Salesforce DATA-ARCHITECT Online Practice Questions and Exam Preparation

Salesforce DATA-ARCHITECT Online Questions & Answers

• Question 251:

  As part of a phased Salesforce rollout. there will be 3 deployments spread out over the year. The requirements have been carefully documented. Which two methods should an architect use to trace back configuration changes to the detailed requirements? Choose 2 answers

  A. Review the setup audit trail for configuration changes.
  B. Put the business purpose in the Description of each field.
  C. Maintain a data dictionary with the justification for each field.
  D. Use the Force.com IDE to save the metadata files in source control.
  B. Put the business purpose in the Description of each field.
  D. Use the Force.com IDE to save the metadata files in source control.

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  Explanation

  Option B is correct because putting the business purpose in the Description of each field is a method that an architect can use to trace back configuration changes to the detailed requirements1. The Description of each field provides a brief explanation of what the field is used for and why it is needed2. Option D is correct because using the Force.com IDE to save the metadata files in source control is another method that an architect can use to trace back configuration changes to the detailed requirements1. The Force.com IDE is an integrated development environment that allows developers to work with Salesforce metadata files and Apex code3. Source control is a system that tracks and manages changes to code and configuration files4. Option A is not correct because reviewing the setup audit trail for configuration changes is not a method to trace back configuration changes to the detailed requirements, but a way to monitor and audit the changes made in the setup area. Option C is not correct because maintaining a data dictionary with the justification for each field is not a method to trace back configuration changes to the detailed requirements, but a document that provides information about the data entities and attributes in a system.

• Question 252:

  Universal Containers is setting up an external Business Intelligence (BI) system and wants to extract 1,000,000 Contact records.

  What should be recommended to avoid timeouts during the export process?

  A. Use the SOAP API to export data.
  B. Utilize the Bulk API to export the data.
  C. Use GZIP compression to export the data.
  D. Schedule a Batch Apex job to export the data.
  C. Use GZIP compression to export the data.

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  Explanation

  According to the exam guide, one of the objectives is to "describe the use cases and considerations for using various tools and techniques for data migration (for example, Data Loader, Bulk API)"1. This implies that option B is the correct way to extract large volumes of data from Salesforce. The Bulk API is designed to handle large-scale data operations and avoid timeouts. Option A is not correct because the SOAP API is not optimized for large data sets and may encounter limits.

  Option C is not correct because GZIP compression does not prevent timeouts, but rather reduces the size of the data transferred. Option D is not correct because Batch Apex is used to process records asynchronously in Salesforce, not to export data to an external system.

• Question 253:

  Universal Containers (UC) needs to run monthly and yearly reports on opportunities and orders for sales reporting. There are 5 million opportunities and 10 million orders. Sales users are complaining that the report will regularly timeout.

  What is the fastest and most effective way for a data architect to solve the time-out issue?

  A. Create custom fields on opportunity, and copy data from order into those custom fields and run all reports on Opportunity object.
  B. Extract opportunity and order data from Salesforce, and use a third-party reporting tool to run reports outside of Salesforce.
  C. Create a skinny table in Salesforce, and copy order and opportunity fields into the skinny table and create the required reports on It.
  D. Create an aggregate custom object that summarizes the monthly and yearly values into the required format for the required reports.
  D. Create an aggregate custom object that summarizes the monthly and yearly values into the required format for the required reports.

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  Explanation

  Creating an aggregate custom object that summarizes the monthly and yearly values into the required format for the required reports (option D) is the fastest and most effective way for a data architect to solve the time-out issue, as it reduces the amount of data that needs to be queried and processed by the reports. Creating custom fields on opportunity and copying data from order into those custom fields (option A) is not a good solution, as it may create data redundancy and inconsistency, and it does not address the large volume of data. Extracting opportunity and order data from Salesforce and using a third-party reporting tool (option B) is also not a good solution, as it may introduce additional complexity and cost, and it does not leverage the native reporting features of Salesforce. Creating a skinny table in Salesforce and copying order and opportunity fields into it (option C) is also not a good solution, as it may not support all the reporting requirements, and it does not reduce the number of records.

• Question 254:

  UC is using SF CRM. UC sales managers are complaining about data quality and would like to monitor and measure data quality.

  Which 2 solutions should a data architect recommend to monitor and measure data quality?

  Choose 2 answers.

  A. Use custom objects and fields to identify issues.
  B. Review data quality reports and dashboards.
  C. Install and run data quality analysis dashboard app
  D. Export data and check for data completeness outside of Salesforce.
  B. Review data quality reports and dashboards.
  C. Install and run data quality analysis dashboard app

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  Explanation

  Reviewing data quality reports and dashboards and installing and running data quality analysis dashboard app are two solutions that can help monitor and measure data quality. Data quality reports and dashboards can provide insights into the completeness, accuracy, and consistency of the data. Data quality analysis dashboard app is a free app from AppExchange that can help analyze and improve data quality by identifying duplicate, incomplete, or inaccurate records.

• Question 255:

  UC recently migrated 1 Billion customer related records from a legacy data store to Heroku Postgres. A subset of the data need to be synchronized with salesforce so that service agents are able to support customers directly within the service console. The remaining non- synchronized set of data will need to be accessed by salesforce at any point in time, but UC management is concerned about storage limitations.

  What should a data architect recommend to meet these requirements with minimal effort?

  A. Virtualize the remaining set of data with salesforce connect and external objects.
  B. Use Heroku connect to bi-directional, sync all data between systems.
  C. As needed, make call outs into Heroku postgres and persist the data in salesforce.
  D. Migrate the data to big objects and leverage async SOQL with custom objects.
  A. Virtualize the remaining set of data with salesforce connect and external objects.

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  Explanation

  Virtualizing the remaining set of data with salesforce connect and external objects is the best way to meet the requirements with minimal effort, as it allows salesforce to access data stored in Heroku Postgres without storing it in salesforce.

  This reduces the storage limitations and avoids data duplication. Heroku connect can bi-directionally sync data between systems, but it requires more configuration and maintenance. Making callouts to Heroku Postgres and persisting the data in salesforce may not be feasible for 1 billion records. Migrating the data to big objects may incur additional costs and require custom code to use async SOQL

• Question 256:

  Which three characteristics of a skinny table help improve report and query performance?

  A. Skinny tables can contain frequently used fields and thereby help avoid joins.
  B. Skinny tables can be used to create custom indexes on multi-select picklist fields.
  C. Skinny tables provide a view across multiple objects for easy access to combined data.
  D. Skinny tables are kept in sync with changes to data in the source tables.
  E. Skinny tables do not include records that are available in the recycle bin.
  A. Skinny tables can contain frequently used fields and thereby help avoid joins.
  D. Skinny tables are kept in sync with changes to data in the source tables.
  E. Skinny tables do not include records that are available in the recycle bin.

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  Explanation

  The three characteristics of a Skinny table that help improve report and query performance are: Skinny tables can contain frequently used fields and thereby help avoid joins. Skinny tables are kept in sync with changes to data in the source tables. Skinny tables do not include records that are available in the recycle bin. These characteristics are beneficial because they reduce the query complexity and execution time, and improve the data accuracy and freshness. For example, skinny tables can contain frequently used fields from multiple objects, such as Account and Contact, and thereby help avoid joins that can slow down queries4. Skinny tables are updated automatically when the source tables are modified, so they always reflect the latest data5. Skinny tables do not include records that are available in the recycle bin, so they only contain active records that are relevant for reports and queries.

• Question 257:

  UC has a requirement to migrate 100 million order records from a legacy ERP application into the salesforce platform. UC does not have any requirements around reporting on the migrated data.

  What should a data architect recommend to reduce the performance degradation of the platform?

  A. Create a custom object to store the data.
  B. Use a standard big object defined by salesforce.
  C. Use the standard "Order" object to store the data.
  D. Implement a custom big object to store the data.
  D. Implement a custom big object to store the data.

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  Explanation

  Implementing a custom big object to store the data is the best recommendation to reduce the performance degradation of the platform, as it allows storing large volumes of data that do not need real-time access or reporting. Custom big objects can be defined using metadata API or developer console, and support up to 1 billion records per object. Creating a custom object or using the standard order object would consume a lot of storage space and impact the performance of queries and reports. Using a standard big object defined by salesforce would not be applicable for order records, as standard big objects are predefined for specific use cases such as audit trails or field history.