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

According to the Salesforce documentation3, data quality is the measure of how well the data in Salesforce meets the expectations and requirements of the users and stakeholders. Data quality can be affected by various factors, such as data entry errors, data duplication, data inconsistency, data incompleteness, data timeliness, etc. To address the issues with data quality that affect the reliability of executive dashboards, a data architect should recommend:

Building a sales data warehouse with purpose-built data marts for dashboards and senior management reporting (option B). This means creating a separate database or system that stores and organizes sales data from Salesforce and other sources for analytical purposes. A data warehouse can provide a single source of truth for sales data and enable faster and more accurate reporting and analysis. A data mart is a subset of a data warehouse that focuses on a specific subject or business area, such as sales performance, customer segmentation, product profitability, etc. A data mart can provide tailored and relevant data for different users or groups based on their needs and interests.

Exploring third-party data providers to enrich and augment information entered in Salesforce (option C). This means using external services or tools that can validate, correct, update, and enhance the data that is entered or imported into Salesforce. This can help improve data quality and accuracy, and reduce data duplication and incompleteness.

Leveraging Salesforce features, such as validation rules, to avoid incomplete and incorrect records (option D). This means using features that allow defining rules and criteria to validate the data that is entered or updated by the users or integrations. This can help prevent invalid or incorrect data from being saved, and trigger actions or alerts to correct or improve the data.

Periodically exporting data to cleanse data and import them back into Salesforce for executive reports (option A) is not a good solution, as it can be time-consuming, error-prone, and inefficient. It may also cause data inconsistency and synchronization issues between Salesforce and other systems. Designing and implementing data-quality dashboard to monitor and act on records that are incomplete or incorrect (option E) is also not a good solution, as it can be complex, costly, and difficult to maintain. It may also not address the root causes of data quality issues or prevent them from occurring in the first place.

The recommended approach to re-run the test is to refresh the full copy sandbox and re-run the data migration test. A full copy sandbox is a replica of the production Org, including all data, metadata, and attachments. Refreshing a full copy sandbox means creating a new copy of the production Org and replacing the existing sandbox. This would ensure that the test is run on a clean and up-to-date environment, without any leftover data or configuration from previous tests. Truncating or hard deleting objects would not be sufficient, because they would not remove all the data or metadata from the sandbox, and they could also affect other dependent objects or processes.

 Clearing the role field in the generic user record and creating a role at the top of the role hierarchy and assigning it to the generic user are two measures that can help avoid performance issues while working with large number of order records. These measures can prevent data skew and lock contention that may occur when a single user owns or shares a large number of records

Leverage an External ID from source system orders to import related order lines. This is the recommended solution because it allows you to use the upsert operation to match records based on the External ID field, which is indexed and unique. This avoids the need to query the parent order IDs in Salesforce before importing the order line items, which can cause time-out errors or long delays1.

When denormalizing a data model into a single object with a picklist field, the data architect should consider the Bulk API limitations on picklist fields and the standard list view in-line editing. The Bulk API has a limit of 1,000 distinct picklist values per file1, which could be an issue if there are more than 1,000 venues in the future. The standard list view in-line editing allows users to edit multiple records at once, which could introduce data quality issues if the venue picklist is not validated or restricted2. The other options are not relevant to denormalizing a data model.

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 best solution to make sure decrypted values are exported during data export is to create another field to copy data from encrypted field and use this field in export. This is because classic encryption does not support exporting decrypted values of encrypted fields. The view encrypted data permission only allows users to view decrypted values in the user interface, but not in reports or data exports. Therefore, a workaround is to create a formula field or a workflow field update that copies the value of the encrypted field to another field, and use that field for data export. However, this solution has some drawbacks, such as exposing sensitive data in plain text and consuming extra storage space. A better solution would be to use Shield Platform Encryption, which supports exporting decrypted values of encrypted fields with the Export Encrypted Data permission

The correct answer is A. To provide additional guidance for users when they enter information in a standard field, a data architect should provide custom help text under field properties. This will display a help icon next to the field label that users can hover over to see the help text. Option B is incorrect because creating a custom page with help text for user guidance will require additional development effort and may not be easily accessible by users. Option C is incorrect because adding custom help text in default value for the field will overwrite the actual default value of the field and may confuse users. Option D is incorrect because adding a label field with help text adjacent to the custom field will clutter the page layout and may not be visible to users.

The recommended solution to address the requirements of Universal Containers (UC) is to roll-up discount request amount at the line-item-level and line-item-level summary discount at the opportunity level. This solution would allow UC to show all sums of discounts across line items at an opportunity level using standard roll-up summary fields. Since UC has a multi-level master-detail relationship for opportunities, a custom opportunity line item object, and a custom discount request object, they can use two levels of roll-up summary fields to aggregate the discount amounts from the child objects to the parent objects. For example, they can create a roll-up summary field on the custom line item object to sum up the discount request amounts from the custom discount request object. Then, they can create another roll-up summary field on the opportunity object to sum up the line-item-level summary discounts from the custom line item object.

The best solution to comply with the DR policy is to leverage a 3rd party tool that extract Salesforce data/metadata and stores the information in an external protected system. This solution can help create a backup of Salesforce data and metadata in case of a disaster or data loss event. It can also help restore data from the backup system to Salesforce if needed. There are various 3rd party tools available in the AppExchange or online that offer data backup and recovery services for Salesforce67. Leverage Salesforce weekly exports and store data in flat files on a protected system is not a good solution because it does not include metadata backup and it does not allow granular or automated data recovery. Utilize an ETL tool to migrate data to an on-premise archive solution is not a good solution because it does not include metadata backup and it may require complex data transformations and synchronizations. Write a custom batch job to extract data changes nightly and store in an external protected system is not a good solution because it does not include metadata backup and it may have performance or reliability issues.