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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Modeling | 25% | - Indexing strategies - Skinny tables for performance optimization - Large Data Volumes (LDV) design considerations - Big Objects and external objects - Normalization and denormalization strategies |
| Topic 2: Data Governance | 20% | - Compliance and regulatory considerations - Data stewardship roles and responsibilities - Data quality standards and metrics - Data retention and archiving policies |
| Topic 3: Data Integration | 15% | - ETL tools and data migration strategies - Handling locks and parallelism during migration - API-based integration (REST, SOAP, Bulk API) - Real-time vs batch integration patterns - Sharing rules impact on data integration |
| Topic 4: Data Architecture | 20% | - Governor limits and bulk processing - Architecture patterns and design principles - Archiving and data lifecycle management - Performance tuning and optimization |
| Topic 5: Master Data Management | 20% | - Identity resolution - Data cleansing techniques - Data duplication and matching rules |
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NEW QUESTION # 229
Universal containers is implementing Salesforce lead management. UC Procure lead data from multiple sources and would like to make sure lead data as company profile and location information. Which solution should a data architect recommend to make sure lead data has both profile and location information? Option
Answer: D
Explanation:
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.
NEW QUESTION # 230
UC has millions of case records with case history and SLA dat
a. UC's compliance team would like historical cases to be accessible for 10 years for Audit purpose.
What solution should a data architect recommend?
Answer: B
Explanation:
The best solution to store historical cases for 10 years for audit purpose is to use a custom Big object to store archived case data. Big objects are a type of custom object that can store massive amounts of data on the Salesforce platform without affecting performance or storage limits. They also support point-and-click tools, triggers, and Apex code. Big objects can be used for archiving historical data that needs to be retained for compliance or analytics purposes3. Archiving case data using Salesforce Archiving process is not a good option because it only supports archiving cases that are closed for more than one year, and it does not allow customizing the archival criteria or accessing the archived data via Apex or APIs4. Purchasing more data storage to support case object is expensive and may impact performance. Using a custom object to store archived case data is not scalable and may consume a lot of storage space.
NEW QUESTION # 231
Universal Containers (CU) is in the process of implementing an enterprise data warehouse (EDW). UC needs to extract 100 million records from Salesforce for migration to the EDW.
What data extraction strategy should a data architect use for maximum performance?
Answer: C
Explanation:
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.
NEW QUESTION # 232
For a production cutover, a large number of Account records will be loaded into Salesforce from a legacy system. The legacy system does not have enough information to determine the Ownership for these Accounts upon initial load. Which two recommended options assign Account ownership to mitigate potential performance problems?
Answer: B,D
NEW QUESTION # 233
Universal Containers (UC) is migrating from a legacy system to Salesforce CRM, UC is concerned about the quality of data being entered by users and through external integrations.
Which two solution should a data architect recommend to mitigate data quality issues?
Answer: B,C
NEW QUESTION # 234
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