Data-Architect Exam Dumps - Salesforce Data-Architect Questions and Answers

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.

Custom metadata records are deployable using packages, which makes them easier to migrate from one environment to another. Custom settings records are not deployable using packages, and they are copied from production to sandbox. Custom metadata types are not available for reporting, and custom metadata records are not editable in Apex.

According to the Salesforce documentation2, extracting large amounts of data from Salesforce can be challenging and time-consuming, as it can encounter performance issues, API limits, timeouts, etc. To extract 100 million records from Salesforce for migration to an enterprise data warehouse (EDW), a data extraction strategy that can provide maximum performance is:

Utilize PK Chunking with the Bulk API (option C). This means using a feature that allows splitting a large query into smaller batches based on the record IDs (primary keys) of the queried object. This can improve performance and avoid timeouts by processing each batch asynchronously and in parallel using the Bulk API3.

Installing a third-party AppExchange tool (option A) is not a good solution, as it can incur additional costs and dependencies. It may also not be able to handle such a large volume of data efficiently. Calling the REST API in successive queries (option B) is also not a good solution, as it can encounter API limits and performance issues when querying such a large volume of data. Using the Bulk API in parallel mode (option D) is also not a good solution, as it can still cause timeouts and errors when querying such a large volume of data without chunking.

 According to the exam guide, one of the objectives is to "describe how to use queryAll() or ALL ROWS keywords to access deleted records in Apex and SOQL"1. This implies that option C is the correct way to access deleted records in Salesforce. Option D is not correct because sending data to a data warehouse does not maintain the data in Salesforce. Options A and B are not correct because they do not apply to deleted records2.

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 documentation1, ownership skew occurs when a large number of records (more than 10,000) are owned by a single user or queue. This can cause performance issues and lock contention when multiple users try to access or update those records. To prevent ownership skew, some of the recommended practices are:

Assign a default user as the owner of the records and do not assign any role to the default user (option C). This way, the records will not be visible to other users in the role hierarchy and will not cause sharing recalculations.

Keep users out of public groups that can be used as the source for sharing rules (option B). Sharing rules based on public groups can cause excessive sharing calculations and lock contention when many records are owned by a single user or queue.

Assign a default user as the owner of the records and assign the top most role in the hierarchy to the default user (option E). This way, the records will be visible to all users in the role hierarchy, but will not cause sharing recalculations or lock contention.

Assigning a default user as the owner of the records and assigning a role in the hierarchy (option A) is not a good practice, as it can cause sharing recalculations and lock contention when the role is updated or moved. Assigning “view all” permission on profile to give access to the records (option D) is also not a good practice, as it can bypass the security and sharing model and expose sensitive data to unauthorized users.

 Patients as Person Accounts, Payment providers as Accounts, & Doctors as Person Accounts is the optimal selection by the data architect to map the actor to Salesforce objects. This is because Person Accounts are a special type of accounts that can store both business and personal information for individual customers. Payment providers are organizations that pay for the care of patients, so they can be modeled as Accounts. Doctors are also individuals who provide care plans for patients and need access to patient information, so they can also be modeled as Person Accounts.

 The best solution to make sure lead data has both profile and location information is to leverage external data providers to populate company profile and location data. This is because external data providers can enrich lead data with additional information from third-party sources, such as Dun & Bradstreet, ZoomInfo, or Clearbit. This can help improve lead quality, segmentation, and conversion. Salesforce supports integrating with external data providers using Data.com Clean or other AppExchange solutions2. Asking sales people to search for populating company profile and location data is inefficient and prone to errors. Running reports to identify records which do not have company profile and location data is useful, but does not solve the problem of how to populate the missing data. Exporting data out of Salesforce and sending to another team to populate company profile and location data is cumbersome and time-consuming.

The number of joins used in report query and the number of records returned by report query are two key considerations to make sure the performance of the report is not degraded due to large data volume. The number of joins used in report query affects the complexity and execution time of the query, especially when joining multiple large objects4. The number of records returned by report query affects the amount of data that needs to be processed and displayed by the report engine.

According to the Salesforce documentation1, 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 mitigate data quality issues, some of the recommended solutions are:

Leverage picklist and lookup fields where possible (option A). This means using fields that restrict the values or references that can be entered by the users or integrations. This can help reduce data entry errors, enforce data consistency, and improve data accuracy.

Leverage validation rules and workflows (option C). 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.

Leveraging Apex to validate the format of data being entered via a mobile device (option B) is not a good solution, as it can be complex, costly, and difficult to maintain. It is better to use standard features or declarative tools that can handle data validation more effectively. Leveraging third-party AppExchange tools (option D) is also not a good solution, as it can incur additional costs and dependencies. It is better to use native Salesforce features or custom solutions that can handle data quality more efficiently.