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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Architecture | 20% | - Governor limits and bulk processing - Architecture patterns and design principles - Archiving and data lifecycle management - Performance tuning and optimization |
| Topic 2: Master Data Management | 20% | - Data cleansing techniques - Data duplication and matching rules - Identity resolution |
| Topic 3: Data Integration | 15% | - Sharing rules impact on data integration - ETL tools and data migration strategies - API-based integration (REST, SOAP, Bulk API) - Handling locks and parallelism during migration - Real-time vs batch integration patterns |
| Topic 4: Data Modeling | 25% | - Normalization and denormalization strategies - Indexing strategies - Skinny tables for performance optimization - Big Objects and external objects - Large Data Volumes (LDV) design considerations |
| Topic 5: Data Governance | 20% | - Compliance and regulatory considerations - Data retention and archiving policies - Data quality standards and metrics - Data stewardship roles and responsibilities |
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NEW QUESTION # 249
UC has a classic encryption for Custom fields and is leveraging weekly data reports for data backups. During the data validation of exported data UC discovered that encrypted field values are still being exported as part of data exported. What should a data architect recommend to make sure decrypted values are exported during data export?
Answer: B
NEW QUESTION # 250
Ursa Major Solar has defined a new Data Quality Plan for their Salesforce data.
Which two approaches should an Architect recommend to enforce the plan throughout the organization?
(Choose two.)
Answer: A,B
Explanation:
Enforcing critical business processes by using Workflow, Validation Rules, and Apex code can help ensure data quality and consistency by applying rules and logic to the data entry and update3. Scheduling a weekly dashboard displaying records that are missing information to be sent to managers for review can help identify and fix data quality issues by providing visibility and accountability4.
NEW QUESTION # 251
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: B
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 # 252
Universal Containers (UC) has multi -level account hierarchies that represent departments within their major Accounts. Users are creating duplicate Contacts across multiple departments. UC wants to clean the data so as to have a single Contact across departments. What two solutions should UC implement to cleanse their data?
Choose 2 answers
Answer: A,C
Explanation:
Making use of a third-party tool to help merge duplicate Contacts across Accounts and using Data.com to standardize Contact address information to help identify duplicates are two solutions that UC should implement to cleanse their data and have a single Contact across departments. A third-party tool, such as an app from the AppExchange, can provide advanced features and capabilities for finding and merging duplicate Contacts across different Accounts, based on various criteria and rules. Data.com can provide address verification and standardization services that can enhance the quality and consistency of Contact address information and make it easier to identify duplicates. The other options are not feasible or effective for cleansing the data, as they would either not work across different Accounts, not address the root cause of duplication, or not provide sufficient functionality for merging duplicates
NEW QUESTION # 253
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?
Answer: C
NEW QUESTION # 254
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