it on the contact record. Additionally, according to the Big Objects Implementation Guide, big objects can handle massive amounts of data (up to billions of records) and can be updated nightly from external systems using Bulk API or batch Apex. Therefore, a custom big object can meet the requirements of NTO’s loyalty program scenario.

Enabling “Set Audit Fields” allows the user loading the data to set the value of the standard CreatedDate field to match the original date from the legacy system. This is a one-time permission that can be revoked after the migration is completed. The other options would either not work or require additional customization

According to the Salesforce documentation1, the query plan tool is a tool that can be enabled and used in the developer console to analyze the performance of a SOQL query. The query plan tool shows the cost, cardinality, sObject type, and relative cost of each query plan that Salesforce considers for a query. The relative cost indicates how expensive a query plan is compared to the Force.com query optimizer threshold. A query plan with a relative cost above 1.0 is likely to cause a time-out error.

To identify why the query is taking too long, a data architect should use the query plan tool in the developer console (option B). This way, the data architect can see which query plan is chosen by Salesforce and how it affects the performance of the query. The data architect can also use the query plan tool to optimize the query by adding indexes, filters, or limits to reduce the cost and improve the efficiency of the query.

Using Splunk to query the system logs looking for transaction time and CPU usage (option A) is not a good solution, as it can be complex, costly, and difficult to integrate with Salesforce. It may also not provide enough information or insights to identify and optimize the query performance. Using Salesforce’s query optimizer to analyze the query in the developer console (option C) is also not a good solution, as it is not a separate tool that can be used in the developer console. The query optimizer is a feature that runs automatically when a SOQL query is executed and chooses the best query plan based on various factors2. Opening a ticket with Salesforce support to retrieve transaction logs to be analyzed for processing time (option D) is also not a good solution, as it can be time-consuming, dependent, and inefficient. It may also not provide enough information or insights to identify and optimize the query performance.

The data architect should recommend building a batch job to move two-year-old records off platform, and delete records from Salesforce as an archiving solution. A batch job is a process that runs in the background and performs operations on large volumes of data in Salesforce. By building a batch job that moves two-year-old records off platform to an external storage system, such as Amazon S3 or Google Cloud Storage, and deletes them from Salesforce, the data architect can reduce the storage consumption and improve the performance of Salesforce org. Option A is incorrect because using a third-party backup solution to backup all data off platform will not free up any storage space in Salesforce, unless the data is also deleted from Salesforce after backup. Option B is incorrect because building a batch job to move all records off platform, and delete all records from Salesforce will result in losing all the current data in Salesforce, which may not be desirable or feasible. Option D is incorrect because building a batch job to move all restore off platform, and delete old records from Salesforce does not make sense, as restore implies restoring data back to Salesforce, not moving it off platform.

Grouping batches by the AccountId field can improve the performance and avoid locking issues when loading Opportunity records using the Bulk API in parallel mode1. This is because the AccountId field is indexed and can be used to distribute the records evenly across batches

The best solution to assign opportunities based on zip code and sales area is to configure territory management feature to support opportunity assignment. Territory management is a feature that allows you to organize your sales team into territories based on criteria such as geography, industry, product line, or customer segment. You can assign accounts and opportunities to territories using assignment rules or manual sharing. You can also define forecast managers and roll up forecasts by territory45. Assign opportunities using list views using zip code is not a good solution because it is inefficient and does not support reporting by sales area. Add custom fields in opportunities for zip code and use assignment rules is not a good solution because it requires creating additional fields and does not support reporting by sales area. Allow sales users to manually assign opportunity ownership based on zip code is not a good solution because it is prone to errors and does not support reporting by sales area.

The correct answer is B, use data classification metadata fields available in field definition. Data classification metadata fields are standard fields that allow you to classify the sensitivity level of your data based on your organization’s policies. You can use these fields to indicate whether a field contains confidential, restricted, or general data. These fields are available for both standard and custom objects in Salesforce. Using app exchange products, creating a custom picklist field, or building reports would not be as effective or consistent as using data classification metadata fields.

 A sound data archiving strategy is essential for ensuring scalability and performance of any application that deals with large volumes of data. You need to consider factors like data retention policies, storage limits, backup and recovery options, and data access requirements when designing a data archiving solution2. You can use tools like OwnBackup, Odaseva, or ArchiveIt to archive your data from Salesforce to external systems

Implementing a Contact Conference junction object with master detail relationship to both contact and conference_c is the correct way to model the relationship between the contact and conference objects, as it allows a many-to-many relationship between them. This means that each contact can attend multiple conferences, and each conference can be related to multiple contacts. Creating a master detail relationship field on either the contact or the conference object would create a one-to-many relationship, which does not meet the requirements. Creating a lookup relationship field on contact object would also create a one-to-many relationship, and would not enforce referential integrity.